# Securing Autonomous Air Traffic Management: Blockchain Networks Driven by
Explainable AI ††thanks: 1University of Oxford, UK; 2Cranfield University, UK;
3Alan Turing Institute, UK; 4University of Glasgow, UK; 5TEKTowr, UK;
∗Corresponding Author<EMAIL_ADDRESS>The authors wish to
acknowledge the UKRI Future Flight Phase 2 Grant: Fly2Plan – Enabling a new
model aviation data system-of-systems (70883)

Louise Axon1, Dimitrios Panagiotakopoulos2, Samuel Ayo2, Carolina Sanchez-
Hernandez2, Yan Zong2,
Simon Brown5, Lei Zhang4, Michael Goldsmith1, Sadie Creese1, Weisi Guo2,3∗

###### Abstract

Air Traffic Management data systems today are inefficient and not scalable to
enable future unmanned systems. Current data is fragmented, siloed, and not
easily accessible. There is data conflict, misuse, and eroding levels of trust
in provenance and accuracy. With increased autonomy in aviation, Artificially
Intelligent (AI) enabled unmanned traffic management (UTM) will be more
reliant on secure data from diverse stakeholders. There is an urgent need to
develop a secure network that has trustworthy data chains and works with the
requirements generated by UTM. Here, we review existing research in 3 key
interconnected areas: (1) blockchain development for secure data transfer
between competing aviation stakeholders, (2) self-learning networking
architectures that distribute consensus to achieve secure air traffic control,
(3) explainable AI to build trust with human stakeholders and backpropagate
requirements for blockchain and network optimisation. When connected together,
this new digital ecosystem blueprint is tailored for safety critical UTM
sectors. We motivate the readers with a case study, where a federated learning
UTM uses real air traffic and weather data is secured and explained to human
operators. This emerging area still requires significant research and
development by the community to ensure it can enable future autonomous air
mobility.

###### Index Terms:

aeronautical telecommunications; blockchain; explainable machine learning; air
traffic management; unmanned traffic management;

##  I Introduction

The aviation industry shares flight data through internationally-defined
standard format messages - see International Civil Aviation Organisation
(ICAO). As shown in Fig.1, Air Navigation Service Providers work with airlines
and airports to deliver air traffic flow and capacity management. It relies on
synchronized and secure Positioning, Navigation, and Timing (PNT) data from
Global Navigation Satellite System (GNSS), radar track data, and flight voice
data. The current ecosystem is designed and operated based upon the assumption
that aircraft would largely fly airport-to-airport along predetermined routes.
This process is no longer fit for purpose, particularly as we enable UTM
traffic and transition towards On-Demand Air Mobility.

Figure 1: Data planes in air traffic management (ATM): Air Navigation Service
Providers (ANSPs) work with airlines and airports to deliver air traffic flow
and capacity management. It relies on synchronized and secure PNT data from
GNSS, radar, and flight. Large volumes of autonomous drones threaten to
disrupt this ecosystem, requiring data sharing across new commercial and
private entities and overwhelming traditional traffic flow management
operators.

###  I-A Challenges in Air Traffic Management Networks

Aviation data is constantly at risk of being eroded and misused, and large
volumes of autonomous and spontaneous drone flights threaten to disrupt this
data ecosystem:

  * •

Data misuse: airlines strip out commercially sensitive fuel consumption
detail, resulting in air navigation and airport service providers using low-
quality information, frequently causing uncertainty at landing runways. Recent
studies indicate that as much as 60% of flight delays are due to data errors
or network in-availability. EASA and Eurocontrol estimates that 1 hour of
delay to 1 flight costs $8-18k. Whilst the bulk of these often happen in
bursts (e.g., Alaska ATM virus, British Airways personal data breach, Russian
air space satellite data manipulation). Furthermore drone data is often at
risk of being misused in a wide range of adversarial and criminal ways,
putting aviation safety at risk.

  * •

Emerging autonomy: Unmanned aerial systems (UAS) dramatically increase the
need for unplanned traffic management at a scale human air traffic controllers
cannot cope with. Unmanned aviation is set to unlock £45bn of benefits to the
UK economy by 2030, with over €10-15bn of benefits per year estimated for the
EU. UAS or drones fly often lower than conventional aircraft, increasing their
risk. Without a pilot, they are heavily dependent on reliable and timely data
acquisition and sharing to ensure their flight is conducted safely, often
using new data sources not provided by the legacy air traffic network.

Cybersecurity Challenge Example: Currently, airlines strip out commercially
sensitive fuel consumption details, resulting in air traffic management using
low-quality information and causing uncertainty at landing queue management.
This has a significant safety impact and a lasting economic impact at the
airport (60% of delays are due to data errors or in-availability). EASA and
Eurocontrol estimates that 1 hour of delay per flight costs $8-18k. Whilst the
bulk of these cybersecurity issues occur in bursts (e.g., Alaska ATM virus, BA
data breach), UTM and UASs’ high dependency on data-driven autonomy puts us at
a level of public safety risk unseen before. These challenges highlight the
impeding data crisis as we move towards a more autonomous aviation.

###  I-B Current Research Overview & Gaps

Whilst the Single European Sky ATM Research (SESAR) programme and American
NextGen encourage greater data sharing through System-Wide Information
Management (SWIM) [1], this still requires organisations to individually
combine and rationalise shared data. Not only is this cumbersome, current
research initiatives also fail to accommodate new airspace users operating
from non-airports and flying bespoke routes. Emerging unmanned aerial systems
(UAS) often use non-aviation positioning or communication channels such as 5G
and terrestrial timing distribution services. All of these new and
decentralised capabilities must be accommodated in a cyber-secure way,
providing high levels of trust and low latency accuracy.

Modern Distributed-Ledger Techniques (DLT) are distributed over wireless
networks offer a potential solution to accommodate the sheer variety of data
and participating systems in a coherent trust-ensuring manner [2, 3]. The key
systems include the need for explainable AI to enable unmanned traffic
management (UTM) [4, 5] to manage increased data complexity and flow
scheduling load. This is being actively addressed by emerging EASA frameworks
and Eurocontrol standards [4] and international R&D programs (NASA AOSP, FAA
NextGen, EU SESAR, Eurocontrol ECHO, UKRI Future Flight).

Existing review and survey papers have largely focused on either end-user
applications of DLT-enabled UASs [6] or integration with space and terrestrial
5/6G ecosystems [7, 8]. What has not been examined is the enabling of the ATM
and air traffic control systems which is much more time-sensitive and safety
critical. The sheer volume of data between many stakeholders (airlines,
airports, ATM, radar,…etc.) makes this challenging. This review paper comes at
the back of a major UK Future Flight project (Fly2Plan) and will review the
state-of-the-art research in emerging AI Integrated DLT technology to support
all airspace management.

###  I-C Novelty & Contribution: Explainable UTM with Secure Blockchain Data
Streams

UTM will adopt AI algorithms to relief the cognitive burden of human operators
at the different entities in Fig.1. As AI is sensitive to data inputs in
different ways that humans are, this places even greater emphasis on dynamic
levels of trust and security on the data that informs AI decisions. This can
only be achieved if the AI algorithms backpropagate security requirements back
to the networked blockchain modules that distribute consensus across the
communication network [9]. The review sets out recent advances which foster
the development of wireless network architectures that enable the novel
collaboration between mission-critical explainable AI and blockchain secured
data streams. The review paper is organised as follows (see Fig.2) with novel
contributions in reviewing state-of-the-art research in the following areas:

  1. 1.

Section II: A review of current wireless network architectures used to
optimise and disseminate consensus whilst being ATM/UTM context aware.

  2. 2.

Section III: A review of blockchain advances for UTM, especially in the
context of adaptively setting the aviation security requirements and working
between competing commercial stakeholders.

  3. 3.

Section IV: A review of explainable AI advances for automated air traffic
control, and the backpropagation of trust and security requirements to
blockchain and consensus distribution. A federated learning case study with
real air traffic and weather data is used to demonstrate securing and
explaining UTM.

  4. 4.

Section V: Summary of the overall vision of future automated ATM data
networks, as well as open challenges.

On top of the review, we also include our own demonstration case studies using
real aviation data. We hope this review enables readers to think about the
joint challenges between secure networked AI for mass air autonomy and
wireless network design.

Figure 2: Future air traffic management: AI-based air traffic management
driven by secure data chains. Deep learning algorithms use explainable AI
(XAI) interfaces to discover the data security requirements. These are
backpropagated to the blockchain controllers to distribute consensus via
wireless network.

##  II Wireless Consensus Distribution

We aim to improve the security and data-driven AI trust in future ATM/UTM
communications to address increased unmanned drone usage and alleviate human
traffic controller’s cognitive burden. This can be achieved between multiple
stakeholders (see Fig.1) using recent advances in distributing consensus in a
blockchain (see Fig.2) [9]. The consensus between all nodes is a critical
component of a modern blockchain (detailed in Section III), while the
underlying communication network that makes quorums possible is essential. In
the case of ATM/UTM, the traffic management sensitive data/decisions (see
Section IV) need to be agreed by all traffic participants and regulators in
the area through achieving a consensus. Sequentially, all nodes can be updated
with latest logs of decisions with non-repudiable records in the blockchain.

Unlike wired systems, wireless systems bring extra channel uncertainty and
scarcity of spectrum, thus entailing different distributed consensus protocols
and performance. A complete protocol with privacy preservation that is
suitable to UTM needs to be established. Information-theoretic models are
needed to bridge the achievable bounds and real performance between wireless
channels and DLT. Operationally, malicious attacks through open wireless
environment may compromise voting-based consensus, raising the need to
consider new threat models to DLT and to develop novel spectrum optimisation
for adaptive secure networking. As shown in Fig.1 ATM/UTM scenarios, most
traffic participants rely on wireless communications links, e.g., VHF (118-137
MHz), UHF (225-400 MHz), ADS-B (978/1090 MHz), and satellite communications
(27/35 GHz), to build up situational awareness and make decisions with
additional feedback from primary, secondary radar systems, and other GNSS
capabilities. Therefore, the blockchain network, upon the consensus group
between all manned/unmanned aircraft, ground control centers and regulators,
needs to have the quorum over wireless network.

TABLE I: ATM/UTM Areas of Operations: Network, blockchain, and (X)AI Requirements. ATM/UTM Area |  Criticality and (Latency Requirement) |  Wireless Channel (Typical Range) |  Consensus Type [9] (Section II-III) |  UTM AI Algorithms [4] (Section IV) |  Suitable XAI Methods [10] (Section IV)
---|---|---|---|---|---
Collision Avoidance [5] |  Safety Critical (100ms) |  LoS/NLoS ($>$5km) |  Byzantine Fault Tolerance (BFT) |  Kalman filter, RNN |  Feature: SHAP
Remote Piloting |  Safety Critical ($<$1ms) |  NLoS ($<$25km) |  Byzantine Fault Tolerance (BFT) |  Kalman filter, RNN |  Feature: SHAP
Aircraft Detection [4] |  Safety Critical ($<$2s) |  LoS ($<$200km) |  Proof-based consensus (PoX) |  Filter banks, CNN & RNN |  Feature: SHAP, DeepLIFT
Delay Prediction [11] |  Mission Critical ($<$1min) |  LoS ($<$5km) |  Proof-based consensus (PoX) |  Expert Models, ARMA, RF, GP, RNN |  Feature: SHAP
Navigation Planning |  Mission Critical (s) |  LoS ($<$350km) |  Crash Fault Tolerance (CFT) |  Q-Learning, DRL |  Feature: DeepLIFT
Autonomous Landing [4] |  Safety Critical ($\approx$10ms) |  LoS ($<$15km) |  Byzantine Fault Tolerance (BFT) |  CNN & Q-Learning / DRL |  Feature: DeepLIFT; Summative: TDA
Air Space Congestion Management [3] |  Mission Critical ($\approx$100ms) |  LoS ($<$50km) |  Voting: Raft or PBFT |  Clustering & Convex Opt. / Feed-forward NN / Bayesian Tree |  Summative: LIME, Meijer-G, TDA

###  II-A ATM Driven Consensus Type Selection

The consensus type of ATM blockchain can be decided based on the ATM tasks
(full summary in Table I):

  * •

Airspace Co-Existence: shared airspace availability information can be updated
into the network using Proof-based consensus (PoX), e.g., Proof-of-Stakes
(PoS), or Proof-of-Authorities (PoA), as the local aviation information may
not have impacts with other regional air traffic participants.

