#  Autonomous Hyperspectral Characterisation Station:
Robotically Assisted Characterisation of Polymer Degradation

Shayan Azizi1,2, Ehsan Asadi1, Shaun Howard2, Benjamin W. Muir2, Riley O’Shea2
and Alireza Bab-Hadiashar1 1Shayan Azizi, Ehsan Asadi and Alireza Bab-
Hadiasher are with Faculty of Manufacturing, Materials and Mechatronics
Engineering, RMIT University, VIC 3000, Australia (email:
<EMAIL_ADDRESS><EMAIL_ADDRESS>alireza.bab-
hadiashar@rmit.edu.au)2Shayan Azizi, Shaun Howard, Benjamin W. Muir and Riley
O’Shea are with the Manufacturing business unit at CSIRO, VIC 3168, Australia
(email<EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS>
###### Abstract

This paper addresses the gap between the capabilities and utilisation of
robotics and automation in laboratory settings and builds upon the concept of
Self Driving Labs (SDL). We introduce an innovative approach to the temporal
characterisation of materials. The article discusses the challenges posed by
manual methods involving established laboratory equipment and presents an
automated hyperspectral characterisation station. This station integrates
robot-aided hyperspectral imaging, complex material characterisation modeling,
and automated data analysis, offering a non-destructive and comprehensive
approach. This work explains how the proposed assembly can automatically
measure the half-life of biodegradable polymers with higher throughput and
accuracy than manual methods. The investigation explores the effect of pH,
number of average molecular weight (Mn), end groups, and blends on the
degradation rate of polylactic acid (PLA). The contributions of the paper lie
in introducing an adaptable classification station for novel characterisation
methods and presenting an innovative methodology for polymer degradation rate
measurement. The proposed system has the potential to accelerate the
development of high-throughput screening and characterisation methods in
material and chemistry laboratories.

###### Note to Practitioners

The characterisation and classification of materials hold significant
importance within the realms of material science, chemistry, manufacturing and
circular economy. We introduce an innovative approach by employing robotics to
automate hyperspectral imaging and processing to accelerate and improve
material characterisation within a laboratory environment. This methodology
facilitates the time-dependent characterisation of materials. This automated
system encompasses sample manipulation and handling for hyperspectral
scanning, accompanied by automated image and data processing procedures to
minimise manual interventions effectively. The developed system can be
seamlessly integrated into lab settings, offering objective, accurate, and
automated measurement of biodegradable polymer degradation rate.

###### Index Terms:

Hyperspectral imaging, laboratory automation, polymer degradation, material
characterisation.

##  I INTRODUCTION

Figure 1: Robot-aided hyperspectral mapping and data sampling.

Efforts have been directed towards bridging the gap between capabilities and
utilisation of robotics and automation within a laboratory setting. With the
introduction of Self Driving Labs (SDL) [1] such as the mobile chemist [2],
automation and AI are expected to significantly impact laboratory operations,
enabling mechanised large-scale experimentation with high levels of
repeatability and throughput while reducing operational costs [3]. Automation
can greatly enhance material classification and characterisation which are
essential tasks within scientific laboratories. Specifically, there has been a
growing interest in the use and applications of biodegradable polymers,
including but not limited to packaging, electronics, automotive components,
and critical medical applications such as implantation and drug delivery
[4][5]. Consequently, automation of degradation rate measurement and analysis
is becoming increasingly important, necessitating objective data sampling
throughout days, nights and weekends for an extended period of time.

###  I-A Problem, solution and rationale

Numerous chemical and material science experiments involve reactions or
processes that happen gradually, yielding crucial data at specific time
points. Prolonged experiments that require regular measurements impose
significant demands on laboratory staff and equipment usage. Moreover, many
experiments prove sensitive to intrusive measurement techniques entailing
resampling or extreme exposure. Established equipment for characterisation,
including mass spectrometry, Fourier-transform infrared spectroscopy (FTIR),
nuclear magnetic resonance (NMR), UV-vis spectroscopy, gas permeation
chromatography (GPC), etc., are frequently operated manually, requiring
laborious sample preparation. They often have prolonged operation times and
are associated with high operational and setup costs. These characteristics
limit their seamless integration into automated workflows.

Current techniques for measuring the degradation rate of biodegradable
polymers encompass a range of methods, including NMR, FTIR, GPC,
thermogravimetric analysis (TGA), (SEM) and turbidity measurements [6]. For
instance, GPC quantifies alterations in the hydrodynamic radius associated
with molecular weight. By evaluating changes in molecular weight, insights
into the polymer degradation rate can be derived. However, GPC involves many
preparatory manual steps as well as high levels of uncertainty associated with
its measurements. Alternatively, a turbidity scanner can measure the intensity
of scattered light by the sample, which may decrease as the polymer degrades.
Turbidity measurements are based on assumptions that the polymer is
homogeneous within the solution and the light beam wavelength is appropriate
for all samples, which is usually not the case. These standard analytical
techniques exhibit limitations in assessing degradation rates and often
involve multiple manual procedures with low throughput.

