# Promise: Prompt-Driven 3D Medical Image Segmentation Using Pretrained Image
Foundation Models

###### Abstract

To address prevalent issues in medical imaging, such as data acquisition
challenges and label availability, transfer learning from natural to medical
image domains serves as a viable strategy to produce reliable segmentation
results. However, several existing barriers between domains need to be broken
down, including addressing contrast discrepancies, managing anatomical
variability, and adapting 2D pretrained models for 3D segmentation tasks. In
this paper, we propose ProMISe, a prompt-driven 3D medical image segmentation
model using only a single point prompt to leverage knowledge from a pretrained
2D image foundation model. In particular, we use the pretrained vision
transformer from the Segment Anything Model (SAM) and integrate lightweight
adapters to extract depth-related (3D) spatial context without updating the
pretrained weights. For robust results, a hybrid network with complementary
encoders is designed, and a boundary-aware loss is proposed to achieve precise
boundaries. We evaluate our model on two public datasets for colon and
pancreas tumor segmentations, respectively. Compared to the state-of-the-art
segmentation methods with and without prompt engineering, our proposed method
achieves superior performance. The code is publicly available at
https://github.com/MedICL-VU/ProMISe

Index Terms—  Medical image segmentation, lightweight adapter, transfer
learning, prompt engineering, pretrained Segment Anything Model (SAM)

##  1 Introduction

Recently, image segmentation foundation models [1, 2] have revolutionized the
field of image segmentation, demonstrating wide generalizability and
impressive performance by training on massive amounts of data to learn general
representations. Prompt engineering further improves the segmentation
capability of these models. Given proper prompts as additional inputs, these
models can handle various zero-shot tasks across domains and produce reliable
segmentations during inference. Unlike these broad successes, medical image
segmentation is often limited by issues such as expensive data acquisition and
time-consuming annotation processing, resulting in a lack of massive public
datasets available for training. Thus it is desirable to leverage transfer
learning from the natural image domain for robust medical image segmentation
[3].

However, directly leveraging pretrained 2D natural image foundation models for
3D medical image segmentation often leads to sub-optimal results [4]. This is
primarily because: (1) medical images have their own unique contrast and
texture characteristics; (2) anatomical differences among individuals make
medical image segmentation challenging; and (3) slice-wise (2D) segmentation
with transfer learning discards important depth-related spatial context in 3D
medical data. Given these challenges, can we effectively adapt the pretrained
models to achieve robust 3D medical segmentations?

Fig. 1: The proposed framework (ProMISe) and details of transformer encoder
are shown as (a) and (b), respectively.

In this paper, we propose ProMISe, prompt-driven 3D medical image segmentation
using pretrained image foundation models (see Fig. 1). Specifically, ProMISe
takes a 3D input image and a single point prompt as inputs, and uses image and
prompt encoders to produce segmentation. Unlike most promptable models, a
shallow convolutional neural network (CNN) is used as complementary path
alongside the pretrained transformer image encoder [1], with adapters employed
within the transformer to capture 3D depth context. During training, most
weights of the adapted transformer encoder remain static; the other components
in the proposed method are designed in a lightweight manner for efficiency and
trained from scratch. We use a structural loss and a novel boundary-aware loss
for precise decisions. The main novel contributions are:

  * •

We propose a method for 3D medical segmentation that adapts pretrained image
foundation models. Plug-and-play lightweight adapters are used to better
optimize knowledge transfer across domains and more effectively capture fine-
grained features. Our method is compatible with various pretrained image
models, easy to implement, and cost-effective to train.

  * •

We present a simple yet efficient boundary-aware loss for ambiguous edges.
This ready-to-use loss can be seamlessly integrated into any training process
without the need for offline edge map generation from ground truth.

  * •

We validate the performance on two public datasets for challenging tumor
segmentations. Our method outperforms state-of-the-art segmentation methods
consistently.

