Single-Atom Nanozymes in Sensing Analysis
A 2024 review in Chemosensors examined advances in single-atom nanozymes for sensing analysis. It discussed catalytic materials with isolated metal centers and their potential roles in analytical detection.
NanoSchool / Blog / Nanotechnology & AI
AI and Single-Atom Nanozymes: Finding Active Sites for Sensitive Biosensing
How artificial intelligence is helping researchers understand atomic-scale catalysts, improve enzyme-like reactions, and develop next-generation biosensors.
- 1isolated metal atom acting as the catalytic center
- 4nitrogen atoms coordinating the iron atom in the example
- 6steps in an AI-assisted active-site discovery process
- 5verification methods covered, from HAADF-STEM to catalytic assays
In this article
Can a Single Atom Make a Biosensor More Sensitive?
Imagine trying to detect a tiny amount of a disease-related biomarker in a blood sample.
The biomarker may be present at a very low concentration, surrounded by proteins, salts, and other biological molecules. A conventional sensing system might struggle to distinguish its signal from background interference.
Now imagine designing a material that can amplify that signal through a carefully controlled chemical reaction.
What if the performance of that material depended on the position and chemical environment of just one metal atom?
This is the idea behind single-atom nanozymes, an emerging class of catalytic nanomaterials that imitate selected functions of natural enzymes.
Scientists are investigating these materials for biosensing, medical diagnostics, environmental monitoring, and chemical analysis.
However, developing an efficient single-atom nanozyme is not simply a matter of placing a metal atom onto a supporting material.
The atom’s surrounding structure, electronic properties, and interactions with reactants influence how well it performs.
This is where artificial intelligence is becoming increasingly valuable.
By combining computational chemistry, materials datasets, and machine learning, researchers can investigate which atomic arrangements are most likely to produce useful catalytic activity.
The goal is to move from repeated trial-and-error experiments toward more informed, evidence-based catalyst design.
In this article, we explore how AI Single-Atom Nanozymes are being studied, why active-site design matters, and how these materials could support the development of more sensitive biosensors.
What Are Single-Atom Nanozymes?
A nanozyme is a nanomaterial that exhibits enzyme-like catalytic activity.
Natural enzymes are biological catalysts that help chemical reactions proceed efficiently. However, some natural enzymes can be sensitive to environmental conditions, difficult to store, or costly to manufacture and stabilize.
Nanozymes offer an alternative approach by using engineered materials to perform selected catalytic functions.
Researchers have investigated nanozymes based on metal oxides, carbon nanomaterials, noble metals, and other catalytic structures.
Single-atom nanozymes, often abbreviated as SAzymes, take this concept further.
Instead of relying primarily on larger nanoparticles or clusters, these materials contain individual metal atoms dispersed and anchored to a suitable supporting material.
These isolated atoms can act as catalytic centers.
For example, a single iron atom may be coordinated to nitrogen atoms within a carbon-based structure.
The surrounding atoms influence how the metal center interacts with chemical reactants.
This arrangement can resemble certain structural features found in natural metalloenzymes.
Common components of single-atom nanozymes
| Component | Role in catalytic design |
|---|---|
| Central metal atom | Participates in catalytic reactions |
| Coordination atoms | Influence the metal’s electronic environment |
| Supporting material | Stabilizes isolated atoms and affects reaction behavior |
| Structural defects | May create or modify anchoring and active sites |
| Reactant molecules | Interact with catalytic centers during reactions |
Commonly investigated metals include:
- Iron
- Cobalt
- Copper
- Manganese
- Platinum
- Other transition metals
Researchers frequently study carbon-based supports, nitrogen-doped carbon structures, and selected metal–organic framework-derived materials.
The important feature is not simply the presence of an isolated metal atom.
It is how the entire local environment influences catalytic performance.
Why Are Active Sites Important in Single-Atom Catalysts?
Every catalyst requires regions where reactants can interact and chemical reactions can occur.
These regions are called active sites.
In a single-atom nanozyme, the catalytic environment may involve an isolated metal center together with its neighboring atoms and supporting structure.
Consider two hypothetical materials containing iron atoms.
Material 1
An iron atom is coordinated to four nitrogen atoms.