This paper presents an automated hyperspectral characterisation station for
high-throughput material analysis without requiring any preparatory steps or
deep domain knowledge. This method allows flexibility and optimised automation
via integrating robot-aided hyperspectral imaging with complex material
characterisation modelling and automated data analysis. The compact and non-
destructive nature of the proposed method allows synergistic deployment with
various classification tools, thereby providing a comprehensive analysis.
Through a detailed case study, we show how the proposed assembly can
automatically measure the half-life of bio-degradable polymers with higher
throughput and accuracy compared to what is typically achieved with manual
methods.

###  I-B Contributions and capabilities

This paper has two major contributions: (I) We introduce an adaptable and
automated classification station that enables the development of novel
characterisation methods involving slow temporal changes by providing readily
analysable hyperspectral data over an extended period of time. Applications of
this system encompass tracking changes within samples over time, conducting
comparative analyses across multiple samples, time-dependent characterisation,
and non-intrusive monitoring. The hyperspectral data provides a range of
information, including variations in total reflectance, alterations in peak
and trough intensities, quantitative colorimetric assessment, and
identification of anomalies in established spectral patterns. This information
is useful to detect the presence or absence of specific materials as well as
to measure solubility and degradation, light sensitivity, uniformity,
concentrations, etc. This framework provides opportunities for the development
of innovative, rapid, automated, and high-throughput screening and
characterisation methods within material and chemistry laboratories. The use
of the proposed system has the potential to accelerate the generation of
training data for machine learning methods that can solve characterisation
challenges. (II) An innovative methodology is developed for polymer
degradation rate measurement as an objective, accurate and automated solution
with minimum human intervention. This technique provides insights into the
degradation of Polylactic Acid (PLA). The impact of variables such as pH,
molecular weight, end groups and blends of polymers on the rate of polymer
degradation is investigated.

##  II Related work

Laboratory automation is increasingly necessary to speed up chemical analysis
processes and reduce the required human resources [1][3]. Recent work are
focused on using collaborative robots (cobots), which are an emerging
technology that brings flexibility to labs without compromising scientists’
safety. As an example, “the mobile robotic chemist” [2] is designed to perform
complete experiments and improve future experiments by analysing the results
of previous runs. Such closed-loop automated processes are intended to
increase the speed of laboratory processes. A less sophisticated application
of cobots in labs is their use in Covid-19 testing facilities [7], where a
sample cleaning procedure is automated, saving lab technicians time and effort
from performing tedious and repetitive tasks. Another example is a robotic
system performing the Michael reaction experiment competently as a junior
chemist [8]. To add flexibility, a system architecture called the ARChemist
[9] was developed to facilitate laboratory automation using existing
equipment. Similarly, a scene-aware robotic chemist with the ability to plan
long horizon trajectories and perform pouring exercises was developed to
perform basic solubility and crystallisation experiments [10].

Robots are also increasingly employed to automate various aspects of
hyperspectral analysis across different domains. As an example, robots and HSI
were used to design a construction demolition waste system [11] to separate
common waste materials such as brick, rubber, concrete, and wood using a
robotic arm. For outdoor applications, an automated hyperspectral imaging
system, in conjunction with a deep learning technique, was also developed to
predict the nitrogen contents in corn plant leaves [12]. However, automated
systems have turned out to have a lower time-averaged throughput compared to
humans. Most recently, a system known as Hyperbot-A [13] has been developed to
automate the process of hyperspectral imaging data gathering with the help of
a cobot. However, hyperbot-A’s integration with external equipment remains
unexplored and hasn’t been utilised for specific applications.

In terms of data analysis, there is significant potential to improve
scientific measurements by using artificial intelligence, particularly those
focused on hyperspectral imaging (HSI) [14]. For instance, a study conducted
in a laboratory setting utilises HSI to predict the mean size of polymer
molecules [15], which had an observed and predicted value correlation (R2) of
0.97. Similarly, by using machine learning and hyperspectral images, the
possibility of measuring the conductivity of thin polymer films was explored
[16]. Although conductivity and reflection spectrum may initially seem
unrelated, the combination of HSI and ML served as a diagnostic tool to
measure film conductivity. While this work demonstrated the potential of
hyperspectral imaging as a characterisation technique, the data-gathering
process was conducted manually.

Figure 2: Overall system design of autonomous hyperspectral characterisation
station for laboratories.

Over the past few years, some work has also investigated polymer degradation
and aging using hyperspectral imaging. Polylactic acid degradation was
evaluated in an accelerated weathering test using a NIR hyperspectral camera,
which used spectral intensity to suggest a decrease in molecular weight
induced by the hydrolysis degradation [17]. Another study of polymer
degradation [18] found that the HSI method provides a more detailed picture of
sample degradation in terms of its spatial variance. Polymer damage and aging
were also analysed for silicone rubbers while mainly utilising the visible
spectrum [19]. Most recently, the aging state of styrene–butadiene–styrene
(SBS) has been analysed using a Short Wave Infrared HSI, predicting the aging
state with an error of 1.8-2.1 days [20]. None of the mentioned work examines
polymer degradation in a chemical lab that allows fine-tuning of different
variables. Our proposed methodology is a generalised approach to
characterising and quantifying the degradation rate of various polymers due to
hydrolysis. It provides a methodology for analysing a larger set of
applications than scenario-specific methods and measurements.

