The purpose of this investigation is to assess the outcome of Oxytactic microorganism in chemical reactive flow of TiO2 + GO/water based hybrid nanofluid flow across a sheet applying artificial intelligence. [Formula: see text] and [Formula: see text] nanoparticles are combined with the base fluid, water ([Formula: see text]). There are numerous real-world uses for the concept of artificial intelligence-driven performance improvement of oxytactic microbes in hybrid nanofluid with chemical reaction and thermal radiation in a variety of sectors. By controlling the temperature and chemical conditions for improved performance and focused treatment, it is applicable in biomedical engineering to improve microbial-based drug delivery systems. The model may improve the efficacy of environmental biotechnology's bioremediation processes, which use microorganisms to degrade pollutants under a range of chemical and temperature conditions. Additionally, the model can improve the efficacy of microbial cultures employed in fermentation or other bio-manufacturing processes in industrial processes like cooling systems by optimizing heat transfer in reactors utilizing nanofluids. The ordinary differential equations can alternatively be resolved utilizing an artificial neural network-based technique with Bayesian regularization. State training, performance, fitting plots, model response, and error histograms plots are utilized to explore the resulting network.
The development of low-budget, efficient, and robust pH-universal hydrogen evolution reaction (HER) electrocatalysts is greatly essential for making water splitting a viable technology to produce hydrogen. Herein, we report the ingenious design of an advanced HER electrocatalyst composed of Ru/Ni hetero-nanoparticles in situ encapsulated in N-doped hollow carbon polyhedron/nanotubes integrated hierarchical superstructures (abbreviated as Ru/Ni@N-CP CNTs-0.50 hereafter). The concurrent implementation of interfacial engineering, nanoscale hollowing design, and carbon-support hybridization renders the resultant Ru/Ni@N-CP CNTs-0.50 with modified electronic structure, enriched active sites, and shortened electron/mass transport pathways. Density functional theory (DFT) computations further demonstrate that the construction of Ru/Ni heterojunction can lower the energy barrier for H2O dissociation and optimize H* adsorption strength, thereby accelerating HER kinetics. Thanks for the composition and architectural advantages, the well-designed Ru/Ni@N-CP CNTs-0.50 catalyst demonstrates exceptional HER activity, requiring overpotentials of only 29 and 40 mV to achieve a current density of 10 mA cm- 2 in 0.5 M H2SO4 and 1.0 M KOH, respectively. This work reveals a sustainable method for the fabrication of multi-component Ru-based electrocatalysts and presents a further deep understanding of synergistic electronic engineering to boost hydrogen evolution.
Neurodegenerative diseases, including Alzheimer's disease (AD) and Parkinson's disease (PD), present a growing public health challenge globally. Recent advancements in neurotechnology and neuroengineering have significantly enhanced brain-computer interfaces, artificial intelligence, and organoid technologies, making them pivotal instruments for diagnosis, monitoring, disease modeling, treatment development, and rehabilitation of various diseases. Nonetheless, the majority of neural interface platforms focus on unidirectional control paradigms, neglecting the need for co-adaptive systems where both the human user and the interface continually learn and adapt. This selected review consolidates information from neuroscience, artificial intelligence, and organoid engineering to identify the conceptual underpinnings of co-adaptive and symbiotic human-machine interaction. We emphasize significant shortcomings in the advancement of long-term AI-facilitated co-adaptation, which permits individualized diagnostics and progression tracking in Alzheimer's disease and Parkinson's disease. We concentrate on incorporating deep learning for adaptive decoding, reinforcement learning for bidirectional feedback, and hybrid organoid-brain-computer interface platforms to mimic disease dynamics and expedite therapy discoveries. This study outlines the trends and limitations of the topics at hand, proposing a research framework for next-generation AI-enhanced neural interfaces targeting neurodegenerative diseases and neurological disorders that are both technologically sophisticated and clinically viable, while adhering to ethical standards.