Material 2
The iron atom has a different local coordination environment.
Although both materials contain iron, their catalytic properties may differ.
Changes in coordination can influence electron distribution, binding energies, reaction pathways, and the stability of intermediates.
These differences can affect the speed and selectivity of enzyme-like reactions.
What determines active-site performance?
Several structural characteristics are important:
- Type of central metal atom
- Number and identity of coordinating atoms
- Bond lengths and local geometry
- Electronic structure
- Defects and functional groups in the supporting material
- Accessibility of the catalytic site
- Reaction conditions such as pH and temperature
Researchers must identify which combinations support the desired reaction.
A site that performs well for one catalytic process may not be suitable for another.
This is why active-site design is one of the central challenges in developing high-performance single-atom nanozymes.
How Does AI Help Researchers Find Better Active Sites?
Traditional catalyst development often involves preparing a series of materials, testing their catalytic performance, and modifying their structures based on the results.
This process can be productive, but evaluating many possible combinations of metals, supports, and coordination environments requires substantial experimental effort.
Artificial intelligence offers a complementary approach.
Machine-learning models can analyze existing material properties and measured or calculated catalytic outcomes.
The models then identify relationships that may help researchers prioritize promising active-site configurations.
For example, a research dataset might contain information about different single-atom catalysts, including their metal centers, coordination structures, adsorption energies, and catalytic performance.
A model can investigate how these characteristics are associated with activity.
Researchers may then use the model to rank candidate structures for further computational or experimental assessment.
A typical AI-assisted active-site discovery process
- 1
Collect catalyst information
Researchers gather structural and catalytic data from published studies, materials databases, laboratory measurements, or computational calculations.
- 2
Describe the atomic environment
Relevant descriptors may include metal identity, coordination number, bond characteristics, and electronic properties.
- 3
Develop predictive models
Machine-learning algorithms examine relationships between descriptors and the selected catalytic outcomes.
- 4
Screen candidate structures
The model estimates which configurations may offer promising performance for a particular reaction.
- 5
Interpret predictions
Researchers examine important features, model uncertainty, and whether the candidates resemble materials included in the training data.
- 6
Validate promising candidates
Further computational analysis, synthesis, characterization, and catalytic experiments help determine whether the predicted performance is achievable.
Artificial intelligence can accelerate candidate selection, but it cannot independently prove that a proposed atomic configuration will work.
How AI Single-Atom Nanozymes Support Catalytic Activity Prediction
Catalytic activity describes how effectively a material supports a particular chemical reaction under defined conditions.
Different nanozymes may imitate different enzyme functions.
Some exhibit:
- Peroxidase-like behavior
- Oxidase-like behavior
- Catalase-like behavior
- Superoxide dismutase-like behavior
These functions are especially interesting because they can be used to generate measurable signals in sensing systems.
For example, peroxidase-like nanozymes can catalyze selected reactions involving hydrogen peroxide.
When a suitable color-producing substrate is present, the reaction can generate an optical signal.
Researchers can measure that change and use it as part of a biosensing assay.
AI may help investigate how the nanozyme’s atomic structure influences the catalytic reaction.
Which AI methods are relevant?
| Computational method | Potential research application |
|---|---|
| Random Forest | Predicting catalytic activity from material descriptors |
| XGBoost | Modeling nonlinear relationships between structure and performance |
| Neural networks | Learning complex patterns in suitable catalyst datasets |
| Graph neural networks | Representing local atomic structures and their connectivity |
| Bayesian optimization | Prioritizing candidate structures for further testing |
| Explainable AI | Investigating which features influence model predictions |
The best approach depends on the scientific question and the available data.
For small datasets, simpler models may be more reliable than highly complex algorithms.
For atomistic representations, graph-based learning methods may be useful because they can represent atoms and their local connections.
However, predictions should be evaluated using appropriate validation strategies and compared with relevant computational or experimental evidence.
How Does Active-Site Design Improve Biosensor Sensitivity?
A biosensor converts a biological or chemical interaction into a measurable signal.
Depending on the system, this signal may be:
- Optical
- Electrical
- Fluorescent
- Electrochemical
The purpose is to detect a target substance, often called an analyte.