##  III Automated Hyperspectral Characterisation Station

This section provides an overview of the innovative characterisation station
proposed as a key element of process automation within chemistry laboratories,
enabling outcomes with objective, data-driven and quantifiable results.
Enabled by the capabilities of the proposed system, in section IV, we
introduce a new and fully automated approach for measuring and analysing
polymer degradation rates. The automation station is designed around multiple
factors such as safety, flexibility, minimising human intervention and
maximising throughput. The system consists of two primary processes: the
characterisation station and the image and data analysis pipeline. The station
is comprised of five principal components: a manipulator (collaborative
robot), an end-effector (gripper), an imaging device (hyperspectral camera),
illumination sources (halogen lamps), and lab equipment (shaker). These
components are integrated through the Robotic Operating System (ROS) as the
communication framework. The data and imaging analysis pipeline comprises five
key sub-processes: calibration, region of interest (ROI) detection, artefact
removal, model fitting, and data visualisation.

###  III-A Process automation and hierarchical state machine

In our setup, we developed a series of independent software modules that each
controlled a physical device. The user can arrange the modules in any sequence
to define the operations required for a task. The resulting sequential state
machine is called a “program” and can be saved into a library for future use.
Combining several programs and a user input module comprises a hierarchical
state machine that enables the user to define a sequence of controlled actions
as shown in Fig. 2. “Program” number refers to the labelling associated with
individual subtasks, and “sequential sampling variables” serves as the
starting point for the sampling strategy, determining the number of iterations
a program runs and specifying the time interval between each iteration.. Fig.
5 shows an example program consisting of modular components for the automation
of an imaging task. Each program performs a specific task via its own state
machine, and the human-machine interface allows for serialised commands,
orchestrating multiple programs. Such tasks include running experiments in
parallel or performing more complex characterisation of high-level tasks.

###  III-B Image processing and data analysis

Hyperspectral images are dense and high-dimensional data structures that
require the implementation of various image and data processing techniques to
enable meaningful analysis. Several techniques are employed in our system,
including flat field correction, artefact removal and ROI detection. A brief
overview of these techniques is as follows.

Calibration: The well-known flat-field correction was employed to account for
the influence of lighting on the samples. A highly reflective (99%) tile was
utilised to measure the light source’s spectrum (W). The background noise
(dark) spectrum (D) was recorded with the shutter closed. Subsequently, the
hyperspectral data is calibrated using $C=\frac{R-D}{W-D}\cdot m$, where $R$
and $C$ are the raw calibrated data, respectively and $m$ is a constant
scaling factor.

ROI detection: Numerous containers commonly found in laboratory settings,
including well plates, petri dishes, and vials, exhibit circular geometries.
OpenCV Hough’s circle detection algorithm [21] with additional developed
optimisations and fine-tuning were utilised to automate the identification of
regions of interest (ROIs) [22][23]. The algorithm guarantees non-overlapping
detection of circles and sequential numbering, ensuring the convenient
identification of each individual sample. Particular focus was placed on the
detection of well plates as it allows for concurrent imaging of multiple
samples.

Artefact removal: Two primary artefacts are encountered in the detected
regions of interest. Firstly, there is glare on the surface of the solution,
which is considerably more reflective than the solution, and secondly, there
is a possibility of regions being erroneously flagged as part of the sample
post-circle detection. To address both concerns, a reflective mask is produced
via 3D printing and fitted on top of the well plate and around the samples.
This mask reflects considerably more light, sufficient to induce CMOS sensor
saturation. Algorithmically, pixels that experience sensor saturation at any
wavelength are excluded from the region of interest, thereby resolving both
artefacts and enhancing the overall outcomes.

Model fitting: A major challenge in transitioning from lab-scale chemistry to
process automation is the lack of quantitative chemical synthesis and
analysis. We utilise a model-fitting algorithm to match a particular model,
whether it’s a basic exponential function or a more complex one, with the data
obtained from hyperspectral imaging for various analyses. For example, our
novel approach to resolve this issue and calculate the polymer’s half-life
using only hyperspectral imaging is outlined in the subsequent section.

##  IV Automating Polymer Degradation Rate Measurement and Analysis

###  IV-A Biodegradable polymers and their characteristics

In this paper, polymer degradation refers to the breakdown of large polymers
into smaller units due to hydrolysis, as seen in Fig. 3. The underlying
mechanisms governing polymer degradation are complex and not fully understood
[24]. Existing research suggests two established approaches: bulk erosion and
surface erosion [25][26]. Numerous theoretical models have been proposed to
describe polymer degradation rates, adopting various approaches, including
diffusion and surface area dependence. This study uses a simple exponential
decay approach for assessing polymer degradation. The first-order rate
equation for the mass of undissolved polymers ($m_{p}$) is given by 1, where
$k_{1}$ is the decay constant.