Photocatalytic hydrogen peroxide (H2O2) production offers a sustainable route for on-demand generation. Covalent organic frameworks (COFs) are attractive candidates, as their modular architectures can be engineered for optimal light capture and charge separation. However, their practical use has been hindered by the limited chemical stability of imine-linked COFs under prolonged operation. This challenge is further compounded by the narrow selection of building blocks suitable for efficient H2O2 generation, restricting advancements in catalytic performance. Here, we present two polyimide-based COFs (NUS-76 and NUS-77) constructed with a newly designed fused-ring triphenylene building block, enabling efficient photocatalytic H2O2 production. Utilizing a simple water-assisted microwave synthesis, both COFs exhibit remarkable robustness, retaining their crystalline structure even in strongly acidic and alkaline environments, outperforming the established imine-linked COF. Remarkably, NUS-77 integrates triphenylene and oligo(phenylenevinylene) moieties, delivering an impressive photocatalytic H2O2 evolution rate up to 23,284 μmol g-1 h-1. To further showcase their practical potential, we developed a continuous-flow photoreactor incorporating NUS-77, which produces 40.7 mM H2O2 in 9 h and retains activity over four consecutive cycles (36 h) under 1 sun irradiation (P = 100 mW cm-2), a product concentration exceeding that of most reported COF photocatalysts. Theoretical calculations reveal that the imide linkage enhances charge separation and facilitates the formation of ·O2- intermediates during the catalytic redox cycle. Together, these findings illustrate how strategic engineering of linkages and building blocks effectively overcomes the stability limitations of COF-based photocatalysts, providing a viable pathway to durable frameworks for solar-to-chemical energy conversion.
Self-sustained oscillators are emerging as key physical elements for neuromorphic electronics, providing a hardware route to emulate the spiking dynamics of biological neurons. As conventional computing architectures struggle with power dissipation and parallel processing limitations, oscillatory devices offer a means to reproduce the brain's remarkable efficiency-performing adaptive and nonlinear tasks with minimal energy consumption. This review provides a unified synthesis of the diverse families of self-oscillating systems developed across physics, chemistry, and electronic engineering. We classify oscillators according to their operational mechanisms, distinguishing those driven by negative differential resistance (NDR) instabilities from those sustained by active-feedback amplifiers. Their common behavior is described within a nonlinear dynamical framework that links materials, electronic response, and the emergence of limit cycles in phase space. We discuss how these devices-ranging from electrochemical and memristive oscillators to transistor-based and hybrid architectures-can be modeled, measured, and coupled to form complex networks. Particular attention is given to the experimental identification of active elements and impedance signatures that reveal self-oscillation. By bridging device physics, nonlinear dynamics, and neuromorphic computing, this review outlines a coherent foundation for designing scalable, energy-efficient oscillatory systems that connect the physical principles of chemical and electronic oscillators with the computational logic of the brain.
In industries like chemical processing, energy systems, metallurgy, filtration, and electronics cooling, activation energy in magneto-nanofluid flow with variant viscosity is essential for regulating reaction rates, maximizing heat and mass transfer, enhancing energy efficiency, and guaranteeing safe operation. This work is important because it advances our knowledge of heat and mass transmission in magnetized nanofluid flows, where the fluid viscosity varies nonlinearly with temperature or other physical parameters. The study's primary goal is to create a numerical model capable of precisely analyzing the intricate relationship between magnetic forces, nonlinear viscosity, porous media, and nanoparticle transport. To get the perfect predictions, the governing model employed the efficacy of artificial neural networks with Levenberg Marquardt structure back propagation (ANN-LMSB), which is designed to investigate energy activation with exponential viscosity variant with temperature on magneto-hydrodynamic nanofluid flow past porous plate (MHD-NFPP). To articulate mathematical modeling, the Reynolds exponential model is used. By employing the model of Darcy-Brinkman-Forchheimer, the momentum equation is additionally formulated. Thermophoresis force and Brownian diffusion have been inspected by implementing Buongiorno model. Along with magnetic body force, mass conservation, nanoparticle concentration, momentum, and energy equations are expressed. Initially, the flow of fluid is denoted by the scheme of PDEs, which are transformed into the structure of ODEs. By employing Adams numerical method, a data set for suggested ANN-LMSB is produced for diverse scenarios by alteration of stretching parameter, the Hartmann number, the thermal and concentration Grashof numbers, the thermophoresis, the Brownian motion, Prandtl number, the chemical reaction constant, Schmidt number, and relative temperature parametric number. By training, testing, and validation procedures of ANN-LMSB, estimated solution of distinct cases is verified, and for the perfection of the suggested model, the comparison for verification is carried out. Afterwards, execution of suggested ANN-LMSB was validated by regression evaluation, mean square error, and histogram studies. Correctness level in range from 10-9 to 10-11 approves distinction of suggested methodology established on the closeness of the recommended and reference results.