  * •

Congestion Management: a multi-drone congestion advisory message (e.g., near
logistics pick up centre) is better handled by the voting-based consensus,
e.g., Raft or Practical Byzantine Fault Tolerance (PBFT), which other
participants may have their input on whether agreeing the message during the
consensus. PBFT is particularly suitable due to robustness against sensing
errors in air space usage and aircraft positioning, as well as when under
malicious data injection.

  * •

Collision Avoidance: in latency sensitive tasks [5], voting-based consensus is
preferred, as it can greatly increase the reliability and availability of the
ATM. There are two major groups of voting-based consensuses. (1) the Crash
Fault Tolerance (CFT) consensus and (2) Byzantine Fault Tolerance (BFT). The
CFT consensus assumes all nodes are benign in the network. However, they may
crash or be out of service due to the limited reliability and availability of
the node. In contrast, BFT handles the network with malicious nodes, producing
false or inaccurate network feedback. The CFT can help boost the reliability
of ATM decision-making, and BFT can help the ATM have better tolerance to
sensor errors and perform error corrections to traffic participants.

###  II-B Safety Critical Consensus Types and Decentralised Optimisation

Upon the deployment of the consensus, wireless communications is important for
the consensus network, as both CFT and BFT require multi-phase messaging to
other nodes in the network:

  1. 1.

Crash Fault Tolerance (CFT): the wireless network can be simplified as a MAC
layer network, where each node only broadcasts and listens to the open space
without a central network controller. Each node may broadcast its message
based on its status within the local consensus group.

  2. 2.

Byzantine Fault Tolerance (BFT): it requires a significant number of
communications between all nodes when the network grows, which is a challenge
for the wireless channel capacity whilst maintaining a minimum latency
standard. Hence the MAC layer-only network might be a bottleneck for the
consensus performance in large drone swarm situations, as the transmission
options are limited to the MAC layer with limited flexibility of broadcast and
uni-cast.

To optimise consensus parameters on a distributed network, where each aviation
platform may not have full knowledge of the consensus distribution network,
one can use machine learning [9]. The research in [2] reviewed novel air-
ground-space blockchain-assisted virtulalised network architectures. Within
that framework, credit-based consensus algorithm in a lightweight vehicular
blockchain can be used to securely and immutably trace malicious data access
and record data transactions for aviation systems with improved efficiency and
security in reaching consensus. The main benefit is that given local aerial
platforms have little explicit knowledge of the whole network, reinforcement
learning (Q-learning on the voting-based consensus outcomes) is used at each
node to optimally schedule the pricing and quality of data sharing strategies
for both data contributor and data consumer via trial and error. In contrast,
the research in [12] present a centralised neural network optimised
blockchain-based mobile drone edge computing (MEC), designed to ensure ultra-
reliability and provide a flat architecture. The master blockchain is created
such that only the pre-existing block information from the respective
blockchain of an individual is operated over the neural model to generate the
optimized values.

###  II-C Open Research Areas

The current active research areas in this space are:

  * •

Secure information-theoretic bounds: Establish secure link budget models by
investigating how DLT system metrics (e.g., transaction delay, throughput,
security threshold) and wireless communication (e.g., delay, throughput, and
reliability) can be integrated. Research in the UTM environment can focus on
voting based consensuses and cascade compromise scenarios. Recognising that
the consensuses’ procedures can be significantly different and communication
relevant, the model for each consensus should be built respectively according
to current and future aviation networks, e.g., crash based consensus (RAFT)
may be used for latency-sensitive tasks; while Byzantine fault tolerant
protocols can be used for some high secure cases without stringent latency
requirements.

  * •

Optimal secure communication resource allocation: The available communication
resource in terms of power and spectrum for the consensus air peer-to-peer
network can significantly affect consensus thus DLT performance. For instance,
increasing communication transmission power can benefit DLT security through a
broader coverage of the broadcasting range since it means faster/safer
procedure for consensus. Thus, there is a lack of research on how to minimise
the required communication resources subject to required DLT performance.

  * •

Zero-knowledge proof (ZKP) based DLT: The highlight of ZKP is to realise the
proof without revealing anything other than the statement is true (i.e., no
leakage of the statement-related information). As such, the coupling of ZKP
and DLT could be a promising technical route to bridge the gap of sharing
sensitive information between airline companies, ATCT, aviation
administrations and other stakeholders. An open area of research is to use ZKP
to protect the privacy of airline companies based on DLT, which is served as a
platform to underpin ZKP protocols using smart contracts to share sensitive
information (e.g., remaining fuel for landing optimisation) and ensure the
shared information is validated and synchronised among stakeholders.

##  III Adversarial Attacks in Blockchain Enabled UTM

Blockchain-based systems have the potential to enable data exchange between
ATM and competing commercial stakeholders (through consensus) - creating a
reliable data ledger (a shared source of “truth”). They also have the
potential to meet important requirements on the security of this data. If
designed securely, blockchain-based systems can produce data ledgers with high
levels of integrity and availability through their core mechanisms. These
properties are vital for safety and mission-critical UTM contexts; in
particular when there is a risk of lack of data integrity.

###  III-A Tamper Resistance

Firstly, a high level of tamper-resistance can be achieved. It is well-
understood that the storage of hash-chained data along with resource-limited
block proposal makes manipulating stored data extremely difficult. This can
help ensure data maintains integrity. For example, a BFT Blockchain can also
withstand some malicious or faulty nodes, such that the system can continue to
operate correctly, maintaining availability of the data exchange under such
circumstances. Furthermore, the availability of data does not rely on the
availability of a single or small set of storage machines since the ledger is
distributed across multiple nodes who store a copy. However, as is the case
with all systems, the security of blockchain-based systems depends on their
design and the operational security practices that surround them. The
integrity, availability and confidentiality of data in these data chains could
be put at risk by various attacks and faults. Detailing all possible attacks
is outside the scope of this paper, but some illustrative examples follow.

###  III-B Adversarial Attack Examples

####  III-B1 Resource Control

Attacks against consensus algorithms are a risk if an adversary gains control
of a sufficiently high level of the network’s resources, e.g., mining power in
a system that uses Proof-of-Work (PoW) as the block-proposal mechanism. This
may enable an adversary to manipulate data (by reverting to a new main chain)
or manipulate finalisation (i.e., decide which blocks are finalised into the
chain). Gaining this resource may be achieved through a range of approaches,
e.g., an infrastructure-level attack (e.g., stealing credentials) to gain
control of an endpoint, Sybil attack, Eclipse attack, collusion between nodes,
or exploiting a vulnerability in the resource-limitation algorithm (e.g., a
hashing vulnerability in the PoW algorithm). The level of resource required is
dependent on the block-finalisation algorithm used. One example is achieving
the 51% attack against the probabilistic longest-chain algorithm [13].

####  III-B2 Coding Vulnerabilities

Another example is the exploitation of coding vulnerabilities in smart
contracts, which could potentially enable adversaries to disable or lock the
smart contract, delete or manipulate data, gain unauthorised access to data,
post data without authorisation, or restrict legitimate control. Such
vulnerabilities could occur accidentally or be inserted deliberately during
initial development or updates to smart contracts. Attacks on access-control
and identity-management mechanisms in permissioned Blockchain systems, which
could enable participation by unintended parties, also need to be considered.
Addressing these risks to security requires secure design and coding
practices, security testing of systems and effective operational security
practice to identify and mitigate the impact of malign behaviour by insiders
and outsiders to the network.

###  III-C Improving Security & Privacy for UTM

There has been research into how to integrate additional security and privacy
techniques with Blockchain systems in order to achieve necessary security
properties. This includes, for example, the use of Trusted Execution
Environments (TEEs) for executing smart contracts securely. In the ATM
context, while data integrity and availability are clearly critical for safety
and efficiency of operations, the confidentiality of certain data or
participants is also important in certain cases – for example while this data
is sensitive for commercial or security reasons. Alongside restricting access
to a set of authorised parties through private, permissioned Blockchains,
privacy-preserving techniques can be used to enable necessary operations:
Zero-Knowledge proofs (e.g., ZK-SNARK), ring signatures (e.g., Monero), and
secure multi-party computation have been proposed and implemented [14].

The prioritisation of security requirements may vary across contexts and types
of information. For example, time-critical safety contexts will prioritise
having the information availability quickly enough for the UTM AI algorithms
to make safe decisions, whereas in other cases competing stakeholders may
prioritise the confidentiality of their commercially sensitive information.
Therefore, a Blockchain-based system used in this context may need to be
adaptable, not only in light of practical factors such as potential temporary
wireless networking constraints (see Section II), but also based on security-
requirement prioritisations, which may need to be back-propagated from the
decision-making algorithms (this is reviewed next in more detail in Section
IV).

Figure 3: Case Study on Flight Trajectory and Arrival Time: arranged in format
of Fig.2. (top left) Diverse data pipelines from different stakeholders
(airline, trajectory, meteorological) is used to predict aircraft position
using a federated CNN & RNN architecture. blockchain secures the data
distribution with different consensus types based on the wireless channel.
(bottom) Explainable AI (XAI) insight using various feature activation map and
SHAP values can highlight the importance of certain data streams [11]. This
informs new requirements for the blockchain to maintain or change the
consensus distribution.

##  IV XAI for Air Traffic Management with Backpropagation of Security
Requirements

A wide range of tasks are performed by ATM to ensure the safe shared usage of
air space. With increased autonomous drone traffic, often in non-segregated
air space, there is active AI research in the following areas to enable UTM.
There is already active AI research in these areas, some of which are safety
critical, whilst others are also mission critical (e.g., vital for efficient
and profitable aviation). A review of the UTM methods along with their
corresponding blockchain consensus types (see Section II) and security
requirements can be found in Table I. The gaps in existing literature is that
most current usage of deep learning based air traffic management is not
explainable [4, 15]. As such, we do not know which data inputs need greater
scrutiny, security, or trust - vital for mission and safety critical mass
autonomy [10]. By advancing in explainable AI (XAI), we can enable
backpropagation of security and trust requirements to improve the accuracy and
robustness of AI outputs for safety and critical air traffic management.

###  IV-A Towards Explainable & Trustworthy AI Metrics

Globally implementation of AI in a variety of industries has raised the
attention of legal issues regarding both its reliability (e.g., variability
and robustness of performance to diverse circumstances) and the provenance of
reasoning (e.g., which data features caused which decisions) [4]. Machine
learning techniques range from statistical classifiers to deep models
(Recurrent Neural Network (RNN), and Convolutional Neural Network (CNN)).
Indeed, these algorithms can be federated to local airspace or air platform
operations, further highlighting the importance of distributed consensus
adaptation. According to emerging national and international legal
requirements (e.g., EU regulatory framework on artificial intelligence), there
is an urgent requirement for developing methods and KPIs for both AI
explainability and trust [10]. Explainability in AI (XAI) is extracted from
different characteristics of machine learning models with multi-level
expression of reasoning in statistics, semantics, symbolic expression, and
logical reasoning.

####  IV-A1 Data Feature Based XAI:

Data is the most direct, meaningful, and detailed explanation in ML models,
the raw explanation highlights the features with high contribution to the
decision-making, which contains rich unbiased and unmodified raw information,
but lacks expressions in logic (why and how extracted features cooperated). At
the perhaps most intuitive level of explainability, one can post-hoc visualise
the features that are important based on their weights or gradients of local
nodes in the NN after training. In a gradient-based approach, we calculate the
gradient of each input feature with respect to an output, where a small change
in the input data feature leads to the level of outcome change can be
visualised (e.g., DeepLIFT). Local features in hidden layers are non-linear
and therefore the interpretation maybe not trivial. Here, approaches such as
Shapley Additive Explanations (SHAP) can play a role to identify the average
marginal contribution of a data features across all possible coalitions.