$-\frac{dm_{p}}{dt}=k_{1}m_{p}.$ (1)

Solving the differential equation 1 results in :

$m_{p}(t)=ae^{-k_{1}t}.$ (2)

This study mainly focused on measuring polylactic acid (PLA) degradation. It’s
been shown that PLA degrades to lactic acid and its oligomers [27].
Additionally, Lactic acid and lactic acid salts are highly soluble in water
[28][29]. Hence, degraded PLA will fully dissolve in potassium buffer saline
(PBS) and not reflect any visible light. Under these conditions, potassium
lactate would be the major product formed at equilibrium. This is further
confirmed by an experiment measuring the light reflected from PBS + lactide,
PBS + lactic acid and the PBS on its own. As shown in Fig. 4, the lactide and
lactic acid do not contribute to a signal change. The slight difference in the
baseline value of the three samples is due to the position of the samples with
respect to the camera and light.

The proposed methodology applies to a variety of polymers as long as they
satisfy two conditions: (I) the polymer is relatively well dispersed within
the solution (this technique isn’t as accurate for visibly concentrated or
agglomerated polymers) and (II) the polymer dissolves into PBS (or other
solution) after degradation. An additional polymer independent condition (III)
also needs to be satisfied to ensure the mass of polymer per imaged-surface-
area ($\rho A$) doesn’t exceed a certain threshold ($\tau$).

Figure 3: A black and white representation of hyperspectral images, used to
measure the degradation rate of polymers. The degradation process is evident
as time (in hours) progresses from the left to the right image.

###  IV-B Hyperspectral imaging model for polymers degradation

Hyperspectral imaging provides measurements of the reflected light over a wide
range of wavelengths from biodegradable polymers, in contrast to conventional
RGB cameras or similar sensors that are restricted to certain wavelengths. The
half-life of polymers can be estimated by fitting an exponential decay model
to regular measurements of total reflected light over an extended period of
time using a hyperspectral camera. The total light reflected by a sample is
modelled as follows:

$R_{T}=R_{s}+R_{p}+R_{b},$ (3)

where $R_{T}$ is the total light reflected, $R_{s}$ is the light reflected by
PBS, $R_{p}$ is the light reflected by any undissolved polymer, and $R_{b}$ is
the light reflected by the background. Since the $R_{s}$ and $R_{b}$ remain
unchanged over time, as the polymer degrades, they can be replaced by a
constant $c$. Hence, we can rewrite Eq. 3 as:

$R_{T}=R_{p}+c$ (4)

As previously discussed, we assume that the polymer is relatively well
dispersed within the solution and its area density doesn’t exceed the
threshold $\tau$. This means that the light reflected by the polymer would
have a linear relationship with the cumulative amount of undissolved polymer.
Notably, the mass of undissolved polymers ($m_{p}$) maintains direct
proportionality with the intensity of reflected light, owing to the fact that
non-degraded polymers can’t dissolve within the solution. This is important as
only the undissolved polymers will contribute to the hyperspectral reflectance
signal. As such, we can reasonably expect a linear relationship between
reflection and undissolved mass given by:

$R_{T}=a\cdot m_{p}+c$ (5)

Figure 4: Total light reflected by the control samples. Lactide and Lactic
acid aren’t contributing to a signal change (plotted with the same range as
the actual results shown in subsequent figures). The standard deviation of
reflection intensity values for these controlled samples is less than 5% of
their average values for all cases.

where $a$ and $c$ are both constants. As given in Eq. 2, the mass of the
remaining polymers can be modelled using an exponential decay, resulting in
the following equation:

$R_{T}=a\cdot e^{-k_{d}t}+c$ (6)

from which the half-life ($t_{\frac{1}{2}}$) of the polymer can be calculated
by:

$t_{\frac{1}{2}}=\frac{\log(2)}{k_{d}}.$ (7)

###  IV-C Advanced surface-mass dependent degradation model

By assuming a spherical shape and constant density ($\rho$) for the polymers,
one can arrive at a surface area ($SA$) dependent rate of degradation as given
in Eq. 8, where $k_{d}$ is the rate constant [30].

$-\frac{dm_{p}}{dt}=k_{2}\cdot\rho\cdot
SA=k_{2}(4\pi\rho)^{\frac{1}{3}}(3m_{p})^{\frac{2}{3}}$ (8)

With the linear combination of the above equation and the mass-dependent model
given in Eq. 1, we derive a new degradation rate model (Eq. 9). This
incorporates both the surface and bulk modes of degradation into one equation.

$-\frac{dm_{p}}{dt}=k_{2}(4\pi\rho)^{\frac{1}{3}}(3m_{p})^{\frac{2}{3}}+k_{1}\cdot
m_{p}$ (9)

Denoting the constant term $k_{2}(4\pi\rho)^{\frac{1}{3}}(3)^{\frac{2}{3}}$ by
$b$, the differential equation 9, will be a Bernoulli ODE that can be solved
exactly with solution given by Eq. 10.