Efficient identification of high-value molecules under limited experimental budgets remains a central challenge in automated chemical design. In Level 4 automation settings, where chemists define candidate spaces and machine learning models guide experimental selection, pool-based active learning has emerged as a practical framework for prioritizing compounds. However, conventional approaches primarily optimize predictive accuracy of surrogate structure-function models, which may not directly align with the objective of maximizing search efficiency toward identifying the molecule with the most favorable property value within a predefined candidate pool. We propose a policy-based active learning framework that reformulates molecular pool selection as a sequential decision-making problem. The iterative selection process is modeled as a Markov decision process, and a policy network is trained to optimize cumulative search performance under constrained evaluation budgets. Property prediction models are incorporated as contextual signals rather than optimization targets, enabling direct optimization of search efficiency. We construct 1409 molecular identification tasks derived from MoleculeNet and ChEMBL. In addition, six literature-curated in vivo tasks are constructed to assess performance. Across both benchmark and in vivo settings, the framework demonstrates improved efficiency in identifying optimal molecules within limited evaluation cycles. Case studies further illustrate the strengths and limitations of the framework. These results highlight the potential of policy-driven active learning to enhance molecular identification efficiency in predefined candidate pools, offering a generalizable strategy for budget-constrained chemical discovery.
Along with the rising interest in regenerative medicine and tissue engineering comes the necessity to gain a deeper understanding of the underlying nature behind the mechanical responses of various soft tissues. This study investigates the differences in mechanical responses of the aorta and pulmonary artery, both of which share similar trilayered structures, by examining how their microstructural architectures influence their response to traction forces. As such, two-photon microscopy was used to visualize elastin and collagen fibers, via two-photon excited autofluorescence and second-harmonic generation respectively, during a mode I tensile fracture test on trouser-shaped specimens harvested from the aorta and pulmonary artery. The tensile tests revealed that while the aorta exhibited significantly higher low-strain elasticity compared to the pulmonary artery (aorta: 184.9 ± 37.4 kPa, pulmonary artery: 25.9 ± 6.0 kPa), both tissues required comparable forces for fracture propagation under mode I fracture (aorta: 7.6 ± 3.4 N, pulmonary artery: 6.3 ± 4.5 N). In this study, the possible reasons behind these mechanical disparities are discussed based on the microscopic images depicting the specimen's histologic structural deformation throughout the tensile test. Elastic responses from the aorta and pulmonary artery were discussed using static images of the elastin and collagen networks taken at a fixed interval during the tensile test. Moreover, this study presents the first successful acquisition of a microscopic video capturing the microstructural failure dynamics during arterial fracture, which served as a basis for discussion on the fracture properties of the aorta and pulmonary artery under mode I loading. These findings offer insights into fracture propagation mechanisms in arteries and underscore the importance of microstructural analysis for elucidating soft tissue biomechanics. STATEMENT OF SIGNIFICANCE: An artery's biomechanical response is essential for its physiological function and is influenced by the vessel's microstructure. While prior studies have focused on the elastic response of the aorta alone, this study examines both elastic and fracture behavior in the aorta and pulmonary artery. Microscopic image sequences capturing elastic and plastic deformation were acquired in both vessels, revealing microstructural differences leading to distinct biomechanical responses. From an engineering perspective, the results show that differences in elastin and collagen organization lead to artery-specific biomechanical properties, indicating that biomechanical models should incorporate vessel-specific structure rather than assuming uniform behavior. Clinically, these differences are relevant for vascular graft design, where matching both mechanical response and structural characteristics may improve performance and durability.