####  IV-A2 Summative XAI Models:

Summative explanation using design explainable models and surrogate models
(high transparency ‘white box’) mentioned above, generates smoothing and
fitted explanations based on the processing of statistics from low
transparency black box. For example, the decision flow of on-board autonomous
model is not visible from Raw Explanation explanations, especially for CNN
encoded high dimensional features, while summative explanation could generate
a fitted symbolic expression of the DL model to clarify the relationships
among inputs in formula expressions [15]:

  * •

Global model level: (1) the Meijer G-function is a general function intended
to include most known special functions and classes. As such, the Meijer
G-function provides a flexible framework to discover the mapping between input
variables and output solution. (2) Topological data analysis (TDA) presents an
opportunity to examine how features are connected together via a graph, which
can generate graph measures for feature cliques.

  * •

Local model level: Local interpretable model-agnostic explanations (LIME)
introduces a measure of complexity, such that one solves the following to
obtain the minimum explanation, where we measure how unfaithful a reduced
model is in the locality of a data point.

Examples of ATM algorithms they can be deployed for is presented in Table 1.

####  IV-A3 Transparency & Trust in AI Models that Inform blockchain Security
Requirements:

Human trust of data and AI is premised often on 2 categories: physical trust
(e.g., XAI performance with secure and verified data - see above), and
emotional trust (e.g., familiarity) [10]. Here, we draw on the aforementioned
XAI models to inform both the physical trust (data and its ability to
influence our AI model) and emotional trust (measure a human operator’s
sentiment). For example, input flight data that the AI algorithm finds to be
more sensitive to, less transparent, and humans trust less will need greater
security requirements. Therefore, the miscellaneous trust and algorithm
performance metrics need to be transformed into blockchain security
requirements. This is then back propagated to the blockchain controller (see
Section II-III) to maintain or change the consensus distribution - see
Fig.3(bottom).

###  IV-B Case Study: Flight Trajectory and Delay Prediction with Federated
Learning

We are applying AI to predict the trajectory, as well as the arrival delay.
Diverse data from airlines and the environment is used to inform the AI
engine. We mainly build a RNN that ingests previous historical aircraft
trajectory and general knowledge of the aircraft and airline. Example data
including aircraft type, cruise altitude, carrier market segment, time of
month, week day and arrival hour, and airline operator. We then inform this
with additional meteorological data (temperature, wind, air convective
patterns) which is processed by a parallel bank of CNNs, and this feeds into
the aforementioned RNN structure. The data pipelines use the blockchain
consensus distribution method detailed in Sections II-III, using a range of
PoX and CFT for this mission critical application. The RNN & CNN architecture
is then federated into separate aircraft to make local predictions, whilst
minimizing the data exchange with the ground-based ATM/UTM provider.

Whilst deep learning is inherently less explainable, we can use a variety of
emerging XAI methods (see Fig.3(bottom)) [11] to show key features driving
predictions, providing better understanding of the model and also areas that
need operational improvements. We see that the SHAP XAI analysis indicates
sensitive to: (1) aircraft type, (2) requested flight cruise altitude level,
and (3) market segment. This indicates that some data chains may need to
improve their blockchain consensus type from PoX to CFT or BFT, depending on
the wireless network available. These requirements will be backpropagated to
the blockchain controllers explained in Sections II and III.

##  V Open Challenges

We are at the intersection of two major drivers for ATM transformation – the
need for its modernisation to cater for the ever-increasing demand in air
traffic, and the need for increased automation and autonomy to integrate the
new UAS entrants, without adding to the already saturated workload of human
operators. In this review, we examined the development of aviation mission-
aware blockchains to distribute consensus. Decentralised machine learning is
used to generate locally adaptable and ultra-reliable solutions for mission
critical applications such as collision avoidance. When connected together
with AI-enabled UTM, this new digital ecosystem blueprint is tailor designed
for mission and safety critical autonomy sectors. Yet, it still requires
significant research and development by the academic and industrial community
to ensure it can enable future air and autonomous mobility.

Future research challenges in this area include:

  1. 1.

High reliable and low latency consensus (at MAC and higher layers of network):
critical for the ATM/UTM systems for real-time decisions.

  2. 2.

Networked privacy-preserving tracking: integrating blockchains that enable
secure Self Sovereign Identity (SSI) for UAS threat identification.

  3. 3.

Cognitive UTM: The transition towards Performance-Based Regulations (PBR) and
Performance-Based Operations (PBOs) (see EASA PBE 2014, FAA PBO 2018 reports)
aim to enable integration of more autonomy, including regulatory approval of
AI solutions [11].

We acknowledge that as we go forwards, the aeronautical data services will be
more diverse, complex and dynamic than what is available today, and these will
have to be accommodated whilst still achieving and even improving the level of
integrity and reliability of existing conventional operations. Safety critical
information will be needed at a much higher fidelity than in today’s
solutions.

## Acknowledgements

We would like to thank the Fly2Plan project (70883) industrial (Heathrow
Airport, National Air Traffic Services, SITA, International Airlines Group,
Rockport, Altitude Angel, SCirium, IBS, Consortiq and the Digital Catapult) as
well as research contributors (Mr. A. El Youmi) that provided data and
conducted research far beyond the scope of this review.

## References

  * [1] D. Gómez Depoorter and W. Kellerer, “Designing the air–ground data links for future air traffic control communications,” _IEEE Transactions on Aerospace and Electronic Systems_ , vol. 55, no. 1, pp. 135–146, 2019.
  * [2] H.-N. Dai, Y. Wu, M. Imran, and N. Nasser, “Integration of blockchain and network softwarization for space-air-ground-sea integrated networks,” _IEEE Internet of Things Magazine_ , vol. 5, no. 1, pp. 166–172, 2022.
  * [3] Y. Wu, X. Lu, and Z. Wu, “Blockchain-based trust model for air traffic management network,” in _2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS)_ , 2021, pp. 92–98.
  * [4] EASA, “AI Roadmap - A human-centric approach to AI in aviation,” _European Union Aviation Safety Agency_ , 2020.
  * [5] S. Ouahouah, M. Bagaa, J. Prados-Garzon, and T. Taleb, “Deep-Reinforcement-Learning-Based Collision Avoidance in UAV Environment,” _IEEE Internet of Things Journal_ , vol. 9, no. 6, pp. 4015–4030, 2022.
  * [6] S. Aggarwal, N. Kumar, and S. Tanwar, “Blockchain-envisioned uav communication using 6g networks: Open issues, use cases, and future directions,” _IEEE Internet of Things Journal_ , vol. 8, no. 7, pp. 5416–5441, 2021.
  * [7] Y. Wang, Z. Su, J. Ni, N. Zhang, and X. Shen, “Blockchain-empowered space-air-ground integrated networks: Opportunities, challenges, and solutions,” _IEEE Communications Surveys & Tutorials_, vol. 24, no. 1, pp. 160–209, 2022.
  * [8] W. Sun, L. Wang, P. Wang, and Y. Zhang, “Collaborative blockchain for space-air-ground integrated networks,” _IEEE Wireless Communications_ , vol. 27, no. 6, pp. 82–89, 2020.
  * [9] L. Xia, Y. Sun, R. Swash, L. Mohjazi, L. Zhang, and M. A. Imran, “Smart and Secure CAV Networks Empowered by AI-Enabled Blockchain: The Next Frontier for Intelligent Safe Driving Assessment,” _IEEE Network_ , vol. 36, no. 1, pp. 197–204, 2022.
  * [10] C. Li, W. Guo, S. Sun, S. Al-Rubaye, and A. Tsourdos, “Trustworthy Deep Learning in 6G Enabled Mass Autonomy: from Concept to Quality-of-Trust KPIs,” _IEEE Vehicular Technology Magazine_ , 2020.
  * [11] C. S. Hernandez, S. Ayo, and D. Panagiotakopoulos, “An Explainable Artificial Intelligence (xAI) Framework for Improving Trust in Automated ATM Tools,” _IEEE/AIAA Digital Avionics Systems Conference_ , 2021.
  * [12] V. Sharma, I. You, D. N. K. Jayakody, D. G. Reina, and K.-K. R. Choo, “Neural-blockchain-based ultrareliable caching for edge-enabled UAV networks,” _IEEE Transactions on Industrial Informatics_ , vol. 15, no. 10, pp. 5723–5736, 2019.
  * [13] X. Li, P. Jiang, T. Chen, X. Luo, and Q. Wen, “A survey on the security of blockchain systems,” _Future Generation Computer Systems_ , vol. 107, pp. 841–853, 2020.
  * [14] L. Axon, M. Goldsmith, and S. Creese, “Privacy requirements in cybersecurity applications of blockchain,” _Advances in Computers_ , vol. 111, 2018.
  * [15] W. Guo, “Explainable Artificial Intelligence (XAI) for 6G: Improving Trust between Human and Machine,” _IEEE Communications Magazine_ , vol. 58, no. 6, 2020.


# Single Molecule SERS in a Single Gold Nanoparticle-driven Thermoplasmonic
Tweezer

Sunny Tiwari<EMAIL_ADDRESS>Department of Physics,
Indian Institute of Science Education and Research, Pune-411008, India
Utkarsh Khandelwal  Department of Physics, Indian Institute of Science
Education and Research, Pune-411008, India  Vandana Sharma  Department of
Physics, Indian Institute of Science Education and Research, Pune-411008,
India  G.V. Pavan Kumar<EMAIL_ADDRESS>Department of Physics, Indian
Institute of Science Education and Research, Pune-411008, India

###### Abstract

Surface enhanced Raman scattering (SERS) is optically sensitive and chemically
specific to detect single molecule spectroscopic signatures. Facilitating this
capability in optically-trapped nanoparticles at low laser power remains a
significant challenge. In this letter, we show single molecule SERS signatures
in reversible assemblies of trapped plasmonic nanoparticles using a single
laser excitation (633 nm). Importantly, this trap is facilitated by the
thermoplasmonic field of a single gold nanoparticle dropcasted on a glass
surface. We employ bi-analyte SERS technique to ascertain the single molecule
statistical signatures, and identify the critical parameters of the
thermoplasmonic tweezer that provide this sensitivity. Furthermore, we show
the utility of this low power ($\approx$0.1mW/$\mu$m2) tweezer platform to
trap single gold nanoparticle and transport assembly of nanoparticles. Given
that our configuration is based on a dropcasted gold nanoparticle, we envisage
its utility to create reconfigurable plasmonic metafluids in physiological and
catalytic environments, and can be potentially adapted as an in-vivo plasmonic
tweezer.

###### keywords:

thermoplasmonic, single molecule SERS, reversible trapping, tweezer

Optical confinement and interrogation of single molecules in a fluid remains
one of the vital goals of nanoscience and technology1, 2, 3, 4, 5, 6. This
letter discusses how single molecule surface enhanced Raman scattering (SERS)
can be performed in a single gold nanoparticle-driven thermoplasmonic trap of
plasmonic particles.

Fluorescence and Raman scattering have been extensively used to study
molecules in a fluid. Generally, fluorescence is sensitive enough to detect
single molecules in a fluid, but Raman scattering remains a weak process due
to low optical cross-sections2. An important way to overcome this problem is
to use SERS7, 8, 9, 10, 11, a spectroscopic method that is sensitive enough to
detect single molecules in a fluid phase7, 12, 13, 14, 15, 16, 17, 18. An
important prerequisite for single molecule SERS (SM-SERS) detection is the
enhanced electromagnetic fields19, 20, 21, 22. Plasmonic structures have been
extensively designed and utilized for near-field interaction with the
molecules23, 24, 25, 26, 27, and thereby influencing their optical emission
not only in terms of their intensity28, 29, 30 but also in terms of their
directivity31, 32. Optical trapping33, 34, 35, 36 and assembly of
nanoparticles in a fluid37, 38, 39, 40 are one of the effective ways to create
large-scale electromagnetic enhancement with reconfigurable nano-constructs
that can be deterministically transported within the fluidic environment41,
42.

