arXiv subjects

Jihua Chen

Publications and source records attributed to Jihua Chen.

8 recordsLinked to original sources

arXiv2026

Chemical Reaction Engineering and Catalysis: AI/ML Workflows and Self-Driving Laboratories

Chemical reaction engineering is key to industrial might and sustainable chemistry. This will be enabled using smart, efficient catalysts or catalysis ecosystems. This is possible with advanced artificial intelligence and machine learning (AI/ML) workflows that need to be employed as agentic AI projects. The fundamentals of catalysis need to be emphasized. A strong focus on catalyst design, mechanistic studies, reaction engineering, and scale-up must use ML-driven workflows, along with high-throughput experimentation (HTE) and an autonomous, self-driving laboratory (SDL). Laboratory experience and data-driven approaches are valuable when working together to accelerate this development. Parametrize and create a virtuous circle for data-driven discovery across heterogeneous, homogeneous, and biocatalysts to enable utility in many chemical process industries as agentic AI tasks. This article builds the case for discovery science in catalysis and continuous improvement in chemical reaction engineering with this new ecosystem.

physics.chem-ph↗

arXiv2026

AI/ML-Driven Surface Plasmon Resonance (SPR) and Spectroscopy: Materials Interfaces and Autonomous Experiments

This review explores the evolution of Surface Plasmon Resonance (SPR) spectroscopy and sensing, transitioning from fundamental studies of adsorption-desorption kinetics to the sophisticated sensing with Electropolymerized Molecularly Imprinted Polymers (E-MIPs). A significant portion of our previous research focuses on the optical properties, electrochromism of polymer dielectrics, and structure-order correlation in polymer brushes and hierarchical ultrathin films. We then address the transformative impact of Artificial Intelligence (AI) and Machine Learning (ML) in data interpretation, culminating in the conceptualization of Self-Driving Labs (SDLs). The importance of generating high-quality training data through high-throughput experimentation (THE) with the SPR is a possibility. These autonomous systems represent the future of materials science, enabling the rapid, closed-loop discovery and optimization of next-generation SPR sensors and analytical methods. This overview highlights the trajectory for integrating conventional experimental design with AI-driven sensing and analytical chemistry across materials and biomedical applications.

cond-mat.mtrl-sci↗

arXiv2026

Polymer Brushes and Grafted Polymers: AI/ML-Driven Synthesis, Simulation, and Characterization towards autonomous SDL

Polymer brushes and grafted polymers have attracted significant interest at the intersection of polymers, interfacial chemistry, colloidal science, and nanostructuring. The confinement of high-density grafted polymers and differences in swelling regimes govern the synthetic challenges and the interesting physics underlying their macromolecular dynamics. In this article, we focus on another intersection, artificial intelligence and machine learning (AI/ML), and how workflows will enhance the microstructure and composition of these systems. It will also accelerate potential applications through high-throughput experimentation (HTE) and data-driven intelligence, enabling scientific discovery and optimization. Applications in microfluidics, sensors, bioimplants, drug delivery, and related areas may yet offer more opportunities for ML-driven optimization. There is also interest in applying these studies with self-driving laboratories (SDLs) that can leverage autonomous systems for synthesis screening, characterization, and application evaluation.

cond-mat.soft↗

arXiv2026

Advanced Manufacturing with Renewable and Bio-based Materials: AI/ML workflows and Process Optimization

Advanced manufacturing with new bio-derived materials can be achieved faster and more economically with first-principle-based artificial intelligence and machine learning (AI/ML)-derived models and process optimization. Not only is this motivated by increased industry profitability, but it can also be optimized to reduce waste generation, energy consumption, and gas emissions through additive manufacturing (AM) and AI/ML-directed self-driving laboratory (SDL) process optimization. From this perspective, the benefits of using 3D printing technology to manufacture durable, sustainable materials will enable high-value reuse and promote a better circular economy. Using AI/ML workflows at different levels, it is possible to optimize the synthesis and adaptation of new bio-derived materials with self-correcting 3D printing methods, and in-situ characterization. Working with training data and hypotheses derived from Large Language Models (LLMs) and algorithms, including ML-optimized simulation, it is possible to demonstrate more field convergence. The combination of SDL and AI/ML Workflows can be the norm for improved use of biobased and renewable materials towards advanced manufacturing. This should result in faster and better structure, composition, processing, and properties (SCPP) correlation. More agentic AI tasks, as well as supervised or unsupervised learning, can be incorporated to improve optimization protocols continuously. Deep Learning (DL), Reinforcement Learning (RL), and Deep Reinforcement Learning (DRL) with Deep Neural Networks (DNNs) can be applied to more generative AI directions in both AM and SDL, with bio-based materials.