$m_{p}=-\frac{e^{-k_{1}t}(be^{\frac{k_{1}t}{3}}-e^{d_{1}k_{1}})^{3}}{k_{1}^{3}}$
(10)

By substituting the Eq. 10 in Eq. 5, we can now model the total reflected
light by:

$R_{T}=-\frac{ae^{-k_{1}t}(be^{\frac{k_{1}t}{3}}-e^{d_{1}k_{1}})^{3}}{k_{1}^{3}}+c$
(11)

where $a>0$, $b\geq 0$, $k_{1}\geq 0$ and $c\geq(\frac{b}{k})^{3}$. Variable
$a$ is just a scaling factor; for simplicity, we assume $a=1$. Akaike
information criterion (AIC) and Minimum Description Length (MDL) given in
equations 12 and 13 respectively [31], are used to evaluate the performance of
the fitted models.

$AIC=\sum_{i=1}^{N}r_{i}^{2}+2P\sigma^{2}$ (12)

$MDL=\sum_{i=1}^{N}r_{i}^{2}+\frac{P}{2}\log(N)\sigma^{2}$ (13)

###  IV-D Robot position for imaging

To calculate the appropriate distance ($d$) and velocity ($V_{c}$) of a sample
with respect to a fixed camera, we need to know the camera’s field of view
($FOV$), effective slit width ($L_{e}$), frame rate ($f$) and spatial
resolution ($R_{p}$) as well as the desired image length ($L$). The desired
image length in practice should be slightly larger than the sample width (e.g.
by $10\%$). Equations 14 and 15 show how a sample’s distance and velocity
values are calculated.

$d=\frac{L-L_{e}}{2}\cot(\frac{FOV^{\circ}}{2})$ (14)

$V_{c}=\frac{L}{R_{p}}\cdot f,$ (15)

###  IV-E Sequential sampling strategy with time-varying intervals

Algorithm 1 Sequential Sampling Strategy

1:All hyperspectral ROI ($D$), their corresponding time ($t$) and variables
$f$, $b_{l}$, $b_{u}$, $g$

2:$t_{n+1}$, optimal time separation for the next sampling

3:while sampling do

4: if new measurement then

5: $I\leftarrow F(D,t)$

6: end if

7: $i_{e}\leftarrow G(c,J)$ $\triangleright$ Where $J\subseteq I$

8: if ($i_{e}<\epsilon$) then

9: Stop Sampling

10: else if $|i_{e}-\Delta i|<\delta$ then

11: Continue without change

12: else

13: $d\leftarrow N(i_{e},\Delta i,g)$

14: $s\leftarrow T(d,f)$

15: $t_{n+1}\leftarrow P(\Delta t,s,b_{l},b_{u})$

16: end if

17:end while

One of the challenges of polymer degradation analysis lies in determining the
optimal time intervals for capturing hyperspectral images during the
degradation process. Employing a constant time interval is not effective, as
setting it too short may lead to inefficiencies within the system while opting
for a long interval can result in missing critical data. Moreover, the rate of
polymer degradation can vary throughout the course of an experiment, adding to
the complexity of the task. To address these challenges, we have developed a
sequential sampling algorithm, as shown in Alg. 1, designed for optimising the
time intervals between hyperspectral images. The process is as follows:

Firstly, each sample’s total reflected intensity ($I$) is extracted from the
ROI of hyperspectral images and recorded after each measurement. Next, a
subset of these intensities, usually the last two measurements and a desired
rate of change, denoted as $c$, are used to calculate the ideal change in
intensity ($i_{e}$). Any change smaller than a predefined degradation limit
($\epsilon$) is considered insignificant. If the ideal change in intensity is
smaller than the defined limit $\epsilon$ (e.g. $<1\%$ of the starting
intensity), it can be inferred that the sample won’t undergo any further
notable degradation, and the experiment can be stopped. Alternatively,
sampling can continue without adjusting if the difference between $i_{e}$ and
the last measured change in intensity ($\Delta i$) is within an acceptable
margin ($\delta$).

However, an optimisation algorithm is utilised if none of these conditions are
met. This algorithm calculates the optimal time for the next measurement
($t_{n+1}$) by considering several factors. First, it computes a normalised
distance ($d$) between the ideal and actual change in intensity, with the
option to increase sensitivity by applying a gain factor ($g$). A simple way
of calculating the normalised distance between $i_{e}$ and $\Delta i$ is given
in Eq. 16.

$d=g\cdot(\frac{\Delta i}{i_{e}}-2+\frac{i_{e}}{\Delta i})$ (16)

To find the appropriate change in sampling time ($s$), we first need to define
the maximum allowed factor of change per time interval $f$ (we usually don’t
want this to be more than a factor of two). We then introduce a smooth
monotone mapping of distances $d$ to $s$, using the $f$ factor as given by Eq.
17.