In this context, performing SERS in an optical or an optothermal trap has
emerged as an interesting prospect41, 43, 44, 45, and there have been some
attempts towards this end, including our work on single-molecule SERS in an
evanescent plasmonic trap13. Many of the SERS studies in optical traps require
multiple lasers operating at relatively high powers46, 47. For single molecule
studies, this may not be a conducive condition. Plasmonic tweezers48, 49, 50,
51, 52, 53, 54 have emerged as an alternative to trap nanoscale entities with
low power budget55, 56. In recent times, the thermoplasmonic fields around a
metal film57, 41 or other designed nanostructures58, 59, 60 have been utilized
to optically trap nano and micro objects at extremely low powers.

Although, a large number of studies have shown the capabilities of
thermoplasmonic tweezer61, 62, 63, 64, 65 to manipulate nanoscale objects,
there is still a requirement of tweezer strategies that can be mobile in a
fluid and adaptable in biological conditions such as cellular interiors. One
of the simplest possible thermoplasmonic nanostructure66, 67 that can be
introduced in a variety of environment is a chemically prepared gold
nanoparticle that can be directly dropcasted from solution phase66, 68, 69.
Furthermore, it is vital to test the capability of such gold nanoparticles to
act like a trapping location for SM-SERS studies.

Motivated by these requirements, in this letter we show how SM-SERS can be
performed in a thermoplasmonic trap driven by a single, dropcasted gold
nanoparticle. This dropcasted nanostructure acts as an anchor trap to create
an assembly of plasmonic particles which can be further utilized for SM-SERS
studies. By utilizing bi-analyte70 SERS technique, we provide experimental
proof of SM-SERS signature in such a thermoplasmonic traps. Furthermore, we
show how such nanoparticle based thermoplasmonic trap can be utilized for
single nanoparticle trapping and optical transport of nanoparticle assemblies,
all at a very low laser power density.

Figure 1: Schematic of the experimental configuration to perform SM-SERS in a
low power, single nanoparticle driven thermoplasmonic tweezer. (i) A single
150 nm AuNP dropcasted on a glass substrate to be operated as an anchor
particle for trapping. (ii) The AuNP was surrounded by a solution containing
CTAC and KCl mixed with molecules and plasmonic particles (AuNPs or Lee-Meisel
AgNPs). (iii) A low power density laser source ($\approx$0.1mW/$\mu$m2) was
used to heat the AuNP for achieving reversible assembly of plasmonic
particles. (iv) SERS was performed on the molecules in the assembled plasmonic
particles using the same laser source. (v) Modified principal component
analysis was performed to statistically confirm SM-SERS signature.

A schematic of the experimental configuration is shown in figure 1. A single
150 nm diameter gold nanoparticle (AuNP) was dropcasted on a glass substrate
to work as an anchor particle to create an assembly of plasmonic structures.
The AuNP was surrounded with an aqueous solution of plasmonic particles,
molecules, surfactant cetyltrimethylammonium chloride (CTAC) and potassium
chloride (KCl). The plasmonic particle assembly provides the electromagnetic
enhancement for the SM-SERS 7. The CTAC and the KCl were used as assisting
agents for thermoplamsonic trapping and SM-SERS, respectively. We discuss
their role later in the manuscript. A single, low power-density laser was used
to simultaneously heat the anchor metal nanoparticle66 for thermoplasmonic
trapping of plasmonic particles, and as an excitation source for SERS. For SM-
SERS validation, we used bi-analyte technique. Two types of molecules were
added in the solution after which modified principal component analysis (MPCA)
was performed to statistically confirm the SM-SERS signature71. See Supporting
information S1 for detailed schematic of optical microscope, and S2 for
discussion on mechanism of the reversible trap.

Figure 2: Reversible trapping of plasmonic particles in the low power
thermoplasmonic tweezer. Time series dark field images of (a) assembly of 250
nm AuNPs and (b) disassembly of the same AuNPs when the laser was turned off.
The dotted, white circle represents the location of the anchor 150 nm AuNP.
Excitation at 633 nm laser with a power density of 0.08 mW/$\mu$m2. (c)
Calculated temperature distribution around a 150 nm AuNP placed on a glass
substrate and surrounded by water. Excitation at 633 nm laser with a power
density of 0.08 mW/$\mu$m2 (d) Calculated temperature gradient $\nabla$T for
the same geometry. (e) Calculated temperature gradient $\nabla$T in the
z-plane. See Supporting Movie 1 for trapping and release of 250 nm AuNPs.

First, we discuss the experiments on reversible trapping of 250 nm AuNPs in a
CTAC solution. Figure 2(a) and (b) show the trapping and release sequence of
250 nm AuNPs. A 150 nm AuNP was excited by focusing a 0.74 mW laser of
wavelength 633 nm at a 3.34 µm spot size by using a 50x, 0.50 NA objective
lens. The AuNP was surrounded with an aqueous solution having 12 mM CTAC. As
the laser was turned on, the plasmonic particles were seen to be trapped near
the anchor AuNP and within 10 s, an assembly of size greater than 5 $\mu$m was
achieved. The assembly disappears within a few seconds after turning off the
laser. This process can be reproduced over multiple cycles. See Supporting
Movie 1 for trapping and release of 250 nm AuNPs.

In the absence of the CTAC, no trapping of plasmonic particles was observed at
the same input power density41. The particles get scattered away because of
the large scattering force when they enter the excitation volume (see
Supporting Movie 2). Assembly of nanoparticles of size as large as 400 nm were
also created efficiently at low power density, using the single nanoparticle
driven thermoplasmonic trapping (see Supporting Movie 3). A slightly more
efficient trapping at 532 nm was achieved with a laser source of 532 nm which
is nearer to the absorption maxima of the anchor particle. See Supporting
Information S3 and S4 for the details on the measurement of scattering cross-
section and calculation of absorption cross-section of 150 nm AuNP placed on a
glass substrate and surrounded with water, and trapping experiments performed
with a 532 nm laser. Note that our thermoplasmonic tweezer is mainly driven by
a single anchor AuNP on a glass substrate, in contrast to metal film-based
tweezers 57.

To further understand the optothermal effects 57 in our tweezer, we performed
finite element method simulations (COMSOL). The refractive index of gold was
taken from ref.72 (see Supporting Information S5 for details on numerical
calculations). Figure 2(c) shows the calculated temperature distribution of a
150 nm AuNP placed on a glass substrate and surrounded with water. A
temperature increment of $\approx$27 K was obtained by exciting the AuNP with
a laser of power density 0.08 mW/$\mu$m2. In addition, there was a temperature
gradient, both in-plane and out-of-plane of our geometry, because of the
heating of AuNP as shown in figure 2(d) and (e), respectively. This gradient
has an important connection to CTAC in the solution. The plasmonic particles
in the fluid and the anchor particles were coated with a positive CTAC double
layer because of the addition of CTAC in the solution73. Since, the
concentration of CTAC is greater than critical micelle concentration(0.3 mM),
the solution also contains CTAC micelles and negatively charged chlorine ions.
The temperature gradient creates a thermophoretic movement74 of CTAC double
layer coated plasmonic particles, and CTAC micelles and Cl- ions present in
the solution and facilitates the reversible trapping of metallic structure.
The particles get trapped near the heated anchor AuNP because of balancing of
the thermogradient force between negatively charged Cl- ions and positively
charged CTAC micelles, and the electrostatic repulsive force between the
positive coating of CTAC double layer on the anchor particle and CTAC double
layer coated metal particles.

Figure 3: Optimization of experimental configuration for SM-SERS. (a)
Schematic of the SERS performed for different plasmonic particles (250 nm and
100 nm AuNPs, and Lee-Meisel AgNPs) with Rh6G molecules. (b) Compassion of the
SERS spectra at same input power using 250 nm AuNPs, 100 nm AuNPs and Lee-
Meisel AgNPs (with varied concentrations of CTAC and KCl). Time series images
of (c) assembly and disassembly of the Lee-Meisel AgNPs. (d) Schematic of SM-
SERS using bi-analyte method at 633 nm laser. The trapping configuration
included the following: 150 nm anchor AuNP, Lee-Meisel AgNPs in solution, NB
and Rh6G molecules in the presence of CTAC (2 mM) and KCl (5 mM). (e) 4000 bi-
analyte SERS spectra recorded from a solution containing 1 nM of both NB and
Rh6G molecules. The integration time for single spectrum was 0.3 s and the
dwell time between two consecutive spectra was 1 s. (f) SERS intensity
variation of 592 cm-1 and 612 cm-1 peaks of NB and Rh6G molecules,
respectively, as a function of spectrum number. (g) Same as the (f), but for a
smaller window of spectral numbers indicating higher resolution. See
Supporting Movie 4 and 5 for trapping and release of Lee-Meisel AgNPs
respectively.

Having shown the formation of large scale assembly, next we show how the
assembly can be utilized for performing SERS of rhodamine 6g (Rh6G) molecules
and compare its performance for various plasmonic particles. A schematic of
the experimental design is shown in figure 3a. A reversible assembly of
plasmonic particles (250 or 100 nm AuNPs or Lee-Meisel AgNPs) was achieved by
heating a 150 nm AuNP. The anchor AuNP was illuminated with a water immersion
1.2 NA, 60x objective lens by focusing a 0.80 mW, 633 nm laser to a spot size
of 2.70 µm. (For this optical excitation, the temperature distribution, and
gradients are shown in Supporting Information S6 ) After an assembly formation
of about the spot size, SERS spectroscopy of Rh6G molecules was performed in
the backscattering configuration using the same excitation laser used for the
trapping purpose. Figure 3b compares the SERS spectrum collected from 10-6M
Rh6G molecules in the assembly of 250 nm AuNPs, 100 nm AuNPs and Lee-Meisel
AgNPs. As can be seen from the SERS spectra figure 3b, the SERS sensitivity
was superior for Lee-Meisel AgNPs in the presence of CTAC and KCl. From the
past work, it is well known that Lee-Meisel AgNPs are excellent substrates for
achieving large SERS enhancement75, 7, when a small amount of KCl is added to
the solution 76, 77, 78. Therefore, prior to adding CTAC in the solution
containing Lee-Meisel AgNPs and molecules, KCl was added in the solution. The
addition of KCl initiates dimer formation of AgNPs which creates hotspots for
SERS enhancement 76. A low concentration of CTAC (2 mM) was added after
formation of dimers, which leaves enough space for the molecules to access the
hotspots. Figure 3c shows the trap and release of large scale assembly of Lee-
Meisel AgNPs using nanoparticle driven thermoplasmonic trap. An assembly of
few microns was formed within tens of seconds after the anchor particle was
illuminated with the laser source. The assembly disappears very fast after the
laser is turned off. See Supporting Movie 4 and 5 for trapping and releasing
of Lee-Meisel AgNPs respectively), and SI 7 for details on preparation of Lee-
Meisel AgNPs.

To push the limit of single nanoparticle driven thermoplasmonic trap, we
performed SM-SERS spectroscopy on the assembly of trapped Lee-Meisel AgNPs
using bi-analyte technique, whose details can be found elsewhere 70, 79, 13.
Figure 3(d) shows the schematic of the experimental configuration to perform
bi-analyte SERS experiments using two types of molecules, which compete for
the same hotspot70. The solution contains two types of molecules, Nile Blue
(NB) (1 nM) and Rh6G (1 nM) mixed with Lee-Meisel AgNPs. As discussed earlier,
the illuminated anchor AgNPs drives the assembly and SERS was performed from
the assembly in the presence of CTAC and KCl (concentration at 2 mM and 5 mM
respectively). Various other control experiments were performed (See
Supporting Information S8 and S9 for bi-analyte SERS experiments performed
without adding KCl and on a location away from assembly respectively. Also see
Supporting Information S10 for SERS experiments performed using a 532 nm
laser)

Next, we discuss the obtained SERS spectra. A waterfall plot of 4000 spectra
containing closely spaced NB SERS peak 592 cm-1 and Rh6G SERS peak (612 cm-1)
is shown in figure 3(e). The exposure time for each spectrum was 0.3s and
dwell time between two consecutive spectra was 1 s. The variation in the peak
intensity can be seen clearly. Figure 3(f) shows the occurrence and variation
in the intensity of both 592 cm-1 peak of NB and 612cm-1 peak of Rh6G
molecules as a function of spectrum number. The zoomed-in portion of spectra
from 665 to 697 (figure 3(g)) shows the individual events of both molecules
and a mixed event. At an event number 673 only an intense NB peak of 592 cm-1
was observed, whereas at event number 681 both peaks (592 cm-1 of NB and 612
cm-1 of Rh6G molecule) was seen, and at 696, only an intense peak of 612 cm-1
of Rh6G peak was visualized. These fluctuating individual events suggest that
the single nanoparticle driven thermoplasmonic trap can be used to perform SM-
SERS.