Psoriasis is a prevalent chronic inflammatory skin disease in which pattern recognition receptors, particularly the NLRP3 inflammasome, are increasingly implicated in disease pathogenesis. Solanum nigrum (SN) has been used in traditional and clinical practice for psoriasis treatment, but its therapeutic mechanisms and key active constituents remain unclear. This study investigated the anti-psoriatic mechanisms of SN and identified its major bioactive component. NLRP3 inflammasome activation in psoriasis was evaluated using public transcriptomic datasets and clinical skin biopsies. The therapeutic effects of SN were assessed in imiquimod-induced primary and relapse psoriasis-like dermatitis models. Bulk RNA sequencing of lesional skin was performed to identify SN-regulated pathways. SN was chemically characterized by UPLC-MS, and candidate active compounds were prioritized by molecular docking and molecular dynamics simulation. NLRP3 inflammasome activation was consistently elevated in psoriatic lesions in both public datasets and clinical specimens. SN markedly alleviated disease severity in primary and relapse models, reduced keratinocyte hyperproliferation, and lowered systemic inflammatory cytokine levels. Transcriptomic analysis showed that SN mainly modulated PRR/NLR-related signaling pathways. Mechanistically, SN inhibited NLRP3 inflammasome activation and decreased IL-1β and IL-18 production. Integrated chemical, biological, and computational analyses identified trigonelline as a major active constituent contributing to the anti-psoriatic effects of SN. SN ameliorates psoriasis-like dermatitis primarily through suppression of NLRP3 inflammasome signaling, with trigonelline identified as a key contributory active component. These findings provide mechanistic support for the therapeutic application of SN in psoriasis.
Poly(lactic-co-glycolic acid) (PLGA) is a biodegradable biopolymer widely used in advanced drug delivery systems (DDSs) due to its biocompatibility, controllable degradation behavior, and tunable physicochemical properties. Its degradation into naturally metabolized lactic and glycolic acids makes PLGA particularly attractive for biomedical applications, positioning PLGA nanoparticles as versatile carriers that bridge material design and therapeutic delivery. In this context, electrospray (electrohydrodynamic atomization) has emerged as an innovative and scalable processing technique that enables precise control over nanoparticle size, morphology, and internal structure under mild conditions, which is particularly suitable for engineering biopolymer-based DDSs. This review provides a comprehensive overview of electrospray-fabricated PLGA nanoparticles, with emphasis on the relationship between processing conditions, polymer structure, and functional performance. The fundamental mechanisms governing drug release, including diffusion, polymer degradation, and their combined effects, are discussed in relation to PLGA properties. The influence of electrospray parameters on nanoparticle formation, morphology, and internal architecture is analyzed, highlighting how process-structure-property relationships can be tailored to achieve specific release profiles. Structural design strategies, including single-matrix, core-shell, and surface-functionalized nanoparticles, are further examined as approaches to enable controlled and sequential dual-DDSs. In addition, emerging modeling and computational approaches are briefly discussed as complementary tools for understanding and optimizing nanoparticle behavior. Challenges and technical problems, such as substrates for nanoparticle detachment, are discussed.
In this study, we exploit the structural dynamics of MIL-53(Al) metal-organic framework (MOF) to develop a highly efficient and selective aqueous-phase heterogeneous catalyst, [MIL-53-Co(OH)], featuring atomically dispersed CoII(OH) active species at the MOF's nodes for the hydrogenation of CO2 into ethanol. Under mild conditions (130°C, 20 bar, H2/CO2: 3), MIL-53-Co(OH) achieves 92% CO2 conversion with an ethanol productivity of 10 133 µmol gcat -1 h-1 and 93% selectivity. Comparisons with the rigid MIL-68(Al) and breathing-suppressed variants of MIL-53 MOFs revealed that the lattice flexibility of MIL-53-Co(OH) enhances ethanol productivity by at least 3.5-fold while suppressing the formation of CH3OH. Experimental, structural and computational analysis suggest that the reversible narrow-pore (np)↔large-pore (lp) interconversion of MIL-53-Co(OH) periodically optimizes the Al2[μ3-O-Co(OH)] active-site geometry at MOF's node, which synchronizes in situ generated CH3OH activation followed by CO insertion to promote C‒C coupling. During the np→lp transition, the transient lattice expansion relaxes the Al-[μ3-O-Co(OH)]-Al hinge, which reduces the activation barrier of σ-bond metathesis between the Co-H bond and C-O bond of CH3OH, a key step in the catalytic cycle. This strategy of leveraging conformational dynamics of MOFs for active-site engineering opens new avenues in designing highly active earth-abundant metal catalysts for challenging chemical transformations.