$s=(1-f)e^{-d}+f$ (17)

Finally, $t_{n+1}$ is determined based on the elapsed between the two previous
measurements $\Delta t$, $s$, and absolute lower ($b_{l}$) and upper ($b_{u}$)
time boundaries. These absolute boundaries are established based on system
limitations and the specific requirements of the experiment. If there are
multiple samples imaged, the final $t_{n+1}$ that the robotic system uses is
the smallest $t_{n+1}$ between all the given samples. The proposed Alg. 1
offers notable advantages, primarily stemming from its independence regarding
how intensities change over time and its low computational cost. These
characteristics make it a flexible and efficient tool for optimising the
timing of hyperspectral image capture during experiments.

##  V Experimental setup and results

###  V-A Apparatus and integration

A Universal Robotics UR5e collaborative robot was selected as the robotic arm
and was equipped with an OnRobot RG6 gripper. Custom detachable 3D-printed
fingers were implemented for specific applications in the hand.

The SPECIM FX10e push-broom hyperspectral camera serves as the primary
characterisation device. It provides a spatial resolution of 1024 pixels and
covers the wavelength range of 400 – 1000nm with a spectral resolution of 224.
The main light source for hyperspectral imaging is a 500W linear halogen lamp.

ROS1 Noetic on Ubuntu 20.04 and standard UR5e and RG6 gripper drivers were
utilised for system integration and communication. The robotic arm was
controlled by directly using its controllers and the Transform Library [32]
for path planning.

One of the main challenges in capturing hyperspectral data was ensuring no
information loss and maintaining 16-bit depth through multiple software
frameworks. The GigE interface of the FX10e limited the camera driver’s
choices. Aravis was the preferred driver option and was used as an element
within a Gstreamer pipeline, allowing the transport of data to be fine-tuned
and previewed.

OpenCV [21] was used to manage the Gstreamer pipeline and save the output to a
cloud server. It’s important to note that the above process was interfaced
with the experiment sequencer, allowing the automatic capture of hyperspectral
images in concert with the robot’s motion.

###  V-B Polymer degradation experimental procedure

An example of a sample preparation procedure we undertook is as follows:
Polymer (5 mg) was dissolved in 2 mL of 1,4-dioxane, 400 µL was taken and
added dropwise to a 15 mL centrifuge tube that contains 8 mL of a solution of
99% ethanol and 1% normal saline (0.9% NaCl in water). The mixture was allowed
to settle for at least 30 minutes, then it was centrifuged at 3500 RPM for 1
minute, and the supernatant was removed. The solid was then dispersed in 1.5
mL of PBS at 0.6M concentration and pH 12.3 (unless mentioned otherwise). This
suspension was then dispensed into a 24 well plate ($\rho A$ = 2.63
$mg/cm^{2}$). Through several experiments, we find that the mass of polymer
per imaged-surface-area ($\rho A$), 3 $>\rho A>$ 2 $mg/cm^{2}$ provides the
best results for our given polymers and experimental setup. The sample well
plate was then put on an orbital shaker, shaking at 30 RPM. The sequencer
program was launched, and the tasks defined in Fig. 5 were executed. The
defined process was repeated at specified time intervals for a defined number
of cycles, depending on the experiment’s requirements.

Figure 5: Schematic diagram of the imaging procedure. The lid manipulations
are crucial steps to minimise evaporation when idle and mitigate any potential
artefacts during the imaging process.

Table I contains information about different polymers used in our experiments.

TABLE I: Polymer information Name | Mn | Mw | Endcap | Dispersity | Source
---|---|---|---|---|---
PLA1 | 56687 | 82911 | Ester | 1.463 | Natureworks
PLA2 | 200913 | 296975 | Acid | 1.478 | Akina Polymers
PLA3 | 11075 | 15089 | Ester | 1.362 | Akina Polymers
PCL1 | 19006 | 34345 | Acid | 1.807 | Merck (Sigma)
PHB1 | 128766 | 284529 | None | 2.210 | Merck (Sigma)

###  V-C Uncertainty and performance

Each measurement conducted during any experimental run exhibited a relative
standard deviation of 4.05%. This was calculated by putting identical samples
within the wells and comparing their differences. The given standard deviation
accounts for the systematic error inherent in our designed system.
Furthermore, when considering both PLA1 and PLA2, the standard deviation of
half-life measurements within the same experimental run was determined to be
approximately 21 minutes. As a result, we can assert a 95% confidence level in
the data’s self-consistency within a margin of error of 42 minutes for all
measured half-life values pertaining to PLA1 and PLA2 within the same run.
It’s worth noting that this margin could potentially be wider for polymers
with longer degradation periods or measurements made among different runs. The
circle detection algorithm exhibits a self-optimising capability, leading to
100% accuracy in detecting regions of interest (ROIs) for well plates of size
24 or smaller, contingent upon providing reasonable initial values to the
algorithm. The final iteration of the robotic system achieved an independent
success rate of 98.75% per measurement. This entailed a total of 396
successful measurements collected and analysed, with only 5 instances
resulting in system failure (likely to be the result of computer glitches).
Notably, the system’s longest consecutive successful run comprised 156
measurements, spanning a week-long period without human intervention.