Related works. Fully fine-tuning image foundation models for a task requires a
large amount computational resources and is not training-efficient. In
contrast, partially fine-tuning [5] or introducing and training new shallow
layers, such as lightweight adapters [6, 7, 8, 9, 10] and the Low-Rank
Adaptation (LoRA) module [11, 12], have demonstrated robust performance as
parameter-efficient fine-tuning methods. Recent works use SAM [1] for 3D
medical image segmentation in a 2D slice-wise manner, which discard important
depth-wise (3D) information and may require additional efforts to create
prompts [5, 12]. Other models use adapters; this approach has proven effective
for adapting a pretrained model from 2D images to 3D (2D+time) videos [6, 9],
and it has subsequently been utilized in 3D medical image segmentation [10]
with the use of adapters in the pretrained transformer block [7]. Although
these models can segment 3D medical images, the image encoder still operates
in a slice-wise (2D) manner with an additional branch for depth information.
The weights for this branch that are replicated from the spatial branch demand
more computational resources. In contrast, a holistic adaptation of SAM for 3D
medical segmentation was proposed in [8], which avoids a depth branch by
including an adaptor with depth-wise convolution [6]. However, a single
adapter in each transformer block may not fully achieve accurate adaptation
due to the notable discrepancies between natural and medical images. Moreover,
this method struggles to adequately capture details and can lead to sub-
optimal results, especially for tumor segmentation. These challenges and the
critical importance of precise segmentation in medical applications motivate
our proposed model as a more robust solution.

##  2 Methods

Fig. 1 illustrates ProMISe, our proposed framework for 3D medical image
segmentation, which employs prompt engineering and a pretrained image
foundation model. Specifically, a 3D patch is taken as input and is fed
through complementary CNN and transformer encoders. The prompt encoder
utilizes the deepest feature from the transformer encoder (blue arrow in Fig.
1) as input together with the point prompt. Subsequently, all features,
including the original input, are used to predict the segmentation mask via a
lightweight CNN decoder. During training, the transformer encoder is partially
tuned, while the rest are trained from scratch.

Image encoders. Our model is designed to effectively capture both global and
local information using complementary transformer and CNN encoders,
respectively.

For the transformer encoder (Fig. 1(b)), the input 3D image patch first passes
through an embedding layer to create tokens with their positional information.
Specifically, the pretrained weights from SAM [1] are employed for spatial
patch embedding, and we introduced a trainable depth embedding layer for 3D
data. The same approach is applied for positional encoding. Furthermore, we
adapted the pretrained weights from SAM and fine-tuned the normalization layer
in every transformer block. Unlike other works that employ a single adapter at
the beginning of the transformer block [6, 8], an additional lightweight
adapter is used before the output to optimize knowledge transfer across
domains and further refine the image features. Notably, the adapter employs
depth-wise convolution to handle 3D images.

Inspired by the hybrid network design [13], a CNN encoder is used to capture
detailed information to complement the transformer. This is particularly
desirable for tumor segmentation, as the boundaries are often ambiguous. It is
designed as a shallow network for efficiency (Fig. 2(a)).

Prompt encoder. We adapt the visual prompt encoder based on [2] (Fig. 3).
Unlike the prompt encoder proposed in SAM [1], we incorporate image embeddings
from the transformer encoder as an additional input. Point embeddings are
derived from the given point prompt and image embedding using visual sampling
(e.g., grid sampling) to ensure that their semantic features are aligned with
image embeddings. Subsequently, the self-attention layer is applied to the
point embeddings and learnable global queries. Afterwards, the image
embeddings are applied to these queries via cross-attention. The output of the
prompt encoder is fed to the mask decoder.

During training, 10 random points from background are provided for each input
patch to increase the generlizability to noisy prompts. In contrast to
previous work that utilized 40 points from target region as prompts [8], we
randomly select 10 point prompts during each iteration if the input patch
contains foreground. For prompt engineering, our goal is a single click with
minimal prior knowledge, but more prompts are supported if desired during
inference.

Mask decoder. Instead of directly adapting the mask decoder from the
foundation model in a 2D manner, we designed a shallow network to efficiently
capture features in 3D and trained it from scratch (Fig. 2(b)). The multi-
level features from the transformer encoder (Fig. 1(b)) are refined by two
successive convolutional blocks. These are followed by a transposed
convolution to ensure the features remain the same size. The fused features
are processed through another convolutional block and a segmentation head for
final results.

Fig. 2: The details of (a) CNN encoder, and (b) mask decoder.  Fig. 3: The
details of the proposed prompt encoder.