Embedding mechanophores into polymeric solids enables the design of materials that respond to mechanical stimuli, with applications in sensing, self-healing, and adaptive systems. This review summarizes modeling approaches for mechanophores in polymer solids across multiple length scales, from nanoscale quantum chemical models and mesoscale reactive molecular dynamics to macroscale continuum frameworks. We also discuss theoretical foundations such as force-modified potential energy surfaces. We then compare computational strategies to experimental insights, highlighting key findings, ranging from the roles of mechanophore geometry, chemical substituents, network architecture, and physical cross-linking in force transduction and activation. Persistent challenges in the field include capturing multiscale dynamics, local environmental effects, and heterogeneity. Advancing predictive models will accelerate mechanophore discovery and enable rational design of mechanoresponsive polymeric solids.
Aromatic hydrocarbons such as benzene, toluene, and ethylbenzene are extensively used as solvents in coatings, resin, and artificial leather industries. Azeotropic mixtures involving these compounds are commonly encountered in chemical manufacturing, where accurate azeotropic temperature and composition are essential for designing and optimizing separation processes such as extractive and pressure-swing distillation. In this study, two quantitative structure-property relationship (QSPR) models were developed to predict the azeotropic temperature and composition of binary mixtures containing aromatic hydrocarbons using only molecular structural information. The models show excellent agreement with experimental data (R2 = 0.9454 and 0.9448, R adj 2 = 0.9400 and 0.9413). Internal validation via leave-one-out cross-validation yields R cv 2 = 0.9308 and 0.9364, while external validation using an independent test set yields Q ext 2 = 0.8939 and 0.9364, indicating strong robustness and superior predictive performance compared to previously reported models. Molecular geometries were optimized using HyperChem 8.0, employing MM + and PM3 methods. Molecular descriptors were calculated using the Online Chemical Modeling Environment (OCHEM). Binary mixture descriptors were derived from pure-component descriptors via Kay's mixing rule. The genetic function approximation (GFA) algorithm was used to select the most relevant descriptors, and predictive models were constructed using multiple linear regression (MLR). Model robustness and predictive capacity were evaluated using leave-one-out cross-validation and an external test set, with applicability domains assessed via Williams plots. All computational procedures and modeling analyses were performed using OCHEM, SPSS, and HyperChem 8.0.
Non-saponin constituents of Panax species, including amino acids, sugars, and nucleosides, have attracted increasing attention due to their nutritional relevance and potential health benefits in food-medicine homologous materials. However, their high polarity and chemical diversity pose significant challenges for comprehensive characterization, limiting their application in food quality evaluation and authenticity assessment. In this study, an integrated mass spectrometry-based workflow was developed to systematically profile water-soluble non-saponins in Panax ginseng, Panax notoginseng, and Panax quinquefolius, which are widely consumed as functional foods, dietary supplements, and traditional herbal products. An offline two-dimensional LC-MS platform was first established to improve the separation and enrichment of polar constituents, followed by feature-based molecular networking (FBMN) and machine learning-assisted structural annotation using the SIRIUS platform. A total of 201 non-saponin compounds were characterized, revealing remarkable interspecies differences in non-saponin composition. Subsequent non-targeted metabolomics identified quinic acid, raffinose, and trehalose as key species-specific markers with nutritional and quality-discriminating relevance. Furthermore, desorption electrospray ionization mass spectrometry imaging (DESI-MSI) was employed to visualize the spatial distribution of representative non-saponins, uncovering tissue-specific accumulation patterns associated with species identity. Finally, a portable nano-electrospray ionization miniature mass spectrometry (nESI-Mini MS) approach was developed for rapid species authentication, enabling high-throughput, on-site analysis with minimal sample preparation. Overall, this study provides an integrated analytical strategy for elucidating the chemical diversity, spatial distribution, and food-related quality attributes of non-saponin constituents in Panax species, offering practical tools for functional food evaluation, authenticity verification, and quality control.