Figure 4:  Modified principal component analysis of bi-analyte SM-SERS. (a)
Eigenvalues obtained from PCA of 4000 spectra consisting of 50 wavelengths
containing the 592cm-1 and 612 cm-1 peaks of NB and Rh6G, respectively. (b)
Distribution of the covariant matrix coefficients ($\alpha$ and $\beta$)
obtained after PCA showing two Rh6G and NB axes. (c) Distribution of the
transformed matrix coefficients $\alpha^{\prime}$ and $\beta^{\prime}$ showing
two orthogonal Rh6G and NB axes. (d) Probability of NB events plotted using
(c) after removing the mixed dye events shown in a black region in (c). (e)
Four possible events: (i) Mixed event obtained near the center of the
histogram (d). (ii) NB event obtained near the region of probability of NB
event=1 (iii) Rh6G event obtained near the region of probability of NB
event=0. (iv) No event obtained when probabilities of both NB event and Rh6G
event are 0.

To statistically confirm the single molecule SERS signature, we performed
modified principal component analysis (MPCA)71, 79, 13 on 4000 spectra
collected (figure 3(e)) from the solution containing both NB and Rh6G
molecules. Figure 4a shows the plot of calculated eigenvalues and
eigenvectors, where the first two elements are the principal eigenvalues.
Figure 4b shows the scatterplot of two covariant matrix coefficients $\alpha$
and $\beta$, with two axes shown as NB and Rh6G. Each data point in the
scatterer plot represents an event. The re-oriented scatterer plot
($\alpha^{\prime}$ and $\beta^{\prime}$) in figure 4c shows two orthogonal
axes Rh6G and NB axes, where events on both axes represent pure events of SERS
from only one type of molecule. Events in between the two axes represent mixed
events which are signals from both the types of molecules. To calculate the
frequency of single molecule events, a histogram was plotted in figure 4d
where the frequency of only NB molecule was plotted after removing noisy mixed
events as shown by a black box in figure 4c. The extreme left part of the
histogram represents the events where SERS from only Rh6G molecules was
recorded. The spectrum in figure 4 (e)(iii) shows such a type of an intense
pure event from only Rh6G molecules. The center part of the histogram shows
the mixed events from both types of molecules and one such event is shown in
figure 4(e)(i). The extreme right part of the histogram displays the
probability of only NB molecule events and one such event is shown in figure
4(e)(ii). Along with these three events, a relatively less probable null event
was also observed, which is shown in figure 4(e)(iv). For further details on
the MPCA analysis, see Supporting Information S11.

Figure 5: Versatility of nanoparticle driven thermoplasmonic tweezer. (a)
Tweezer driven by anchor AuNP. Time series dark field images of assembly and
its translation along two axes. (b) Tweezer driven without anchor AuNP. Time
series dark field images of assembly and its translation along two axes. Note
that this assembly process is stochastic and takes longer duration compared to
the previous case. (c) Single particle trapping and release. A reversible
dimer formation was observed. Excitation wavelength: 633 nm at a power density
of 0.08 mW/$\mu$m2, anchor AuNP: 150 nm, trapped plasmonic particles: 250 nm
AuNPs, for all the observations. See Supporting Movie 6,7 and 8 related to the
above data.

Can our tweezer platform assemble and transport plasmonic structures? Can such
a platform trap single nanoparticles at the low laser power densities? In
order to address these questions, further experiments were performed as shown
in figure 5. First, we trapped and transported an assembly of 250 nm AuNPs
along two different axes, as shown in figure 5a. We performed this transport
by holding the trap at its position and moving the motorized-stage of the
optical microscope. An interesting aspect of this transport is that once
assembled, the plasmonic particles do not need the assistance of the anchor
nanoparticles for further trapping. This may be due to temperature increment
and gradient facilitated by the assembly and hence acting as a ‘self-
sufficient’ trap. See Supporting Movie 6 showing the working of tweezer driven
by anchor AuNP. Secondly, we show that the assembly can also be created
without the assistance of anchor NP (figure 5b). Although we could trap and
transport the assembly of plasmonic particles, we found the trapping process
to be stochastic and can take much longer times when compared to anchor AuNP
based tweezers (figure 5a). The figure 5(b) shows the formation of the
assembly when the laser was illuminated on a glass substrate. Initially, a
single particle comes under the excitation volume and gets pushed towards the
glass substrate because of scattering force, and it stays there for finite
duration. Within that time, the temperature of the particle increases, which
leads to the formation of a thermal gradient, and hence a trap. See Supporting
Movie 7 showing the working of tweezers driven without anchor nanoparticles.

Finally, we show how a single 250 nm plasmonic particle can also be trapped
using a single, anchor AuNP, thus forming a dynamic, plasmonic dimer. Figure
5(c) shows the reversible trapping and release of a single 250 nm AuNP.
Turning off the laser results in the release of the trapped particle from the
anchor location. Turning on the laser again, further traps the nanoparticle
thus confirming the reversibility. See Supporting Movie 8 showing single
nanoparticle trapping.

To conclude, we have shown how a single dropcasted gold nanoparticle on a
glass substrate can be utilized as a thermoplasmonic tweezer to reversibly
assemble plasmonic nanoparticles for SM-SERS experiments. By employing bi-
analyte SERS technique we have revealed the single-molecule spectral dynamics
and its extreme statistical signatures in the assembly of nanoparticles. We
have emphasized the relevance of surfactant and salt solution in creating a
conducive environment for trapping and SM-SERS at low laser powers
($\approx$0.1mW/$\mu$m2) . Furthermore, we have revealed the capability of the
tweezer to not only trap single plasmonic nanoparticles, but also to transport
plasmonic nanoparticle assembly to a desired location. Such an assembled
plasmonic entity can be potentially harnessed as components of plasmonic meta-
fluid. Given that our tweezer platform is based on a colloidal gold
nanoparticle, it can be chemically functionalized for targeted trapping of
nano-objects including bio-molecules. We envisage its utility as a flexible
trapping and transport platform in-vivo, and anticipate further exploration on
colloidal plasmonic tweezers sensitive to single-molecule reactions in
physiological conditions.

Authors thank Naveen Nishad and Angira Rastogi (IISER Pune) for helping with
the MPCA code, and Debashree Roy, Diptabrata Paul, and Shailendra K Chaubey
(IISER Pune) for fruitful discussion. Authors also thank Dr. Rohit Chikkaraddy
(University of Cambridge) and Dr. Partha Pratim Patra (Dispelix Oy) for a
discussion on MPCA, and Gokul MA and Pranab Dutta for helping in experiments.
This work was partially funded by Air Force Research Laboratory grant
(FA2386-18-1-4118 R& D18IOA118), DST Energy Science grant (SR/NM/TP-13/2016),
and Swarnajayanti fellowship grant (DST/SJF/PSA02/2017-18) to G V PK.

The Supporting Information is available free of charge.

Experimental setup, mechanism of trap, scattering and absorption cross-
sections, details on FEM simulations, details on MPCA, controlled experiments
on bi-analyte SERS with 633 and 532 nm laser. (pdf)

The following videos can be found here

https://www.youtube.com/playlist?list=PLVIRTkGrtbrvs7BaNsaH6tjPpzLUizyMI

Reversible trapping of 250 nm AuNPs (AVI), Effect of CTAC on trapping (AVI),
Reversible trapping of 250 nm AuNPs (AVI), Trapping of Lee-Meisel AgNPs (AVI),
Release of Lee-Meisel AgNPs assembly (AVI), Working of tweezer driven by
anchor AuNP (AVI), Working of tweezer driven without anchor AuNP (AVI), Single
AuNP trapping (AVI), Reversible trapping of 250 nm AuNPs (AVI), Unprocessed
movie of reversible trapping of Lee-Meisel AgNPs (AVI).