Boundary-aware loss. In medical image segmentation, accurately delineating the
boundaries of objects is important, especially for irregularly shaped objects
such as tumors [14]. Besides popular structural segmentation losses, such as
the combined Dice loss and cross-entropy loss (denoted as $L_{structural}$),
we further propose a boundary loss ($L_{boundary}$) to preserve fine details
and produce robust segmentations. Moreover, by emphasizing edge accuracy, the
model might generalize better to unseen data for tumor segmentation. As shown
in Fig. 1, we extract a smooth boundary map rather than a binary boundary for
a more robust representation, and because learning from a binary boundary is a
challenging task. Specifically, we use average-pooling operation $P_{ave}$
with kernel size 5 as boundary generator. Given a binary mask $M$, the smooth
boundary is derived: $B(M)=|M-P_{ave}(M)|$. The total objective function is:

$L(S,G)=\lambda_{1}L_{structural}(S,G)+\lambda_{2}L_{boundary}(B(S),B(G))$

where $S$ and $G$ represent segmentation and ground truth.
$L_{structural}=L_{Dice}+L_{CE}$ is used to capture the structural information
and $L_{boundary}=L_{MSE}$ recovers the detailed contours. Unlike other
methods [15] that require complicated offline computation of edge or distance
maps to avoid iterative generation, our proposed ready-to-use boundary loss is
computationally efficient and can be easily adapted to any segmentation task,
and is independent of any augmentation.

##  3 Experiments

Table 1: Dice and normalized surface Dice (NSD) for colon and pancreas tumor. Bold indicates best performance. Significant improvements (2-tailed paired t-test, $p<0.05$) are denoted via ∗. The promptable models use 1 point prompt per 3D volume. Dataset | Metric |  nnU-Net [16] |  3D UX-Net [17] |  nnFormer [18] |  Swin-UNETR [19] |  3DSAM-adapter [8] | ProMISe
---|---|---|---|---|---|---|---
Colon | Dice | 45.60 | 23.07 | 21.36 | 37.23 | 57.32 | 66.81∗
NSD | 53.01 | 32.84 | 32.05 | 51.16 | 73.65 | 81.24∗
Pancreas | Dice | 39.12 | 37.57 | 35.98 | 37.98 | 54.41 | 57.46∗
NSD | 57.66 | 55.25 | 53.45 | 56.42 | 77.88 | 79.76∗
Fig. 4: Qualitative results. BL denotes boundary-aware loss. The major differences are highlighted by orange arrows. Table 2: Quantitative results of ablation study with single point prompt unless noted. R and C represent residual and concatenate fusions, and B indicates boundary loss. + shows the cumulative variants. Best viewed by individual sections. | Colon | Pancreas
---|---|---
Method | Dice | NSD | Dice | NSD
baseline [8] | 57.32 | 73.65 | 54.41 | 77.88
\+ two adapters | 61.61 | 73.88 | 56.08 | 77.89
\+ up-Conv | 62.92 | 77.62 | 55.37 | 77.38
ProMISe-R | 63.67 | 79.96 | 55.15 | 79.02
ProMISe-R-B | 64.75 | 79.77 | 56.57 | 79.46
ProMISe-C | 64.76 | 77.59 | 56.35 | 78.01
ProMISe-C-B (proposed) | 66.81 | 81.24 | 57.46 | 79.76
baseline [8] (10 prompts) | 63.09 | 79.97 | 55.94 | 79.18
ProMISe-C-B (10 prompts) | 67.28 | 81.63 | 58.05 | 80.36

###  3.1 Experimental settings

Datasets. We evaluated our proposed method on two public datasets from the
Medical Segmentation Decathlon (http://medicaldecathlon.com/) for challenging
tumor tasks from pancreas and colon applications, where ambiguous edges are
present. These consist of 281 ($0.61\times 0.61\times 0.7$ to $0.98\times
0.98\times 7.5mm^{3}$) and 126 ($0.54\times 0.54\times 1.25$ to $0.98\times
0.98\times 7.5mm^{3}$) 3D CT volumes, respectively. Following the setup from
the prior study [8], we used the same data split for each task with a
training/validation/testing split of 0.7/0.1/0.2 and only use tumor labels to
focus on binary segmentation.

Preprocessing. We resample to 1$mm$ isotropic resolution, intensity clip based
on foreground $0.5$ and $99.5$ percentiles, and Z-score normalize based on all
foreground voxels. Four data augmentations were used: random flip, rotation,
zoom, and intensity shift. During training, an input patch of size $128\times
128\times 128$ was randomly selected such that its center pixel is equally
likely to be foreground or background. Subsequently, each dimension was
upsampled to 512.