Surface-enhanced Raman scattering (SERS) offers ultrasensitive molecular fingerprinting, yet most current substrates rely on static architectures with fixed nanostructures, which undermines their quantitative reliability and prevents conformal sensing. Here, we introduce a reconfigurable and conformable SERS platform based on Au-coated shape memory polymer (SMP) nanopillar arrays that reversibly switch out-of-plane (vertical) Au─Au nanogaps. The substrate comprises Au-coated SMP nanopillars embedded with silver nanoparticles (AgNPs) that serve as photothermal triggers and mechanical reinforcements. Compression and laser-induced recovery of the nanopillars reversibly modulate the vertical Au─Au nanogaps from wide to narrow states, thereby tuning near-field coupling, hotspot density, and molecular accessibility. This reversible out-of-plane reconfiguration enables the same platform to support detection of analytes across different size scales. The size-adaptive sensing capability is demonstrated by the detection of a macromolecule (hemoglobin, 64 kDa) on curved stainless-steel surfaces in the wide-gap state and of a small molecule (thiram, 0.24 kDa) on fruit surfaces in the narrow-gap state. The platform also maintains performance over repeated compression-recovery and washing-measurement cycles, confirming reusability and operational stability. Overall, this adaptive nanogap-switching strategy offers a practical route toward reusable, size-adaptive SERS platforms for next-generation food safety and forensic chemical analysis.
Accurate, continuous monitoring of psychophysiological states is central to understanding stress and autonomic dysfunction across diverse medical contexts. Current approaches such as polygraphy and polysomnography rely on cumbersome, wired sensors that limit real-world utility and burden patients, particularly vulnerable populations such as infants. Here, we introduce a wireless, skin-interfaced multimodal sensing system capable of simultaneously recording cardiac, respiratory, electrodermal, and thermal signals in a time-synchronized manner. Leveraging compact and soft designs, the technology enables unobtrusive monitoring across controlled, clinical, and naturalistic settings. Validation studies performed in parallel with gold standard systems demonstrate high fidelity in quantifying stress responses during polygraph interviews, cognitive load tasks, and cold pressor tests. In pediatric sleep studies, the data reliably identify arousals, hypopnea, and apnea while revealing disease-specific autonomic signatures in infants with Down syndrome. Real-world deployment during emergency simulation training shows that multimodal stress signatures correlate inversely with performance, underscoring translational value in medical education. Machine learning analyses across all studies confirm that multimodal features outperform single-signal approaches in detecting stress and clinical events with high sensitivity and specificity. Collectively, these findings establish the technology as a next-generation wearable platform that bridges engineering innovation and clinical practice, offering mechanistic insight and diagnostic potential in stress medicine, sleep medicine, and beyond.
Molecular dynamics (MD) simulations were employed to unravel the atomistic mechanisms responsible for the selective permeation of cobalt (Co2+) and mercury (Hg2+) ions through chemically functionalized nanoporous graphene (GRA) membranes. The computational framework consisted of nanoporous GRA membranes functionalized with electronegative fluorine (-F) and chlorine (-Cl) moieties and immersed in mixed aqueous nitrate environments. An external electric field applied along the membrane normal induced directed ionic migration across the pores. Detailed structural and dynamical analyses reveal that ion transport is dictated by a delicate balance among hydration free energy, ion-pore electrostatic interactions, and interfacial polarization effects. The F-functionalized nanoporous GRA membranes have been shown to promote enhanced ion transport when subjected to an external electric field. Notably, Co2+ ions exhibit preferential permeation through F-functionalized pores, whereas Hg2+ ions demonstrate higher permeation efficiency in Cl-functionalized pores. These findings provide a fundamental molecular-level understanding of how functional group chemistry and applied electric fields modulate ion selectivity and transport energetics in GRA-based membranes with tailored pore diameters, offering predictive insights for the rational design of next-generation nanofiltration and electroseparation systems.