## References

  * Moerner 2020 Moerner, W. E. Viewpoint: Single Molecules at 31: What’s Next? _Nano Letters_ 2020, _20_ , 8427–8429
  * Kulzer and Orrit 2004 Kulzer, F.; Orrit, M. Single-molecule optics. _Annu. Rev. Phys. Chem._ 2004, _55_ , 585–611
  * Wang et al. 2012 Wang, Q.; Goldsmith, R. H.; Jiang, Y.; Bockenhauer, S. D.; Moerner, W. Probing single biomolecules in solution using the anti-Brownian electrokinetic (ABEL) trap. _Accounts of chemical research_ 2012, _45_ , 1955–1964
  * Bishop and Landes 2018 Bishop, L. D.; Landes, C. F. From a protein’s perspective: elution at the single-molecule level. _Accounts of chemical research_ 2018, _51_ , 2247–2254
  * Bespalova et al. 2019 Bespalova, M. I.; Mahanta, S.; Krishnan, M. Single-molecule trapping and measurement in solution. _Current opinion in chemical biology_ 2019, _51_ , 113–121
  * Fränzl et al. 2019 Fränzl, M.; Thalheim, T.; Adler, J.; Huster, D.; Posseckardt, J.; Mertig, M.; Cichos, F. Thermophoretic trap for single amyloid fibril and protein aggregation studies. _Nature methods_ 2019, _16_ , 611–614
  * Le Ru and Etchegoin 2008 Le Ru, E.; Etchegoin, P. _Principles of Surface-Enhanced Raman Spectroscopy: and related plasmonic effects_ ; Elsevier, 2008
  * Langer et al. 2019 Langer, J.; Jimenez de Aberasturi, D.; Aizpurua, J.; Alvarez-Puebla, R. A.; Auguié, B.; Baumberg, J. J.; Bazan, G. C.; Bell, S. E.; Boisen, A.; Brolo, A. G., et al. Present and future of surface-enhanced Raman scattering. _ACS nano_ 2019, _14_ , 28–117
  * Kumar 2012 Kumar, G. V. P. Plasmonic nano-architectures for surface enhanced Raman scattering: a review. _Journal of Nanophotonics_ 2012, _6_ , 064503
  * Lim et al. 2010 Lim, D.-K.; Jeon, K.-S.; Kim, H. M.; Nam, J.-M.; Suh, Y. D. Nanogap-engineerable Raman-active nanodumbbells for single-molecule detection. _Nature materials_ 2010, _9_ , 60–67
  * Park et al. 2018 Park, J.-E.; Jung, Y.; Kim, M.; Nam, J.-M. Quantitative Nanoplasmonics. _ACS Central Science_ 2018, _4_ , 1303–1314
  * Le Ru and Etchegoin 2012 Le Ru, E. C.; Etchegoin, P. G. Single-molecule surface-enhanced Raman spectroscopy. _Annual review of physical chemistry_ 2012, _63_ , 65–87
  * Patra et al. 2014 Patra, P. P.; Chikkaraddy, R.; Tripathi, R. P.; Dasgupta, A.; Kumar, G. V. P. Plasmofluidic single-molecule surface-enhanced Raman scattering from dynamic assembly of plasmonic nanoparticles. _Nature communications_ 2014, _5_ , 1–8
  * Ahmed and Gordon 2012 Ahmed, A.; Gordon, R. Single molecule directivity enhanced Raman scattering using nanoantennas. _Nano letters_ 2012, _12_ , 2625–2630
  * Haran 2010 Haran, G. Single-molecule Raman spectroscopy: a probe of surface dynamics and plasmonic fields. _Accounts of chemical research_ 2010, _43_ , 1135–1143
  * Weiss and Haran 2001 Weiss, A.; Haran, G. Time-dependent single-molecule Raman scattering as a probe of surface dynamics. _The Journal of Physical Chemistry B_ 2001, _105_ , 12348–12354
  * Zrimsek et al. 2013 Zrimsek, A. B.; Henry, A.-I.; Van Duyne, R. P. Single molecule surface-enhanced Raman spectroscopy without nanogaps. _The Journal of Physical Chemistry Letters_ 2013, _4_ , 3206–3210
  * Dieringer et al. 2007 Dieringer, J. A.; Lettan, R. B.; Scheidt, K. A.; Van Duyne, R. P. A frequency domain existence proof of single-molecule surface-enhanced Raman spectroscopy. _Journal of the American Chemical Society_ 2007, _129_ , 16249–16256
  * Liebel et al. 2020 Liebel, M.; Pazos-Perez, N.; van Hulst, N. F.; Alvarez-Puebla, R. A. Surface-enhanced Raman scattering holography. _Nature Nanotechnology_ 2020, _15_ , 1005–1011
  * Alvarez-Puebla et al. 2011 Alvarez-Puebla, R. A.; Agarwal, A.; Manna, P.; Khanal, B. P.; Aldeanueva-Potel, P.; Carbó-Argibay, E.; Pazos-Pérez, N.; Vigderman, L.; Zubarev, E. R.; Kotov, N. A., et al. Gold nanorods 3D-supercrystals as surface enhanced Raman scattering spectroscopy substrates for the rapid detection of scrambled prions. _Proceedings of the National Academy of Sciences_ 2011, _108_ , 8157–8161
  * Pozzi et al. 2015 Pozzi, E. A.; Zrimsek, A. B.; Lethiec, C. M.; Schatz, G. C.; Hersam, M. C.; Van Duyne, R. P. Evaluating single-molecule Stokes and anti-Stokes SERS for nanoscale thermometry. _The Journal of Physical Chemistry C_ 2015, _119_ , 21116–21124
  * Kleinman et al. 2011 Kleinman, S. L.; Ringe, E.; Valley, N.; Wustholz, K. L.; Phillips, E.; Scheidt, K. A.; Schatz, G. C.; Van Duyne, R. P. Single-molecule surface-enhanced Raman spectroscopy of crystal violet isotopologues: theory and experiment. _Journal of the American Chemical Society_ 2011, _133_ , 4115–4122
  * Roy et al. 2021 Roy, D.; Rajendra, R.; Tripathi, S.; Ballav, N. Controlling the Aspect Ratios of Au Nanocrystals with Ag+ Addition Time in Seed-Mediated Synthesis: Implications for Surface-Enhanced Raman Scattering. _ACS Applied Nano Materials_ 2021, _4_ , 7426–7434
  * Punj et al. 2015 Punj, D.; Regmi, R.; Devilez, A.; Plauchu, R.; Moparthi, S. B.; Stout, B.; Bonod, N.; Rigneault, H.; Wenger, J. Self-assembled nanoparticle dimer antennas for plasmonic-enhanced single-molecule fluorescence detection at micromolar concentrations. _ACS photonics_ 2015, _2_ , 1099–1107
  * Zhang et al. 2019 Zhang, Q.; Hernandez, T.; Smith, K. W.; Jebeli, S. A. H.; Dai, A. X.; Warning, L.; Baiyasi, R.; McCarthy, L. A.; Guo, H.; Chen, D.-H., et al. Unraveling the origin of chirality from plasmonic nanoparticle-protein complexes. _Science_ 2019, _365_ , 1475–1478
  * De Silva Indrasekara et al. 2017 De Silva Indrasekara, A. S.; Shuang, B.; Hollenhorst, F.; Hoener, B. S.; Hoggard, A.; Chen, S.; Villarreal, E.; Cai, Y.-Y.; Kisley, L.; Derry, P. J., et al. Optimization of spectral and spatial conditions to improve super-resolution imaging of plasmonic nanoparticles. _The journal of physical chemistry letters_ 2017, _8_ , 299–306
  * Dominguez-Medina et al. 2016 Dominguez-Medina, S.; Kisley, L.; Tauzin, L. J.; Hoggard, A.; Shuang, B.; DS Indrasekara, A. S.; Chen, S.; Wang, L.-Y.; Derry, P. J.; Liopo, A., et al. Adsorption and unfolding of a single protein triggers nanoparticle aggregation. _ACS nano_ 2016, _10_ , 2103–2112
  * Chikkaraddy et al. 2016 Chikkaraddy, R.; De Nijs, B.; Benz, F.; Barrow, S. J.; Scherman, O. A.; Rosta, E.; Demetriadou, A.; Fox, P.; Hess, O.; Baumberg, J. J. Single-molecule strong coupling at room temperature in plasmonic nanocavities. _Nature_ 2016, _535_ , 127–130
  * Russell et al. 2012 Russell, K. J.; Liu, T.-L.; Cui, S.; Hu, E. L. Large spontaneous emission enhancement in plasmonic nanocavities. _Nature Photonics_ 2012, _6_ , 459–462
  * Punj et al. 2013 Punj, D.; Mivelle, M.; Moparthi, S. B.; Van Zanten, T. S.; Rigneault, H.; Van Hulst, N. F.; Garcia-Parajo, M. F.; Wenger, J. A plasmonic ‘antenna-in-box’platform for enhanced single-molecule analysis at micromolar concentrations. _Nature nanotechnology_ 2013, _8_ , 512–516
  * Vasista et al. 2018 Vasista, A. B.; Jog, H.; Heilpern, T.; Sykes, M. E.; Tiwari, S.; Sharma, D. K.; Chaubey, S. K.; Wiederrecht, G. P.; Gray, S. K.; Kumar, G. V. P. Differential wavevector distribution of surface-enhanced Raman scattering and fluorescence in a film-coupled plasmonic nanowire cavity. _Nano letters_ 2018, _18_ , 650–655
  * Tiwari et al. 2021 Tiwari, S.; Vasista, A. B.; Paul, D.; Chaubey, S. K.; Kumar, G. V. P. Beaming Elastic and SERS Emission from Bent-Plasmonic Nanowire on a Mirror Cavity. _The Journal of Physical Chemistry Letters_ 2021, _12_ , 6589–6595
  * Jones et al. 2015 Jones, P. H.; Maragò, O. M.; Volpe, G. _Optical tweezers: Principles and applications_ ; Cambridge University Press, 2015
  * Maragò et al. 2013 Maragò, O. M.; Jones, P. H.; Gucciardi, P. G.; Volpe, G.; Ferrari, A. C. Optical trapping and manipulation of nanostructures. _Nature nanotechnology_ 2013, _8_ , 807–819
  * Dienerowitz et al. 2008 Dienerowitz, M.; Mazilu, M.; Dholakia, K. Optical manipulation of nanoparticles: a review. _Journal of Nanophotonics_ 2008, _2_ , 021875
  * Devadas et al. 2014 Devadas, M. S.; Li, Z.; Hartland, G. V. Imaging and Analysis of Single Optically Trapped Gold Nanoparticles Using Spatial Modulation Spectroscopy. _The Journal of Physical Chemistry Letters_ 2014, _5_ , 2910–2915, PMID: 26278098
  * Nan and Yan 2019 Nan, F.; Yan, Z. Silver-nanowire-based interferometric optical tweezers for enhanced optical trapping and binding of nanoparticles. _Advanced Functional Materials_ 2019, _29_ , 1808258
  * Han and Yan 2020 Han, F.; Yan, Z. Phase transition and self-stabilization of light-mediated metal nanoparticle assemblies. _ACS nano_ 2020, _14_ , 6616–6625
  * Gargiulo et al. 2017 Gargiulo, J.; Brick, T.; Violi, I. L.; Herrera, F. C.; Shibanuma, T.; Albella, P.; Requejo, F. G.; Cortes, E.; Maier, S. A.; Stefani, F. D. Understanding and reducing photothermal forces for the fabrication of Au nanoparticle dimers by optical printing. _Nano letters_ 2017, _17_ , 5747–5755
  * Yan and Scherer 2013 Yan, Z.; Scherer, N. F. Optical vortex induced rotation of silver nanowires. _The Journal of Physical Chemistry Letters_ 2013, _4_ , 2937–2942
  * Lin et al. 2016 Lin, L.; Peng, X.; Wang, M.; Scarabelli, L.; Mao, Z.; Liz-Marzán, L. M.; Becker, M. F.; Zheng, Y. Light-directed reversible assembly of plasmonic nanoparticles using plasmon-enhanced thermophoresis. _ACS nano_ 2016, _10_ , 9659–9668
  * Ghosh and Ghosh 2018 Ghosh, S.; Ghosh, A. Mobile nanotweezers for active colloidal manipulation. _Science Robotics_ 2018, _3_
  * Fazio et al. 2016 Fazio, B.; D’Andrea, C.; Foti, A.; Messina, E.; Irrera, A.; Donato, M. G.; Villari, V.; Micali, N.; Maragò, O. M.; Gucciardi, P. G. SERS detection of Biomolecules at Physiological pH via aggregation of Gold Nanorods mediated by Optical Forces and Plasmonic Heating. _Scientific reports_ 2016, _6_ , 1–13
  * Messina et al. 2011 Messina, E.; Cavallaro, E.; Cacciola, A.; Saija, R.; Borghese, F.; Denti, P.; Fazio, B.; D’Andrea, C.; Gucciardi, P.; Iati, M., et al. Manipulation and Raman spectroscopy with optically trapped metal nanoparticles obtained by pulsed laser ablation in liquids. _The Journal of Physical Chemistry C_ 2011, _115_ , 5115–5122
  * Kang et al. 2016 Kang, Z.; Chen, J.; Ho, H.-P. Surface-enhanced Raman scattering via entrapment of colloidal plasmonic nanocrystals by laser generated microbubbles on random gold nano-islands. _Nanoscale_ 2016, _8_ , 10266–10272
  * Tong et al. 2009 Tong, L.; Righini, M.; Gonzalez, M. U.; Quidant, R.; Käll, M. Optical aggregation of metal nanoparticles in a microfluidic channel for surface-enhanced Raman scattering analysis. _Lab on a Chip_ 2009, _9_ , 193–195
  * Svedberg et al. 2006 Svedberg, F.; Li, Z.; Xu, H.; Käll, M. Creating hot nanoparticle pairs for surface-enhanced Raman spectroscopy through optical manipulation. _Nano letters_ 2006, _6_ , 2639–2641
  * Kotsifaki and Chormaic 2019 Kotsifaki, D. G.; Chormaic, S. N. Plasmonic optical tweezers based on nanostructures: fundamentals, advances and prospects. _Nanophotonics_ 2019, _8_ , 1227–1245
  * Kotsifaki et al. 2020 Kotsifaki, D. G.; Truong, V. G.; Chormaic, S. N. Fano-Resonant, Asymmetric, Metamaterial-Assisted Tweezers for Single Nanoparticle Trapping. _Nano Letters_ 2020, _20_ , 3388–3395, PMID: 32275440
  * Kotsifaki et al. 2021 Kotsifaki, D. G.; Truong, V. G.; Nic Chormaic, S. Dynamic multiple nanoparticle trapping using metamaterial plasmonic tweezers. _Applied Physics Letters_ 2021, _118_ , 021107
  * Crozier 2019 Crozier, K. B. Quo vadis, plasmonic optical tweezers? _Light: Science & Applications_ 2019, _8_ , 1–6
  * Shoji and Tsuboi 2014 Shoji, T.; Tsuboi, Y. Plasmonic optical tweezers toward molecular manipulation: tailoring plasmonic nanostructure, light source, and resonant trapping. _The journal of physical chemistry letters_ 2014, _5_ , 2957–2967
  * Tsuboi et al. 2010 Tsuboi, Y.; Shoji, T.; Kitamura, N.; Takase, M.; Murakoshi, K.; Mizumoto, Y.; Ishihara, H. Optical trapping of quantum dots based on gap-mode-excitation of localized surface plasmon. _The Journal of Physical Chemistry Letters_ 2010, _1_ , 2327–2333
  * Masuhara and Yuyama 2021 Masuhara, H.; Yuyama, K.-i. Optical Force-Induced Chemistry at Solution Surfaces. _Annual Review of Physical Chemistry_ 2021, _72_ , 565–589
  * Zhang et al. 2021 Zhang, Y.; Min, C.; Dou, X.; Wang, X.; Urbach, H. P.; Somekh, M. G.; Yuan, X. Plasmonic tweezers: for nanoscale optical trapping and beyond. _Light: Science & Applications_ 2021, _10_ , 1–41
  * Li et al. 2021 Li, N.; Cadusch, J.; Liu, A.; Barlow, A. J.; Roberts, A.; Crozier, K. B. Algorithm-Designed Plasmonic Nanotweezers: Quantitative Comparison by Theory, Cathodoluminescence, and Nanoparticle Trapping. _Advanced Optical Materials_ 2021, 2100758
  * Lin et al. 2018 Lin, L.; Wang, M.; Peng, X.; Lissek, E. N.; Mao, Z.; Scarabelli, L.; Adkins, E.; Coskun, S.; Unalan, H. E.; Korgel, B. A., et al. Opto-thermoelectric nanotweezers. _Nature photonics_ 2018, _12_ , 195–201
  * Liu et al. 2018 Liu, Y.; Lin, L.; Bangalore Rajeeva, B.; Jarrett, J. W.; Li, X.; Peng, X.; Kollipara, P.; Yao, K.; Akinwande, D.; Dunn, A. K., et al. Nanoradiator-mediated deterministic opto-thermoelectric manipulation. _ACS nano_ 2018, _12_ , 10383–10392
  * Yoo et al. 2018 Yoo, D.; Gurunatha, K. L.; Choi, H.-K.; Mohr, D. A.; Ertsgaard, C. T.; Gordon, R.; Oh, S.-H. Low-power optical trapping of nanoparticles and proteins with resonant coaxial nanoaperture using 10 nm gap. _Nano letters_ 2018, _18_ , 3637–3642
  * Sharma et al. 2020 Sharma, V.; Paul, D.; Chaubey, S. K.; Tiwari, S.; Kumar, G. V. P. Large-scale optothermal assembly of colloids mediated by a gold microplate. _Journal of Physics: Condensed Matter_ 2020, _32_ , 324002
  * Wang et al. 2011 Wang, K.; Schonbrun, E.; Steinvurzel, P.; Crozier, K. B. Trapping and rotating nanoparticles using a plasmonic nano-tweezer with an integrated heat sink. _Nature communications_ 2011, _2_ , 1–6
  * Jones et al. 2018 Jones, S.; Andren, D.; Karpinski, P.; Kall, M. Photothermal heating of plasmonic nanoantennas: Influence on trapped particle dynamics and colloid distribution. _ACS Photonics_ 2018, _5_ , 2878–2887
  * Ndukaife et al. 2016 Ndukaife, J. C.; Kildishev, A. V.; Nnanna, A. G. A.; Shalaev, V. M.; Wereley, S. T.; Boltasseva, A. Long-range and rapid transport of individual nano-objects by a hybrid electrothermoplasmonic nanotweezer. _Nature nanotechnology_ 2016, _11_ , 53–59
  * Hong et al. 2020 Hong, C.; Yang, S.; Ndukaife, J. C. Stand-off trapping and manipulation of sub-10 nm objects and biomolecules using opto-thermo-electrohydrodynamic tweezers. _Nature Nanotechnology_ 2020, _15_ , 908–913
  * Braun et al. 2015 Braun, M.; Bregulla, A. P.; Günther, K.; Mertig, M.; Cichos, F. Single molecules trapped by dynamic inhomogeneous temperature fields. _Nano letters_ 2015, _15_ , 5499–5505
  * Baffou 2017 Baffou, G. _Thermoplasmonics: Heating Metal Nanoparticles Using Light_ ; Cambridge University Press, 2017
  * Baffou et al. 2020 Baffou, G.; Cichos, F.; Quidant, R. Applications and challenges of thermoplasmonics. _Nature Materials_ 2020, _19_ , 946–958
  * Setoura et al. 2017 Setoura, K.; Ito, S.; Miyasaka, H. Stationary bubble formation and Marangoni convection induced by CW laser heating of a single gold nanoparticle. _Nanoscale_ 2017, _9_ , 719–730
  * Chikazawa et al. 2019 Chikazawa, J.-i.; Uwada, T.; Furube, A.; Hashimoto, S. Flow-induced transport via optical heating of a single gold nanoparticle. _The Journal of Physical Chemistry C_ 2019, _123_ , 4512–4522
  * Le Ru et al. 2006 Le Ru, E. C.; Meyer, M.; Etchegoin, P. G. Proof of single-molecule sensitivity in surface enhanced Raman scattering (SERS) by means of a two-analyte technique. _The journal of physical chemistry B_ 2006, _110_ , 1944–1948
  * Etchegoin et al. 2007 Etchegoin, P. G.; Meyer, M.; Blackie, E.; Le Ru, E. C. Statistics of single-molecule surface enhanced Raman scattering signals: Fluctuation analysis with multiple analyte techniques. _Analytical chemistry_ 2007, _79_ , 8411–8415
  * Johnson and Christy 1972 Johnson, P. B.; Christy, R.-W. Optical constants of the noble metals. _Physical review B_ 1972, _6_ , 4370
  * Gomez-Grana et al. 2012 Gomez-Grana, S.; Hubert, F.; Testard, F.; Guerrero-Martínez, A.; Grillo, I.; Liz-Marzán, L. M.; Spalla, O. Surfactant (bi) layers on gold nanorods. _Langmuir_ 2012, _28_ , 1453–1459
  * Reichl et al. 2014 Reichl, M.; Herzog, M.; Götz, A.; Braun, D. Why charged molecules move across a temperature gradient: the role of electric fields. _Physical review letters_ 2014, _112_ , 198101
  * Lee and Meisel 1982 Lee, P.; Meisel, D. Adsorption and surface-enhanced Raman of dyes on silver and gold sols. _The Journal of Physical Chemistry_ 1982, _86_ , 3391–3395
  * Meyer et al. 2006 Meyer, M.; Le Ru, E.; Etchegoin, P. Self-limiting aggregation leads to long-lived metastable clusters in colloidal solutions. _The Journal of Physical Chemistry B_ 2006, _110_ , 6040–6047
  * Otto et al. 2003 Otto, A.; Bruckbauer, A.; Chen, Y. On the chloride activation in SERS and single molecule SERS. _Journal of molecular structure_ 2003, _661_ , 501–514
  * Grochala et al. 1998 Grochala, W.; Kudelski, A.; Bukowska, J. Anion‐induced charge‐transfer enhancement in SERS and SERRS spectra of Rhodamine 6G on a silver electrode: how important is it? _Journal of Raman Spectroscopy_ 1998, _29_ , 681–685
  * Patra and Kumar 2013 Patra, P. P.; Kumar, G. V. P. Single-molecule surface-enhanced Raman scattering sensitivity of Ag-core Au-shell nanoparticles: revealed by bi-analyte method. _The journal of physical chemistry letters_ 2013, _4_ , 1167–1171