Implementation details. We utilized pretrained ViT-B from SAM [1] as
transformer encoder, and set $\lambda_{1}:\lambda_{2}=1:10$ during training.
The batch size was 1, and initial learning rate was 0.0004 with decreased
amount $2{e^{-}6}$ every epoch. The AdamW optimizer was used with a maximum of
200 epochs. We used PyTorch, MONAI and an NVIDIA A6000 GPU for our
experiments. The Dice score and normalized surface Dice (NSD) are used for
evaluation. Compared state-of-the-art methods include: CNN (nnU-Net [16]), CNN
with large kernel (3D UX-Net [17]), Swin-encoder with CNN decoder (Swin-unetr
[19]), pure transformer (nnFormer [18]), and adaptation method with adapters
(3DSAM-adapter [8]). We retrained using their official codes, and the
pretrained weights are also employed if publicly available.

###  3.2 Results

Quantitative results. Tab. 1 presents a detailed comparison of results for
colon and pancreas tumor segmentation. Notably, while CNN-based networks
segment these tumors more effectively than transformers, prompt-driven methods
outperform others when provided with only a single point in the entire volume.
Our proposed method consistently outperforms all in terms of both Dice and
boundary (NSD) metrics.

Ablation study. We also investigated the efficiency variations of the proposed
ProMISe (Tab. 2). The use of two adapters and the boundary-aware loss mostly
improved the results. Interestingly, switching from trilinear upsampling to
up-convolution improved the performance for the colon, but showed a decline
for the pancreas. This implies that trilinear upsampling may be more
appropriate for pancreas tumors, which are typically round in shape. Using
concatenation (-C) in the CNN encoder offers better Dice scores than residual
connections (-R), though the latter improves surface quality more. While the
performance of ProMISe improves with 10 prompts, the improvement is limited
over a single prompt. Furthermore, it is challenging to identify the tumor
area due to ambiguous boundaries, making the use of a single click preferable
in practice, as it requires less expert knowledge.

Qualitative results. Fig. 4 shows qualitative visualizations from top-
performing promptable methods. ProMISe yields results that closely align with
the ground truth. 3DSAM-adapter [8] fails to detect certain regions that
ProMISe captures, even without the boundary-aware loss. This indicates the
improved generalizability of the model through our proposed modifications.
Moreover, the use of the boundary-aware loss yields robust segmentations,
alleviating issues of both under-segmentation for colon and over-segmentation
for pancreas tumors, respectively. Notably, the boundary-aware loss improves
segmentation not just for the irregularly shaped colon tumors but also for the
pancreas tumors, which typically have a more regular, rounded shape. However,
slight under-segmented areas are found in pancreas segmentation.

##  4 conclusion

In this paper, we propose a promptable network, named ProMISe, designed for
robust 3D tumor segmentation using pretrained weights from image foundation
models. We evaluate on two public datasets, where our model consistently
outperforms state-of-the-art methods across all tasks. Moreover, the critical
role of the two adapters and boundary-aware loss techniques are demonstrated.
Future work will aim to improve the efficiency through knowledge distillation.

Acknowledgments. This work was supported, in part, by NIH U01-NS106845, NSF
grant 2220401.

##  5 Compliance with Ethical Standards

This research study was conducted retrospectively using human subject data
made available in open access by MSD. Ethical approval was not required as
confirmed by the license attached with the open access data.

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# Interfacial Dynamics and Catalytic Behavior of Single Ni Atom Site

G. Sudha Priyanga<EMAIL_ADDRESS>Department of Sciences and Methods for
Engineering, University of Modena and Reggio Emilia, 42122 Reggio Emilia,
Italy.  Department of Metallurgical and Materials Engineering, Indian
Institute of Technology Madras, Chennai, 600036, India.  Sushant Kumar Behera
<EMAIL_ADDRESS>Materials Science Division, Lawrence Berkeley National
Laboratory, CA - 94720, USA.  Materials Engineering, Indian Institute of
Science, Bangalore - 560012, India.