Examples include glucose, hydrogen peroxide, dopamine, and selected biological biomarkers.
Single-atom nanozymes can function as catalytic components within these sensing platforms.
Their role is often to facilitate reactions that produce or amplify a measurable signal.
Example: A color-changing nanozyme biosensor
Consider a sensing system that uses a peroxidase-like nanozyme.
Under appropriate conditions, the nanozyme can help catalyze the oxidation of a color-forming substrate such as TMB in the presence of hydrogen peroxide.
The reaction produces a detectable color change.
A sensing assay may use the intensity of this signal to estimate a substance associated with the reaction.
The process can be summarized as:
- Target-related reaction
- →
- Formation or consumption of a measurable reagent
- →
- Nanozyme-catalyzed signal generation
- →
- Optical measurement
A highly active catalytic site may improve signal generation under suitable conditions.
However, stronger catalytic activity does not automatically produce a better biosensor. Researchers must also consider background reactions, detection selectivity, reproducibility, measurement noise, and the behavior of the sensor in real samples.
What makes a biosensor sensitive?
Sensitivity depends on how strongly and reliably the measured signal changes when analyte concentration changes.
Researchers also evaluate:
- Limit of detection
- Selectivity for the target analyte
- Linear detection range
- Signal stability
- Repeatability
- Recovery in real samples
A good diagnostic sensor must balance these characteristics rather than maximize catalytic activity alone.
Applications of Single-Atom Nanozymes in Diagnostic Sensors
Single-atom nanozymes are being investigated in several types of analytical and biosensing systems.
Glucose detection
Researchers have explored catalytic systems that use nanozyme-assisted reactions to measure glucose-related signals.
Some platforms combine glucose oxidase with a peroxidase-like nanozyme in an enzyme–nanozyme cascade.
Other designs investigate alternative catalytic configurations.
Hydrogen peroxide sensing
Hydrogen peroxide is involved in multiple biochemical reactions and is a useful target for developing and evaluating catalytic sensors.
Single-atom nanozymes with suitable activity can support optical or electrochemical detection approaches.
Dopamine detection
Dopamine is an important biological signaling molecule.
Research on single-atom nanozyme sensors includes methods for detecting dopamine and distinguishing its signal from interfering substances.
Glutathione and oxidative stress-related analysis
Glutathione plays an important role in cellular redox regulation.
Some nanozyme-based sensing systems investigate glutathione through its effects on catalytic reactions or sensor signals.
Environmental and food-related detection
Beyond biomedical research, single-atom nanozymes are also being explored for sensing selected contaminants and chemical substances.
These applications show the versatility of atomic-scale catalytic materials.
However, promising performance in laboratory samples does not automatically establish that a sensor is clinically validated or suitable for routine field deployment.
A Research Example: Comparing Single-Atom Active Sites for Biosensing
Suppose a research team wants to develop a peroxidase-like nanozyme for a colorimetric biosensor.
The researchers are considering three hypothetical catalytic configurations.
| Candidate | Illustrative structure | Research question |
|---|---|---|
| Catalyst A | Iron coordinated to nitrogen-doped carbon | Does the coordination environment support strong catalytic activity? |
| Catalyst B | Cobalt coordinated to a modified carbon structure | How do changes in metal identity affect reaction behavior? |
| Catalyst C | Copper anchored to a defect-rich support | Could support defects influence active-site accessibility and stability? |
The team collects relevant computational and experimental information.
They then train a model to estimate a defined catalytic property.
The model ranks Catalyst B as a promising candidate.
Should the researchers immediately conclude that Catalyst B will produce the best biosensor?
No.
The team must first investigate whether the model’s prediction is reliable and relevant to the intended reaction.
They may need to examine the proposed site’s stability, possible reaction mechanisms, and sensitivity to experimental conditions.
After synthesis, the team must verify the atomic dispersion and coordination environment.
Finally, the material must be tested within the actual biosensor system.
The example illustrates the difference between identifying a promising catalyst and demonstrating a useful sensing device.
A good AI prediction helps researchers choose what to investigate next.
How Are Single-Atom Active Sites Verified Experimentally?