###  V-D Performance of degradation models

((a)) accuracy ($R^{2}$ values) of the exponential degradation model

((b)) accuracy ($R^{2}$ values) of the advanced degradation model

Figure 6: Degradation model-fitting performance with fixed sampling rate.

Two distinct mathematical models (Eq. 6 and Eq. 11) were formulated to
describe the behavior of PLA in a PBS when utilising hyperspectral imaging. As
illustrated in Figures 6(a) and 6(b), the model incorporating a linear
combination of both surface area and mass-dependent degradation rates
demonstrates superior performance, achieving a $R^{2}$ value of over 99% for
the examined polymers. In comparison, the simpler exponential model attains
$R^{2}$ values in the range of approximately 96-98%. This eludes the accuracy
of the developed model for describing the factors underlying polymer
degradation. However, it’s worth noting that the advanced surface-mass-
dependent degradation model doesn’t have a half-life as an inherent
characteristic. Consequently, we introduce a new metric called “total life”,
signifying the time required for the hyperspectral signal to diminish to less
than 1% of its initial amplitude, directly translating to more than 99% of the
polymer degraded and dissolved into the PBS. Table II compares the performance
of the two different models using AIC and MDL; the maximum value data point
was normalised to $1$ prior to applying the model selection metrics. For all
three of the given samples, the surface-mass-dependent degradation model
(denoted as adv.) has a lower AIC and MDL, indicating a more descriptive model
for the degradation process compared to a simple exponential approach (denoted
as exp.). Despite the fact that the surface-mass-dependent model provides a
more accurate description of polymer degradation, we will use the exponential
model (Eq. 6) for subsequent analyses to report the well-established concept
of half-life. This is justified as the $R^{2}$ values fitting the exponential
model fall within an acceptable range.

TABLE II: Degradation model performance Sample | AIC(exp.) | AIC(adv.) | MDL(exp.) | MDL(adv.)
---|---|---|---|---
PLA1 at pH=12.3 | 0.14 | 0.026 | 0.32 | 0.070
PLA2 at pH=2 | 0.048 | 0.020 | 0.11 | 0.054
PLA2 at pH=3 | 0.072 | 0.026 | 0.17 | 0.072

((a)) Sequential sampling algorithm and exponential model fitting

((b)) Sequential sampling algorithm and advanced model fitting

Figure 7: Performance of model fitting via variable sequential sampling
algorithm. The $R^{2}$ value given is between the fitted model using the post-
sampling data and the original data set. The percentage of data used after
sequential sampling is given within the brackets

###  V-E Performance of variable sequential sampling strategy

The sequential sampling strategy is applied to existing data to assess its
performance. The objective is to evaluate the trade-off between the reduction
in the number of data points and the resulting measurement accuracy. In
practice, the algorithm would predict the optimal time for the next
measurement and then select the closest available data point to that predicted
time. When comparing Figures 7(b) and 7(a) to 6(b) and 6(a), respectively, a
notable reduction in the number of data points used is observed, down to only
30-42% of the original data set. Despite this reduction in the number of data
points, the measured half-life and total life remain within 10% of the values
obtained using the full data set. This demonstrates the efficiency and
effectiveness of the sequential sampling strategy in optimising data
collection while maintaining measurement accuracy. The algorithm’s performance
is assessed offline using the collected data in section V-D. This approach
enables a direct comparison between sampling methods, focusing exclusively on
the distinction between fixed and time-variant intervals while minimising the
influence of other variables.

###  V-F Additional polymer types

Throughout our experimentation, PLA served as the primary polymer due to its
relatively rapid degradation, enabling us to explore and demonstrate the
capabilities of the characterisation system. However, as illustrated in Fig.
8, the system showcases its capabilities beyond PLA by successfully measuring
the half-life of other polymers, such as PHB and PCL.

Figure 8: The degradation of PCL1 and PHB1 were measured over the course of
two weeks. PBS of strength pH 14 was used to attain these results. Figure 9:
Illustrates the effect of pH strength on the half-life of PLA2

###  V-G Effect of pH

The degradation of PLA at different pH strengths was measured to give a
quantitative analysis of the effect of pH strength on the polymer degradation
rate. As demonstrated in Fig. 9, increasing the pH of PBS from 9.6 to 12.3
significantly accelerated polymer degradation, reducing the half-life of PLA2
by almost 50 times. However, it’s important to note that this effect could
vary among different polymers and buffer solutions. Hence, our system provides
the methodology needed for high-throughput degradation measurement under
various conditions. This methodology is not limited to the variability of pH
strength and can be extended to polymer mass, temperature, buffer type or
concentration.

###  V-H GPC measurements

The samples undergoing degradation were first imaged and then periodically
taken out of the wells at specific time intervals to conduct the destructive
GPC analysis. This step was crucial to confirm that the changes observed by
the hyperspectral camera were caused by the polymer degradation process. As
depicted in Fig. 10(a), the sample at t=0, which had not undergone any
degradation, displayed a broad peak with a central value of Mn $\approx$
200,000 g/mol. At each subsequent time point, we observe the original peak
diminishing in intensity, while two additional peaks emerge at Mn $\approx$
2,500 and Mn $\approx$ 5,000 g/mol. Notably, no polymer is detected at Mn $<$
1,000 g/mol. This absence in the lower Mn range could be attributed to the
partial dissolution of oligomers below this molecular weight threshold,
resulting in rapid degradation with only monomers remaining.