Supplementary Material
Single Molecule SERS in a Single Gold Nanoparticle-driven Thermoplasmonic
Tweezer
Sunny Tiwari,1,∗ Utkarsh Khandelwal,1 Vandana Sharma1 and G. V. Pavan Kumar1,∗

1Department of Physics, Indian Institute of Science Education and Research,
Pune-411008, India
∗Email<EMAIL_ADDRESS><EMAIL_ADDRESS>
###### Contents

  1. S1
    1. S1.1 S1: Thermoplasmonic tweezer coupled with SERS setup
    2. S1.2 S2: Detailed mechanism of reversible trapping
    3. S1.3 S3: Scattering and absorption cross-sections of nanoparticle on glass substrate and surrounded by water
    4. S1.4 S4: Trapping experiments performed with a 532 nm laser
    5. S1.5 S5: Details on FEM based simulations
    6. S1.6 S6: Calculated temperature and temperature gradient using a 1.2 NA water immersion lens
    7. S1.7 S7: Details on preparation of Lee-Meisel silver nanoparticles
    8. S1.8 S8: Bi-analyte SERS without adding KCl in the solution
    9. S1.9 S9: Bi-analyte SERS away from the assembly
    10. S1.10 S10: Bi-analyte SERS using 532 nm laser
    11. S1.11 S11: Details on modified principal component analysis
    12. S1.12 S12: Details on Supporting Movies
  2. S2 S1: Thermoplasmonic tweezer coupled with SERS setup
  3. S3 S2: Detailed mechanism of reversible trapping
  4. S4 S3: Scattering and absorption cross-sections of nanoparticle on glass substrate and surrounded by water
  5. S5 S4: Trapping experiments performed with a 532 nm laser
    1. S5.1 S4. (i) Calculated temperature and temperature gradient upon illuminating the nanoparticle
    2. S5.2 S4. (ii) Trapping of 250 nm AuNPs using 532 nm laser
    3. S5.3 S4. (iii) Effect of CTAC concentration and input power density on the trapping
  6. S6 S5: Details on FEM based simulations
  7. S7 S6: Calculated temperature and temperature gradient using a 1.2 NA water immersion lens
  8. S8 S7: Details on preparation of Lee-Meisel silver nanoparticles
  9. S9 S8: Bi-analyte SERS without adding KCl in the solution
  10. S10 S9: Bi-analyte SERS away from the assembly
  11. S11 S10: Bi-analyte SERS using 532 nm laser
  12. S12 S11: Details on modified principal component analysis
  13. S13 S12: Details on Supporting Movies

## Appendix S1

###  S1.1 S1: Thermoplasmonic tweezer coupled with SERS setup

###  S1.2 S2: Detailed mechanism of reversible trapping

###  S1.3 S3: Scattering and absorption cross-sections of nanoparticle on
glass substrate and surrounded by water

###  S1.4 S4: Trapping experiments performed with a 532 nm laser

###  S1.5 S5: Details on FEM based simulations

###  S1.6 S6: Calculated temperature and temperature gradient using a 1.2 NA
water immersion lens

###  S1.7 S7: Details on preparation of Lee-Meisel silver nanoparticles

###  S1.8 S8: Bi-analyte SERS without adding KCl in the solution

###  S1.9 S9: Bi-analyte SERS away from the assembly

###  S1.10 S10: Bi-analyte SERS using 532 nm laser

###  S1.11 S11: Details on modified principal component analysis

###  S1.12 S12: Details on Supporting Movies

##  Appendix S2 S1: Thermoplasmonic tweezer coupled with SERS setup

Figure S1: Schematic representation of the setup.

The sample was illuminated using a 50x, 0.50 NA air objective lens for
trapping and 60x, 1.2 NA water immersion objective lens for simultaneous
trapping and SERS. The backscattered light was collected using the same
lenses. The laser light was expanded using a set of two lenses L1 and L2. M1
and M3 are mirrors and M2 is a flip mirror to choose either of the lasers. BS1
and BS2 are beam splitters to simultaneously excite the sample with laser and
its visualization using white light for bright field imaging. Lens L3 is used
to loosely focus white light on the sample plane. Notch filter is used to
reject the elastically scattered light for SERS spectroscopy and dark field
and bright field imaging. Lenses L4 and L5 are used to focus the emission onto
the spectrometer or CCD. M4 is a flip mirror to route light for the imaging or
the SERS. For dark field imaging the sample was excited with a dark field
condenser from the bottom and the scattered light was collected from the top
objective lens. A spacer of height 120 $\mu$m was used while performing
trapping experiments and no spacer was used while performing simultaneous
trapping and SERS.

##  Appendix S3 S2: Detailed mechanism of reversible trapping

Figure S2: Detailed mechanism of the reversible trapping using single
nanoparticle driven thermoplasmonic trap.