Classification of vehicle engines using the chemical composition of the exhaust from these engines can be used to identify the engine's design and verify compliance with environmental regulations through the vehicle's emissions. This paper describes a method to identify the type of vehicles using machine learning (ML), where low-cost MQ series sensors measure the gases and particle emissions from a vehicle exhaust system, while simultaneously collecting and measuring the vehicle's temperature and humidity levels. A custom-designed multi-sensor exhaust sensing module is employed to capture real-time exhaust emissions prior to entering the atmosphere. Exhaust samples are collected from vehicles representing three major engine categories: petrol, diesel, and compressed natural gas (CNG). In addition, fresh air samples are collected as a baseline environmental reference for comparison. All exhaust measurements are collected under controlled and consistent engine operating conditions to ensure comparable emission profiling across vehicle classes. To ensure consistent combustion-based emission profiling, this study focuses on conventional fuel-powered vehicles. MQ-series gas sensors are sensitive to combustion by-products emitted during engine operation, such as carbon monoxide (CO), hydrocarbons (HC), while also exhibiting cross-sensitivity to other gaseous components present in exhaust mixtures. Nevertheless, the proposed system performs pattern-based classification using relative sensor response signatures. Standardization of data is achieved through z-score normalization. The best models developed (based on three separate experimental designs) are trained and validated using six supervised machine learning algorithms such as Logistic Regression, Support Vector Machine (RBF), k-Nearest Neighbors, Random Forest, Gradient Boosting Decision Tree, and XGBoost and are compared against one another. Evaluation of the tested algorithms using various evaluation metrics demonstrated that ensemble models outperformed all other algorithms, achieving the highest accuracy of 99.5%. Furthermore, noise analysis confirms that the proposed solution maintains high classification accuracy (more than 89%) even under substantial sensor perturbations mimicking the real-world deployment. The solution proposed below illustrates how using gas sensors and advanced algorithms can provide accurate exhaust identification and identify engines in real-time.
Physics-inspired machine learning (ML) models can be categorized into two classes: those relying solely on three-dimensional structure and those incorporating electronic information. In this work, we benchmark both classes for predicting quantum-chemical properties of transition metal complexes with diverse charge and spin states, using three complementary datasets. The evaluated methods include molecular representations (SLATM, FCHL, SOAP, and SPAHM family) combined with kernel ridge regression, as well as geometric deep learning models (MACE and 3DMol). We examine how the inclusion of electronic information affects predictive accuracy across datasets and target properties. Models that incorporate electronic information consistently outperform purely structure-based models for properties whose distributions are strongly governed by electronic characters, such as spin-splitting energies and frontier orbital energies. In contrast, structure-only models perform well for predicting the HOMO-LUMO gap and dipole moment magnitude, whose distributions are relatively insensitive to electronic characteristics. Geometric deep learning models with charge and spin embeddings (MACE-QS and 3DMol-QS) show the highest overall accuracy, with 3DMol offering the best computational efficiency among the tested models. These results clarify when geometric information is sufficient and when incorporating electronic information becomes essential, providing practical guidance for selecting effective physics-based ML models for transition metal complexes.
Neuromorphic computing inspired by mammalian intelligence aims to emulate the nonlinear dynamics of biological neurons and synapses to achieve fast, low-energy, and highly efficient information processing. Brain-inspired computing relies on the design and discovery of materials exhibiting nonlinear current-voltage profiles, frequently underpinned by electronic state transitions, to achieve spiking neurons and dynamically tunable synapses. A signature challenge in the design of artificial neurons is controlling the steepness of first-order transitions in active elements, as abrupt transitions are at risk of driving unstable voltage and temperature oscillations, which result in catastrophic device failure. A critical knowledge gap is the lack of structure-function correlations mapping the composition and atomistic structure of crystalline solids to nonlinear dynamical response characteristics. Here, we address the key question of how modification of atomistic structure correlates with alteration of neuron-like functionality. Constructing oscillator circuits from millimeter-scale single crystals enables high-resolution atomic structure solutions, which we use to demonstrate that the selective positioning of Pb cations modifies charge ordering along a one-dimensional CuxV2O5 framework even at low insertion stoichiometries, thereby providing an atom-precise design parameter for damping first-order transitions. We use temperature-variant X-ray diffraction and X-ray spectroscopy to elucidate the suppression of Cu-ion shuttling based on the precise positioning of Pb ions in seven-coordinated tunnel interstitial sites as the mechanistic basis for transition broadening, thus bridging a critical gap between statistical mechanics and quantum chemical descriptions of phase transitions. Such mechanistic understanding thus paves the way to site-selective modification strategies for modulating the sharpness of first-order transitions, with an exemplary demonstration here in tuning neuronal signal processing.