Fig. 10(b) presents the hyperspectral measurements corresponding to the GPC
results, which are illustrated earlier in Fig. 10(a). Within a span of 25
hours, it appears that the polymer has undergone near-complete degradation,
with the exponential model closely approaching its projected asymptote.
Consequently, Fig. 10(c) showcases the final GPC measurement acquired at the
25th hour compared to the 5th hour of the experiment. This measurement shows a
minute peak at Mn $\approx$ 2,200 g/mol. This observation serves as additional
confirmation, reinforcing the accuracy and reliability of the degradation
measurements conducted by our proposed system.

((a))

((b))

((c))

Figure 10: (a) A plot of the GPC measurements over the course of 5 hours. (b)
The hyperspectral results of the control sample in the GPC run. (c) The GPC
measurements at 5 hours and 25 hours time points.

###  V-I Effect of molecular weight and end-groups on degradation

Blends of three pairs of polymers (PLA1-PLA2, PLA1-PLA3 and PLA2-PLA3) with
different molecular weights were made, resulting in a range of samples with
varying number average molecular weights (Mn). The half-life of these blends
was measured and is illustrated in Fig. 11. The results demonstrate that PLA1
has the highest half-life despite having a smaller Mn than PLA2. Additionally,
we know that PLA1 and PLA3 are both ester end-capped while PLA2 is acid end-
capped. These results show that the degradation rate is affected by a
multitude of inter-dependant factors, such as the combination of end groups
and molecular weight. Additionally, the convergence of half-life measurements
between two distinct sets of blends (PLA1-PLA2 and PLA1-PLA3) and measurements
show the accuracy of the measured half-life.

###  V-J Complex blends

To fully demonstrate the capabilities of the proposed system, we synthesised a
blend comprising three distinct polymers (PLA1-PLA2-PLA3) and subsequently
measured their respective half-lives, as shown in Fig. 12. Many variables
affect the degradation rate of polymers; hence, a high throughput analysis of
a wide range of polymers and their blends through an automated process is
crucial.

Figure 11: Blend of pairs of polymers demonstrating the effect of molecular
weight and end groups. The “+” on a data point represents that it’s a shared
point between two blends. Figure 12: 3D ternary plot of the half life of PLA1,
PLA2, PLA3 and their blends

##  VI Discussion

The characterisation station successfully measured the half life of staple
biodegradable polymers such as PLA, PHB, and PCL within a laboratory
environment. The GPC measurements confirm the observed change in hyperspectral
images is due to polymer degradation followed by dissolution. Due to an
increase in pH strength, the acceleration of degradation was measured
quantitatively to be a factor of 2-5 per integer increase in pH within the
range of 9.6 and 14 for PLA2. To fully quantify the extent of change in the
degradation rate due to an increase in pH strength, a more extensive study
analysing a variety of polymers will be required.

Furthermore, by utilising blends of PLA1, PLA2 and PLA3, we identified a
gradual but non-linear change in the half-life of the PLA samples when they
were blended at different ratios. This is critical as without an extensive
study of their blends, some results could be interpreted as outliers. The
ternary plot in Fig. 12 depicts the smooth transition of half-life between the
different polymers to verify the genuine effects of molecular weight and end
groups underpinning the degradation rate. This lays the groundwork for
automated and high throughput analysis of polymer blends and their properties
within the realm of material science.

The sequential sampling strategy developed to optimise the time interval
between hyperspectral measurements can be implemented in real-time as a next
step to improve the efficiency and multi-experimental throughput of the
system. Additionally, computationally efficient machine learning algorithms
could be explored as an alternative method to the presented method. All the
results provided in this paper were acquired individually, leading to extended
idle periods of the characterisation station. However, the system is capable
of running multiple independent or dependent experiments in parallel, which
should be considered in the future use of the developed system for other
applications.

##  VII CONCLUSION

An innovative robot-assisted automated hyperspectral imaging-based methodology
is proposed to facilitate the temporal characterisation of materials. Through
the utilisation of this developed system, hyperspectral analysis is introduced
as a novel technique for evaluating the degradation rate of biodegradable
polymers. The proposed automated system has accurately measured polymer
degradation rates at high throughput. This capability enables the
characterisation of degradation rates across a wide range of different
conditions. Notably, our observations indicate that multiple factors influence
degradation rates, with end groups emerging as the most influential polymer
property. Our work lays the foundation for further exploration through in-
depth investigations into the individual factors, ultimately expanding our
collective understanding of polymer degradation dynamics. The proposed system
is shown to be capable of generating systematic data for the validation of
models. Future work could utilise this capability to test the suitability of
potential models for material characterisation. There exists ample potential
for extending the application of our demonstrated system to a broader range of
temporal experiments encompassing various materials and conditions.

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