Computational predictions are most useful when researchers can compare them with physical evidence.
A model may suggest that a particular metal–nitrogen coordination environment will exhibit favorable catalytic properties.
But how can scientists determine whether that structure actually exists in the synthesized material?
Researchers use advanced characterization techniques to examine atomic dispersion, local structure, and chemical states.
Important characterization methods
HAADF-STEM
High-angle annular dark-field scanning transmission electron microscopy.
Can help visualize isolated heavy atoms against suitable supporting materials under appropriate imaging conditions.
XAS
X-ray absorption spectroscopy.
Provides information about oxidation states and local atomic environments.
EXAFS
Extended X-ray absorption fine structure.
Can help investigate the neighboring atoms and coordination environment surrounding a metal center.
XPS
X-ray photoelectron spectroscopy.
Provides surface-sensitive information about elemental composition and chemical states.
Catalytic activity assays
Measure how the material performs under specified reaction conditions.
No single characterization method necessarily provides complete proof of every structural feature.
Researchers often combine complementary techniques to obtain a more reliable understanding of the active sites.
For example, microscopy may help identify isolated atoms, while X-ray absorption analysis can provide information about local coordination.
Catalytic assays then help connect the measured structure with actual reaction performance.
What Does Recent Research Show?
Research published between 2024 and 2026 highlights growing interest in the design of single-atom nanozymes and their applications in sensing.
Tuning Atomically Dispersed Metal Sites
A 2025 review in Angewandte Chemie International Edition explored how tuning atomically dispersed metal sites can influence catalytic activity, specificity, and sensing performance.
Single-Atom Nanozymes for Electrochemical Biosensors
Also in 2025, a review in Talanta examined single-atom nanozymes as catalytic components for electrochemical biosensors. The authors discussed enzyme-like activities and important challenges associated with sensor development.
Detecting Small Biomolecules
A 2026 review in Molecules examined single-atom nanozyme applications in the detection of small biomolecules, including glucose, hydrogen peroxide, dopamine, glutathione, and uric acid.
Together, these studies suggest that atomic-scale engineering is becoming an important strategy for improving catalytic sensing materials.
At the same time, challenges involving activity, selectivity, stability, material characterization, and real-sample performance remain important.
Challenges in AI-Guided Single-Atom Nanozyme Design
Although the field is promising, several challenges remain.
Limited high-quality training data
Machine-learning models need reliable information connecting atomic structures with catalytic performance.
Different studies may report results using different conditions, units, or experimental methods.
Difficulty identifying true active sites
A synthesized material may contain isolated atoms, small clusters, and other structural features.
Determining which sites are responsible for the observed catalytic activity can be difficult.
Stability of isolated atoms
Single metal atoms may migrate or aggregate during synthesis, storage, or catalytic reactions.
Their stability depends on the support and operating conditions.
Model generalization
A model trained on one family of catalysts may not accurately predict the behavior of a substantially different material.
Selectivity in complex samples
A highly active nanozyme may respond to multiple substances.
This can create interference when the material is used in blood, biological fluids, or environmental samples.
Scaling up production
A catalyst that performs well in a small research experiment may be difficult to manufacture consistently at larger scale.
These limitations show why computational predictions and experimental evidence need to work together.
The Future of AI Single-Atom Nanozymes
The next phase of single-atom nanozyme research is likely to involve closer integration between artificial intelligence, atomic-scale characterization, computational chemistry, and biosensor engineering.
Several developments are particularly promising.
AI-guided active-site screening
Machine-learning models may help researchers compare larger numbers of metal centers and coordination environments.
Structure–activity prediction
Better datasets may help clarify relationships between atomic configuration and catalytic performance.
Explainable catalyst design
Interpretable models can help identify structural features associated with promising predictions.
Computational–experimental feedback
Newly measured results can be added to datasets to improve subsequent predictions.
Advanced biosensor integration
Research may explore integrating single-atom catalysts into portable sensing platforms, microfluidic devices, and other analytical systems.
Multi-objective optimization
Future models may evaluate catalytic activity, selectivity, stability, material cost, and sensor performance together.
These approaches could make catalyst research more systematic.
However, their success will depend on reliable data, meaningful experimental validation, and realistic performance testing.