Figure S2 shows the mechanism of the reversible trapping. 150 nm gold
nanoparticles in ethanol solution were dropcasted on the glass substrate and
were left to dry. The AuNPs were surrounded with an aqueous solution
containing plasmonic particles, and CTAC molecules at a concentration greater
than critical micelle concentration. The plasmonic particles suspended in the
solution and the anchor metal particle get coated with a CTAC double layer
which is positive in charge73. The solution also contains negatively charged
chlorine ions. A distribution of the ions is shown in figure S2(a). As the
anchor particle is illuminated with the laser source, the temperature of the
particle increases. This also creates a temperature gradient near the anchor
metal particle. This temperature gradient leads to the thermophoretic motion
of the charged ions in the solution74. This causes a redistribution of Cl-
ions and CTAC micelles, and CTAC double layer coated plasmonic particles. The
thermoelectric force ET between the Cl- ions present near the anchor particle
and the positively charged CTAC micelles forces the plasmonic particles to
move towards the anchor particle. An interaction between the CTAC coated
anchor particle and CTAC micelle creates a repulsive force Er which balances
the ET and the plasmonic particles get trapped near the anchor particle. After
the laser is turned off the repulsive force in addition with the Brownian
motion of the plasmonic particles causes the particles to move away from the
anchor particle.

##  Appendix S4 S3: Scattering and absorption cross-sections of nanoparticle
on glass substrate and surrounded by water

Figure S3: Scattering and absorption cross-sections of a 150 nm AuNP placed on
a glass substrate and surrounded by water. (a) Experimentally measured
scattering spectrum of the AuNP in water and placed on a glass substrate. (b)
Calculated scattering spectrum of a 150 nm AuNP in a material of refractive
index 1.365. (c) Calculated absorption of a 150 nm AuNP in a material of
refractive index 1.365.

Figure S3 shows the scattering and absorption cross-sections of a 150 nm
diameter nanoparticle. The scattering spectrum was experimentally measured
using slant angle excitation and the scattered light was collected using a low
numerical aperture objective lens. To experimentally calculate the absorption
cross-section, the peak of the calculated scattering spectrum was recorded by
tuning the refractive of the surrounding medium in which the gold particle was
suspended. Refractive index of the medium was chosen by taking the effective
refractive index of the medium as the particle was placed on a glass substrate
and was surrounded by water68. The peak position of experimentally measured
and numerically calculated scattering spectrum matches at an effective
refractive index of 1.365. Finally, at a refractive index of 1.365 of the
medium the absorption cross-section was calculated and is shown in figure S3
(c).

##  Appendix S5 S4: Trapping experiments performed with a 532 nm laser

###  S5.1 S4. (i) Calculated temperature and temperature gradient upon
illuminating the nanoparticle

Figure S4: Calculated temperature and temperature gradient upon illumination
of a single nanoparticle with a 532 nm laser at a power density of 0.07
mW/$\mu$m2. (a) Temperature increment of the nanoparticle. (b) Calculated
temperature gradient ($\nabla$T) in the x-y plane and near the surface of the
nanoparticle. (c) Calculated temperature gradient ($\nabla$T ) in the z plane.

###  S5.2 S4. (ii) Trapping of 250 nm AuNPs using 532 nm laser

Movie S9 shows the reversible trapping of 250 nm AuNPs using the single
nanoparticle driven thermoplasmonic trap. A single 150 nm diameter was placed
on a glass substrate and was surrounded by water, added with CTAC molecules.
The solution was placed inside a chamber of height 120 $\mu$m. The final
concentration of CTAC in the solution was 15 mM. The particle was illuminated
by focusing a 0.55 mW laser of wavelength 532 nm at a 3.17 $\mu$M spot size by
using a 50x, 0.50 NA objective lens.

###  S5.3 S4. (iii) Effect of CTAC concentration and input power density on
the trapping

Figure S5: Effect of input power density and CTAC concentration on trapping.

For a constant CTAC concentration of 12 mM, a week trapping was observed at an
input power of 0.07 mW$\mu$m2 but after decreasing the power density to 0.02
mW/$\mu$m2 no trapping was observed. After increasing the CTAC concentration
to 15 mM a fast and strong trapping is observed at an input power density of
0.07 mW/$\mu$m2. A week trapping was also observed at an input power density
of 0.02 mW/$\mu$m2. After forming the assembly at a power of 0.07 mW/$\mu$m2,
the assembly was stable by reducing the power density to 0.02 mW/$\mu$m2 and
also to 0.016 mW/$\mu$m2. A further decrease in the power leads to
disassembly. After further increasing the CTAC concentration, no observable
changes in the trapping was observed.

##  Appendix S6 S5: Details on FEM based simulations

The heat power density $q(r,t)$at any time $t$ and at any location $r$ inside
an illuminated structure is given by66

$q(\mathbf{r},t)=\mathbf{J}(\mathbf{r},t)\cdot\mathbf{E}(\mathbf{r},t)$ (S1)

where $J$ is the electronic current density and $E$ is the electric field
inside the nanostructure.

Taking the time average, and denoting the frequency of the light source
illuminating the structure as $\omega$, the heat power density becomes,

$q(\mathbf{r})=\left(\frac{\omega}{2}\right)\varepsilon_{0}\operatorname{Im}(\varepsilon)|\mathbf{E}(\mathbf{r})|^{2}$
(S2)

Calculation for electric field intensity inside the metal structures and thus
temperature increment and temperature gradient upon illumination were
performed using finite element method based study using COMSOL Multiphysics as
solver.

Electromagnetic power loss density option was selected to account for the
heating source. Boundary conditions were defined using the equation

$q_{0}=h\left(T_{ext}-T\right)$ (S3)

with $T_{ext}$ = 293.15 K and $h$ is the heat transfer coefficient, with
external natural convection option for a spherical geometry of diameter 150
nm. The particle was excited with a focused Gaussian beam with values of power
and beam waist size were put in, by measuring the experimental power at the
sample plane and beam spot size. The refractive index of the metal particle
was taken from ref72.

##  Appendix S7 S6: Calculated temperature and temperature gradient using a
1.2 NA water immersion lens

Figure S6: Calculated temperature and temperature gradient upon illumination
of a single nanoparticle with a 633 nm wavelength laser at a power density of
0.14 mW/$\mu$m2. (a) Temperature increment of the nanoparticle. (b) Calculated
temperature gradient $\nabla$T in the x-y plane and near the surface of the
nanoparticle. (c) Calculated temperature gradient $\nabla$T in the z plane.

##  Appendix S8 S7: Details on preparation of Lee-Meisel silver nanoparticles

Figure S7: UV-Vis absorption spectra of Lee-Meisel silver nanoparticles.

180 mg of AgNO3 was dissolved in 1L of mQ water to prepare a 1.04 mM solution.
A 10 ml solution of 1$\%$ of Na3C6H5O7 was added while boiling, which serves
both as reducing and stabilizing agent. The solution was boiled for one hour
and then brought to the room temperature. A gray color solution was obtained
which confirms the preparation of silver nanoparticles. The UV-Vis spectrum of
the solution is shown in figure S7 and the peak position matches well the
reported values7. A detailed synthesis process can be found in ref75.

##  Appendix S9 S8: Bi-analyte SERS without adding KCl in the solution

Figure S8: Bi-analyte SERS from assembly of trapped Lee-Meisel AgNPs without
adding KCl in the solution. The final concentration of NB and Rh6G molecules
in the solution was 5 nM. The exposure time for each spectrum was 5 s. The
spectra show two peaks ‘a’ and ‘b’, where ‘a’ is 592 cm-1 peak of NB and ‘b’
is 612 cm-1 peak of Rh6G. The SERS enhancement was very low as compared to the
enhancement obtained from trapped Lee-Meisel AgNPs with added KCl.

##  Appendix S10 S9: Bi-analyte SERS away from the assembly

Figure S9: Bi-analyte SERS away from the assembly of Lee-Meisel AgNPs created
using a single nanoparticle driven thermoplasmonic trap. The concentration of
NB and Rh6G molecules in the final solution was 1 nM. The exposure time for
each spectrum was 5 s. The appearance of molecular SERS intensity as a
function of time is shown. The peaks of both molecular emission appears less
frequently as compared to peaks obtained from the assembly because the number
of hotspot away from the assembly was lower as compared to the hotspots
present in the assembly.

##  Appendix S11 S10: Bi-analyte SERS using 532 nm laser

Figure S10: Bi-analyte SERS spectra of bi-analyte solution prepared using 5 nM
Rh6G and NB molecules from assembly of Lee-Meisel AgNPs formed using single
nanoparticle driven thermoplasmonic trap. The anchor particle was excited with
a 532 nm laser for trap and the SERS spectroscopy was performed using the same
laser source. The CTAC concentration in the final solution was 10 mM. The
exposure time for each spectrum was 0.3 s and the dwell time between two
consecutive spectrums was 1 s. A total of 2000 spectra were taken. The final
concentration of KCl salt is 2 mM in the solution and prepared Lee-Meisel
AgNPs were diluted by 20 times. The spectra show sharp Rh6G peak but the peak
intensity of NB molecules was not good which is generally required for SM-SERS
studies.

##  Appendix S12 S11: Details on modified principal component analysis

  * •

To statistically confirmed the SM-SERS we used MPCA which is developed by P G
Etchegoin et al. and can be found in ref71.

  * •

Collected 4000 SERS spectra with 50 wavelengths were arranged in a rectangular
matrix (M) of T rows and N columns (4000×50).

  * •

After this for each row of the matrix mean intensity was subtracted which
gives a zero mean intensity matrix (M’).

  * •

For the matrix M’ a covariance matrix V of dimension V×V is calculated, from
which N Eigenvalues and corresponding N Eigenvectors are calculated and are
plotted in figure 4a. In the case of studying SM-SERS using bi-analyte
technique, the first two Eigenvalues are principal components.

  * •

A coefficient matrix C was obtained by matrix operation equivalent to the
scalar product of spectra with the first two Eigenvectors.

  * •

The matrix C was plotted in the coefficient space (shown in figure 4b) and
each point in the scatterer plot is an event, with pure events from only one
type of molecule is shown along two axes drawn as NB axis and Rh6G axis.

  * •

To obtain only the positive coefficient that represents the spectra of the
molecule a linear transformation was applied to reorient the axis obtained in
the scatterer plot in coefficient space.

  * •

Finally, a histogram is plotted after removing the noisy events from the
Figure 4c. The histogram shows the contribution of events from one type of
molecules in the total number of spectra. The contribution is plotted in terms
of probability of the occurrence of peak of only one type of molecules (as
shown in figure 4d).

##  Appendix S13 S12: Details on Supporting Movies

  1. 1.

Supporting Movie 1: Reversible trapping of 250 nm AuNPs using 150 nm AuNP as
anchor trap with 12 mM CTAC solution. The power density used was
0.08mW/$\mu$m2.

  2. 2.

Supporting Movie 2: No trapping observed in the absence of CTAC with 150 nm
AuNP as anchor trap and 250 nm AuNPs in the solution. The power density used
was 0.08mW/$\mu$m2.

  3. 3.

Supporting Movie 3: Reversible trapping of 400 nm AuNPs using 150 nm AuNP as
anchor trap with 12 mM CTAC solution. The power density used was
0.08mW/$\mu$m2.

  4. 4.

Supporting Movie 4: Reversible trapping of Lee-Meisel AgNPs using 150 nm AuNP
as anchor trap with 2 mM CTAC solution. The power density used was
0.14mW/$\mu$m2.

  5. 5.

Supporting Movie 5: Release of Lee-Meisel AgNPs assembly.

  6. 6.

Supporting Movie 6: Working of tweezer driven by anchor AuNP using 150 nm AuNP
as anchor trap and 250 nm AuNPs in the solution with 12 mM CTAC solution. The
power density used was 0.08mW/$\mu$m2.

  7. 7.

Supporting Movie 7: Working of tweezer driven without anchor AuNP with 250 nm
AuNPs in the solution and 12 mM CTAC solution. The power density used was
0.08mW/$\mu$m2.

  8. 8.

Supporting Movie 8: Trapping of single 250 nm AuNP using 150 nm AuNP as anchor
trap with 12 mM CTAC solution. The power density used was 0.08mW/$\mu$m2.

  9. 9.

Supporting Movie 9: Reversible trapping of 250 nm AuNPs using 150 nm AuNP as
anchor trap with 15 mM CTAC solution using a 532 nm laser. The power density
used was 0.07mW/$\mu$m2.

  10. 10.

Supporting Movie 10: Unprocessed video of trapping and release of Lee-Meisel
AgNPs using 150 nm AuNP as anchor trap with 2 mM CTAC solution. The power
density used was 0.14mW/$\mu$m2.