###### Abstract

Single-atom catalysts (SACs) have garnered significant interest due to their
ability to reduce metal particles to the atomic scale, enabling finely tunable
local environments and enhanced catalytic properties in terms of reactivity
and selectivity. Despite this potential, their application has largely been
confined to small-molecule transformations as metal-catalyzed reaction. In
this study, we present a diverse single-atom nickel (Ni) catalyst established
via a nanoporous carbon (NPC) supported practice. This catalyst represents a
breakthrough by achieving the bond formation between carbon and nitrogen and
interfacial dynamics in the SAC. The present first principle-based density
functional simulations establish the reaction dynamics and catalytic behaviour
of such SAC. This dynamic nature comprises an exclusive nitrogen intercalated
site showing excellent base effects. This base quickly tunes the interfacial
atmosphere, enabling dynamic movement of adatoms into the NPC species,
significantly changing the reaction path in Ni SACs due to superior steric
effects. The research demonstrates that SACs can extend the capabilities of
catalytic systems to include a wider range of complex reactions, offering
substantial promise for the development of new, efficient synthetic methods
for creating value-added molecular products.

###### keywords:

American Chemical Society, LaTeX

Introduction\- The use of noble metals supported on metal oxides as
heterogeneous catalysts is prevalent in industry, with the performance of
these catalysts being highly dependent on the size of the metal particles 1,
2, 3. This size influences both the efficiency of metal atom usage and the
selectivity of the catalyst. To optimize these factors, precise control of
particle size through advanced preparation techniques is essential. Single-
atom catalysts (SACs), which reduce metal particles to the atomic scale, have
garnered significant interest in the field of catalysis, largely due to the
pioneering work of Qiao’s group 4. While there has been extensive research on
noble metals such as Pt, Au, Ir, and Pd in SACs, the exploration of more cost-
effective transition metals remains limited. This is despite the concept of
isolated active sites within solid catalysts being established earlier 5, 6.
Transition metals like Ni are particularly important for ${CO}_{2}$ activation
and hydrogenation reactions. Recent studies have highlighted the importance of
Ni cluster sizes in determining reaction stability and selectivity 7. SACs
provide atomic-level precision, enabling highly tunable local environments and
superior catalytic properties, including enhanced reactivity and selectivity.
However, despite these benefits, SACs have primarily been used for small-
molecule transformations. Extending their application to more complex
reactions, such as metal-catalyzed cross-coupling, which is crucial for
synthesizing a variety of chemical products, remains a significant challenge
8.

In this study, we present a groundbreaking development in the field of single-
atom catalysts (SACs) by creating a heterogeneous single-atom nickel (Ni)
catalyst through a novel supercritical nanoporous carbon (NPC) assisted
method. This innovative catalyst enables, for the first time, C-C and C-N
bond-forming migratory insertion reactions using SACs, representing a major
leap forward in catalyst technology. The successful execution of these complex
reactions with SACs expands their potential applications far beyond the
conventional small-molecule transformations, paving the way for new
possibilities in catalysis.

Our quantum mechanical simulations offer crucial insights into the reaction
mechanism, uncovering the role of a distinctive nitrogen-rich coordination
site. This site demonstrates a surprising base effect, wherein the base
temporarily alters the coordination environment, thereby facilitating
migratory insertion into an N-C species. This process, which was previously
impeded by significant steric hindrance, showcases the ability of SACs to
transcend traditional limitations. Furthermore, it highlights the potential of
incorporating novel coordination structures, which have been largely
unexplored in catalyst design, thus opening new avenues for innovation in the
field 9, 10.

The results of this investigation emphasize the immense potential of single-
atom catalysts (SACs) in broadening the scope of catalytic systems to
encompass a broader array of intricate reactions. Through the successful
demonstration of SACs’ capability in facilitating C-C and C-N bond formations,
this study lays the groundwork for devising novel and efficient synthetic
approaches for generating high-value molecular products. These insights not
only showcase the versatility of SACs but also point towards their capacity to
transform synthetic chemistry by spearheading innovative strides in catalyst
design and implementation.