Why This Research Matters for Students and Scientists
Single-atom nanozymes connect several important research disciplines.
They bring together nanotechnology, catalysis, materials science, artificial intelligence, analytical chemistry, and biotechnology.
For students and early-career researchers, understanding this field creates opportunities to investigate practical questions such as:
- How does atomic coordination influence catalytic activity?
- Which material descriptors are useful for machine-learning models?
- How can computational predictions guide experiments?
- What makes an enzyme-like material suitable for biosensing?
- How can researchers distinguish catalytic activity from overall sensor performance?
These questions encourage researchers to think beyond material synthesis alone.
They highlight the importance of combining structural understanding, data analysis, and experimental measurements.
At NSTC | NanoSchool, research-oriented learning across nanotechnology, artificial intelligence, and biotechnology supports the development of skills relevant to interdisciplinary scientific problems.
Students interested in this area can begin with:
Frequently Asked Questions
1. What are single-atom nanozymes?
Single-atom nanozymes are catalytic nanomaterials containing isolated atomic active sites that can imitate selected functions of natural enzymes.
2. How does AI help design single-atom nanozymes?
AI can analyze material descriptors and catalytic data to identify patterns and prioritize promising atomic configurations for further investigation.
3. Why are active sites important in biosensing?
Active sites influence the catalytic reactions that generate measurable sensor signals. Their structure can affect reaction activity, stability, and selectivity.
4. Which metals are commonly studied in single-atom nanozymes?
Researchers investigate metals such as iron, cobalt, copper, manganese, and platinum, among others.
5. Can single-atom nanozymes replace natural enzymes?
They may serve as alternatives in selected sensing applications, but they do not universally outperform or replace natural enzymes.
6. What AI models can be used for catalytic activity prediction?
Potential approaches include Random Forest, XGBoost, neural networks, graph neural networks, and Bayesian optimization.
7. Are single-atom nanozyme biosensors ready for clinical use?
Many reported applications remain at the research or prototype stage. Clinical use requires appropriate validation of analytical performance, reliability, safety, and intended use.
Conclusion: Designing Better Biosensors, One Active Site at a Time
Single-atom nanozymes represent an exciting direction in modern catalytic materials research.
By engineering individual metal atoms and their surrounding environments, scientists can investigate how atomic-scale structure influences enzyme-like activity.
Artificial intelligence adds another useful capability by helping researchers analyze existing data, identify promising structural features, and prioritize candidate materials.
Together, these methods may contribute to more informed catalyst design and improved biosensing technologies.
However, the path from an AI prediction to a reliable diagnostic sensor still requires careful synthesis, structural characterization, catalytic testing, and validation with relevant samples.
The real opportunity is not simply to find the most active atom. It is to understand how atomic structure, catalytic behavior, and sensor performance work together.
For researchers interested in the future of nanotechnology, biosensing, and artificial intelligence, single-atom nanozymes offer a valuable opportunity to connect computational methods with experimental science.
Scientific note: This article discusses emerging research. Reported catalytic performance and biosensing applications depend on material design and experimental conditions. Computational predictions alone do not establish diagnostic reliability or clinical suitability.
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Scientific References and Further Reading
View the five references
- Zhang, H., Zhang, S., & Zhang, Z. (2024). Advancements and Applications of Single-Atom Nanozymes in Sensing Analysis. Chemosensors, 12(10), 209. https://doi.org/10.3390/chemosensors12100209
- Tuning Atomically Dispersed Metal Sites in Nanozymes for Sensing Applications. Angewandte Chemie International Edition (2025). https://doi.org/10.1002/anie.202424070
- Zhu, X., et al. (2025). Single-atom nanozymes for enhanced electrochemical biosensing: A review. Talanta, 294, 128179. https://doi.org/10.1016/j.talanta.2025.128179
- Application of Single-Atom Nanozymes in the Detection of Small Biomolecules: A Review. Molecules (2026), 31(8), 1242. https://doi.org/10.3390/molecules31081242
- Recent Advances in Synthesis and Biosensing Applications of Single-Atom Nanozymes. Nanoscale (2026). https://doi.org/10.1039/D6NR01140C