cond-mat.soft↗

arXiv2026

Agentic AI and Machine Learning for Accelerated Materials Discovery and Applications

Artificial Intelligence (AI), especially AI agents, is increasingly being applied to chemistry, healthcare, and manufacturing to enhance productivity. In this review, we discuss the progress of AI and agentic AI in areas related to, and beyond polymer materials and discovery chemistry. More specifically, the focus is on the need for efficient discovery, core concepts, and large language models. Consequently, applications are showcased in scenarios such as (1) flow chemistry, (2) biosensors, and (3) batteries.

cond-mat.mtrl-sci↗

arXiv2024

Enhanced Capacitance in Surfactant Mediated Ion Complexes

Ion complexes hold the key for various energy transfer and communication systems in nature and industries. Achieving controllable mechanical and electrical properties in these complex systems is highly desirable but remains challenging. In this work, we propose the use of amphiphilic molecules to mediate salt crystallization. The resultant ionic interfaces can be tunable through reduced surface tensions of crystal facets, an additional intermolecular modifier, and the diffusion-limited crystallization during controlled solvent evaporation. Specifically, we utilized the ionic surfactant Sodium Lauryl sulfoacetate (SLSA) to mediate the crystallization of sodium chloride. Citric acid (CA) was adopted as a hydrogen-bond modifier. Systems mediated by SLSA exhibited the highest capacitance, and a significant enhancement of capacitance was also observed in systems with both SLSA and CA. The substantially increased capacitance of these ionic complexes can be attributed to changes in interfacial and crystalline grain structures. Transmission electron microscopy (TEM), optical microscopy (OM), and Finite Element Analysis (FEA) were used to study the effects of surfactant molecules in these ionic complexes. Understanding the role of ionic complexation in defining the thermodynamic and kinetic process of the crystallization process will contribute to better optimization in nucleation and scalability of organic and inorganic crystal production.

cond-mat.mtrl-sci↗

arXiv2024

AI for Manufacturing and Healthcare: a chemistry and engineering perspective

Artificial Intelligence (AI) approaches are increasingly being applied to more and more domains of Science, Engineering, Chemistry, and Industries to not only improve efficiencies and enhance productivity, but also enable new capabilities. The new opportunities range from automated molecule design and screening, properties prediction, gaining insights of chemical reactions, to computer-aided design, predictive maintenance of systems, robotics, and autonomous vehicles. This review focuses on the new applications of AI in manufacturing and healthcare. For the Manufacturing Industries, we focus on AI and algorithms for (1) Battery, (2) Flow Chemistry, (3) Additive Manufacturing, (4) Sensors, and (5) Machine Vision. For Healthcare applications, we focus on: (1) Medical Vision (2) Diagnosis, (3) Protein Design, and (4) Drug Discovery. In the end, related topics are discussed, including physics integrated machine learning, model explainability, security, and governance during model deployment.

cond-mat.mtrl-sci↗

arXiv2014

Diffusive Dynamics of Water inside Hydrophobic Carbon Micropores Studied by Neutron Spectroscopy and Molecular Dynamics Simulation

When water molecules are confined to nanoscale spacings, such as in the nanometer size pores of activated carbon fiber (ACF), their freezing point gets suppressed down to very low temperatures ($\sim$ 150 K), leading to a metastable liquid state with remarkable physical properties. We have investigated the ambient pressure diffusive dynamics of water in microporous Kynol\texttrademark ACF-10 (average pore size $\sim$11.6 Å, with primarily slit-like pores) from temperature $T=$ 280 K in its stable liquid state down to $T=$ 230 K into the metastable supercooled phase. The observed characteristic relaxation times and diffusion coefficients are found to be respectively higher and lower than those in bulk water, indicating a slowing down of the water mobility with decreasing temperature. The observed temperature-dependent average relaxation time $<τ>$ when compared to previous findings indicate that it is the size of the confining pores - not their shape - that primarily affects the dynamics of water for pore sizes larger than 10 Å. The experimental observations are compared to complementary molecular dynamics simulations of a model system, in which we studied the diffusion of water within the 11.6 Å gap of two parallel graphene sheets. We find generally a reasonable agreement between the observed and calculated relaxation times at the low momentum transfer $Q$ ($Q\le 0.9$ Å${^{-1}}$). At high $Q$ however, where localized dynamics becomes relevant, this ideal system does not satisfactorily reproduce the measurements. The best agreement is obtained for the diffusion parameter $D$ associated with the hydrogen-site when a representative stretched exponential function, rather than the standard bi-modal exponential model, is used to parameterize the self-correlation function $I(Q,t)$.

cond-mat.soft↗