Computational Details\- Density Functional Theory (DFT) simulations were
carried out using the SIESTA code 11. The chosen exchange-correlation (XC)
functional encompassed the van der Waals density functional (vdW-DF) and the
C09 exchange 12, offering enhanced accuracy in line with experimental results,
akin to the PBESol XC functional for solids 13. Norm-conserving Troullier-
Martins pseudopotentials (PPs) 14 were applied to account for core electrons
of various atomic species. Additionally, valence electrons of specific atomic
species like C, N, and Ni were considered in the computational calculations.
During geometry optimization and electronic structure computations, a 0.15 eV
energy shift was introduced for the polarized double-(DZP) basis set.
Structural optimizations were performed with a force convergence limit of less
than 0.01 eV/${\AA}$. Notably, the study focused on nanoporous carbon (NPC)
system and the interactions between several nitrogen atoms (NPC-N) along with
Ni interaction (NPC-Ni) and the reduction of nitrogen in presence of both N
and Ni atoms (NPC-N-Ni) system were explored. Various interaction parameters,
including density of states (DOS), adsorption energies (AE), and reaction
paths were investigated. In the simulations, energy cutoff of 560 eV was
utilized for the real space mesh grids in all the systems. Brillouin zone
integration employed a $3\times 3\times 1$ k-point mesh according to the
Monckhorst-Pack scheme for both pristine (NPC) and atom-intercalated (NPC-N,
NPC-Ni and NPC-N-Ni) systems. The density of states and total energy were
calculated using the tetrahedron method. It should be noted that the
incorporation of spin-orbit coupling (SOC) was omitted in these calculations
to avoid overestimation of energy values in the computed profiles.

Results and Discussions\- The optimized molecular structures of NPC and atom-
intercalated systems (NPC-N, NPC-Ni, NPC-N-Ni) are shown in Fig. 1. Elaborate
information regarding the geometric parameters of the simulation cell is
provided in Table S1. It’s important to note that the bulk density of the
molecular system corresponds to the density of the simulation cell. Notably,
the density within the simulation cell is adaptable with changes in cell
volume, all the while maintaining the structural integrity and independence
from external perturbations. In cases of larger simulation cells, residual
empty space might persist, which could lead to increased computational
overhead. To address this, a complete optimization of the cell volume is
implemented, as outlined in Table S2, thereby effectively managing any
computational inefficiencies associated with empty space within the simulation
cell.

Figure 1: Geometry optimization of systems (a) NPC, and atom-intercalated
systems, like (b) NPC-N, (c) NPC-Ni and (d) NPC-N-Ni. Coffee, silver and light
blue color for C, Ni and N atoms, respectively. N passivation improves surface
activation energy.

In this analysis, we have determined the total forces acting on each atom at a
fixed radial distance by deriving the total energy with respect to atomic
positions within the simulation cell. These computations align with the
principles of the Born-Oppenheimer surface, replicating ab initio quantum
mechanical simulations. Our findings primarily converge towards the Hellmann-
Feynman force equation, focusing on first-order approximations and neglecting
higher-order terms (i.e., second and third-degree terms). Here, this presents
a breakdown of various energy components comprising ion-electron interactions,
incorporating both local and non-local pseudopotential contributions. The non-
local aspect of total energy and forces is analyzed on a linear scale.
Consequently, this approach enables the effective and efficient handling of
larger nanoporous systems through linear scaling Density Functional Theory
(DFT) calculations. An intriguing observation from Table S2 is the enhanced
dynamism imparted to the NPC system through N passivation, followed closely by
the presence of Ni adatoms. This insight sheds light on the mechanisms
governing the system’s behavior and offers valuable guidance for optimizing
its performance in practical applications.

Electronic properties: Figure 2 provides insight into the electronic density
of states (DOS) for the NPC system and all atom-intercalated variations. The
DOS patterns confirm heightened activity in proximity to the Fermi level,
underscored by the localization of electron states and the discernible
influence of atom intercalation on system functionality. Significantly, a
considerably broader energy spectrum is observed at the Fermi level when N or
Ni atoms are intercalated, or when both N and Ni are simultaneously
intercalated. This indicates an amplified adsorption capability and heightened
sensitivity towards these specific atomic species. This dynamic nature of
active, delocalized surface electrons is distinctly illustrated through the
distribution of DOS population density in ion-intercalated systems, resulting
in broader distributions with more pronounced peak intensities compared to the
pristine NPC system.

Figure 2: Density of states (DOS) of systems (a) NPC, and atom-intercalated
systems, like (b) NPC-N, (c) NPC-Ni and (d) NPC-N-Ni. The vertical dotted
black lines indicate the Fermi levels. This dynamic nature of active,
delocalized surface electrons is evident on N or Ni atoms are intercalated, or
when both N and Ni are simultaneously intercalated.

