Functional Biomaterials, Spatial Biosensing, and Artificial Intelligence
- 1St. Petersburg College
Abstract
Recent advances in bioengineering mark a paradigm shift from passive structural scaffolding toward dynamic, interactive platforms capable of modulating and continuously monitoring the cellular microenvironment. This review explores the convergence of functional biomaterials, spatial biosensing architectures, and artificial intelligence (AI) in decoding and steering complex cell behavior. Responsive hydrogels, nanozymes, metal–organic frameworks (MOFs), and conductive scaffolds serve as information-generating systems that adapt to microenvironmental cues and produce measurable bio-signals. Concurrently, microfluidic organ-on-a-chip architectures integrated with continuous electrochemical, plasmonic, and field-effect transistor sensors enable real-time, non-destructive tracking of cellular secretions and metabolic gradients. To process these high-dimensional multimodal streams, machine learning models—ranging from explainable AI and neural networks to generative transformers and digital twins—are deployed for biomaterial optimization, label-free spectral fingerprinting, and spatial deconvolution. Finally, we highlight the application of these integrated bio-platforms in cancer modeling, liquid biopsy analytics, regenerative medicine, and closed-loop wound theranostics while addressing key hurdles in biofouling, dataset integration, and industrial scalability.
Related
- Artificial Intelligence for Advanced Functional Materials: Progress and Emerging Frontiers
- Artificial Intelligence and Machine Learning Model Implementation for UC Irvine Dataset 322
- Integrating artificial intelligence and molecular simulation for peptide discovery: limitations, validation and decision-centered workflows
- Artificial Intelligence‐Assisted Design of High‐Entropy Oxide Catalysts
- Concentration Determination of Aspirin Deposit Patterns Using Quantitative Image Analysis and Artificial Intelligence
BibTeX
@misc{long2026functional,
title = {{Functional Biomaterials, Spatial Biosensing, and Artificial Intelligence}},
author = {Samuel Long},
howpublished = {ChemRxiv},
year = {2026},
month = aug,
doi = {10.26434/chemrxiv.15008031/v1},
url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008031/v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Functional Biomaterials, Spatial Biosensing, and Artificial Intelligence
AU - Samuel Long
PY - 2026
DA - 2026/08/30
PB - ChemRxiv
DO - 10.26434/chemrxiv.15008031/v1
UR - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008031/v1
AB - Recent advances in bioengineering mark a paradigm shift from passive structural scaffolding toward dynamic, interactive platforms capable of modulating and continuously monitoring the cellular microenvironment. This review explores the convergence of functional biomaterials, spatial biosensing architectures, and artificial intelligence (AI) in decoding and steering complex cell behavior. Responsive hydrogels, nanozymes, metal–organic frameworks (MOFs), and conductive scaffolds serve as information-generating systems that adapt to microenvironmental cues and produce measurable bio-signals. Concurrently, microfluidic organ-on-a-chip architectures integrated with continuous electrochemical, plasmonic, and field-effect transistor sensors enable real-time, non-destructive tracking of cellular secretions and metabolic gradients. To process these high-dimensional multimodal streams, machine learning models—ranging from explainable AI and neural networks to generative transformers and digital twins—are deployed for biomaterial optimization, label-free spectral fingerprinting, and spatial deconvolution. Finally, we highlight the application of these integrated bio-platforms in cancer modeling, liquid biopsy analytics, regenerative medicine, and closed-loop wound theranostics while addressing key hurdles in biofouling, dataset integration, and industrial scalability.
ER -