Reaction path mechanisms: According to reports 15, 16, 17, 18, it is
preferable to have a good catalyst with superior adsorption Gibbs free
energies ($\Delta$G) and adsorption energies ($E_{ads}$). Figure 3 shows the
plot of both Gibbs free energies and calculated adsorption energies for the
systems in case of $N_{2}$* adsorptions. It is observed that the $\Delta$G
values of $N_{2}$ molecule chemisorption on Ni intercalated NPC systems are
more negative than those of NPC-N system. Similar trend is also noticed for
the $E_{ads}$ values.

Figure 3: Adsorption Gibbs free energies and calculated adsorption energies of
atom-intercalated systems, like NPC-N, NPC-Ni and NPC-N-Ni.

In the limelight of activation barrier for the N bonds to form stable 2N via
transition state, we further calculate its dissociation barrier energy for the
NPC system with N (Fig. 4(a)) and Ni (Fig. 4(b)) intercalation. The NPC-N
system shows a high energy barrier value of 3.75 eV which is still quite
difficult for the $N_{2}$ molecule to dissociate into separate N atoms and
later get adsorbed. While the intercalation happens with Ni atom, the bind
length got shorter and the barrier reduced to 3.68 eV, making the process
smooth. Thus, the metal atom helps in tuning the N bonds exhibiting superior
$N_{2}$ activation capacity.

Figure 4: The reaction paths for $N_{2}$ adsorptions via the $N_{2}$
dissociation minimum energy path on the NPC system (a) NPC and atom-
intercalated system in (b) NPC-Ni.

The Density of States (DOS) patterns indicate an increased level of activity
near the Fermi level, highlighting the localization of electron states and the
significant impact of atom intercalation on system functionality.
Specifically, the intercalation of nitrogen (N), nickel (Ni), or a combination
of both results in a notably broader energy spectrum at the Fermi level. This
broadening signifies an enhanced adsorption capability and a heightened
sensitivity towards these particular atomic species. According to research, an
effective catalyst typically exhibits favorable adsorption Gibbs free energies
($\Delta$G) and adsorption energies (${E}_{ads}$). In our study, the plots of
Gibbs free energies and calculated adsorption energies reveal the performance
of various systems during the adsorption of $N_{2}$ molecules. For the NPC-N
system, the energy barrier for $N_{2}$ dissociation into separate N atoms is
relatively high, at 3.75 eV, which poses a significant challenge for the
adsorption process. However, when Ni atoms are intercalated, the binding
length is shortened, reducing the energy barrier to 3.68 eV. This reduction in
the energy barrier facilitates a smoother adsorption process. Consequently,
the presence of Ni atoms aids in tuning the nitrogen bonds, thereby enhancing
the system’s capacity for $N_{2}$ activation. This demonstrates that metal
atom intercalation, particularly with nickel, plays a crucial role in
optimizing the adsorption and activation properties of the system, making it a
more effective catalyst for nitrogen-related reactions.

Conclusions\- In summary, we introduce a novel single-atom nickel (Ni)
catalyst supported by nanoporous carbon (NPC), marking a significant
advancement. This catalyst achieves carbon-nitrogen bond formation and
showcases dynamic interfacial behavior within the SAC. Utilizing first-
principle-based density functional simulations, we uncover the reaction
dynamics and catalytic characteristics of this SAC. The dynamic nature of this
catalyst is highlighted by an exclusive nitrogen-intercalated site that
exhibits exceptional basic effects. This element rapidly adjusts the
interfacial environment, facilitating the dynamic movement of adatoms into NPC
species. Consequently, this leads to a notable alteration in the reaction
pathway within Ni SACs due to superior steric effects. Our study underscores
the potential of SACs to expand the repertoire of catalytic systems, allowing
for a broader spectrum of complex reactions. This progress holds substantial
promise for the advancement of efficient synthetic methods aimed at producing
high-value molecular products.

Acknowledgements SKB acknowledges DOE, Govt. of USA and UGC, Govt. of India.
Parts of the simulations are also performed in Computational facility of SERC,
IISc.
Author Contributions G.S.P and S.K.B. formulated the problem, conducted all
the work and analysis. Both the authors wrote the draft.
Notes The authors declare no competing financial interest.
Keywords Single atom catalyst; Ni atom site; Nano porous carbon; Interfacial
mechanism; reaction mechanism

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