jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yinsheng Liu | Rui Xue | Kai Zhu | Bing Yan
Source: Angewandte Chemie (article, 2025-02-12)
Field: Materials Science — Materials Chemistry

σ‐π Hyperconjugation as a Design Strategy for High‐Performance Fluorescent Covalent Organic Frameworks Abstract Realizing efficient solid‐state fluorescence in covalent organic frameworks (COFs) represents a persistent and fundamental challenge in materials science, hindering their application in next‐generation optoelectronics. Herein, a transformative strategy leveraging σ‐π hyperconjugation is introduced to enhance the fluorescence properties of COFs. The incorporation of a methyl group into a pyrene‐based COF monomer yields an unprecedented fluorescence quantum yield increase of more than 100‐fold, accompanied by significantly enhanced optoelectronic performance. This enhancement arises from σ‐π hyperconjugation, which redistributes electron density, converting pyrene from an electron donor to an electron acceptor and facilitating efficient electronic transitions. Additionally, a COF‐based fluorescent acoustic sensor, fabricated via electrospinning, demonstrates real‐time speech pattern recognition when integrated with machine learning algorithms, showcasing potential in language‐assistive technologies. This study pioneers the exploration of hyperconjugation in COFs, offering a new paradigm for the precise modulation of optoelectronic properties and bridging fundamental research with real‐world applications. Fluorescence ; Covalent bond ; Chemistry ; Hyperconjugation ; Nanotechnology ; Organic chemistry ; Materials science ; Molecule ; Physics Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Conducting polymers and applications
Authors: Abderrahmane Aït‐Kaddour | Hassan M.A. Abdelbaky | Shaimaa Hamdy | Tahar Boubellouta | S. Abouelkaram | Hani Sh. Abdelmentolb
Source: Journal of Food Composition and Analysis (article, 2025-06-24)
Field: Chemistry — Analytical Chemistry

Discrimination of thermally treated milk using fluorescence spectroscopy combined with PCA and artificial neural networks This study assessed the use of multilayer perceptron artificial neural networks (MLP-ANN) combined with fluorescence spectroscopy to classify milk samples by species (cow or buffalo), thermal treatment, and cold storage duration. Models were developed using different fluorescence inputs: Vitamin A, tryptophan, synchronous spectra (80 nm offset), and a Vitamin A–tryptophan combination. Model performance was evaluated based on classification accuracy, mean square error (MSE), and training efficiency. Principal component analysis (PCA) was also employed to explore the effects of cooling, thermization, and pasteurization on milk’s physicochemical properties. PCA results showed clear clustering of samples based on species, thermal treatments, and storage times, with associated differences in pH, lactose, acidity, nitrogen fractions, calcium, and clotting time. MLP-ANN models achieved classification accuracies ranging from 44% to 100% and MSE values from 0% to 14%, depending on input features and classification complexity. Synchronous fluorescence spectra yielded the highest performance, achieving 100% accuracy in distinguishing both species and thermal treatments, highlighting their sensitivity to heat-induced molecular changes. Simpler classification tasks required fewer training epochs and hidden neurons, while more complex tasks involving multiple variables demonstrated lower performance, particularly when using combined tryptophan–Vitamin A inputs. • PCA clustered milk samples by species, treatment, and storage time based on physicochemical features. • Fluorescence spectroscopy has proved to be a reliable method to detect thermal treatments of milk. • MLP-ANN with fluorescence was useful to differentiate milk based on their storage time, thermal history and animal origin. • MLP-ANN with synchronous, Vit A or tryptophan spectra and their combinations gave 100% success classification rate for each milk classes. Fluorescence spectroscopy ; Artificial neural network ; Fluorescence ; Spectroscopy ; Principal component analysis ; Pattern recognition (psychology) ; Artificial intelligence ; Chemistry ; Biological system ; Computer science ; Biology ; Optics ; Physics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Meat and Animal Product Quality
Authors: Nuanfei Zhu | Yixing Tian | Sinuo Tao | Zhihua Qiao | Zhugen Yang | Ligang Hu | Jingfu Liu | Zhen Zhang
Source: Environmental Science & Technology Letters (article, 2025-06-16)
Field: Chemistry — Analytical Chemistry

Chromium Speciation Monitoring Platform for Drinking Water: Machine Learning-Assisted Dual-Emission Fluorescence Sensor Array Different chromium (Cr) speciation in drinking water shows distinct risk levels to humans, failing to reflect real environmental impacts only by total Cr analysis. Integrated with machine learning, a novel fluorescence sensor array was developed for rapid identification and quantitative detection of Cr speciation without sample pretreatment other than filtration. This system prepared three-component fluorescence hybrid materials (MSN@Zr@Au and MSN@Zr@AgAu) with dual emission wavelengths. The sensing unit with a dual-mode algorithm was specific for Cr speciation and accurately identified chromium speciation among 11 coexisting cations. The algorithm of linear discriminant analysis (LDA) assisting hierarchical cluster analysis (HCA) provided higher selectivity for Cr speciation for real samples. Finally, this method showed good analytical performance ranging from 1 to 60 μM, exhibiting a low detection limit of 1.29 μM. This strategy shows excellent practicability for Cr speciation analysis in drinking and tap water, developing a practical monitoring platform for real water. Fluorescence ; Chromium ; Genetic algorithm ; Dual (grammatical number) ; Environmental chemistry ; Materials science ; Environmental science ; Chemistry ; Remote sensing ; Physics ; Geology ; Metallurgy ; Optics ; Ecology ; Biology ; Art Analytical chemistry methods development ; Advanced biosensing and bioanalysis techniques ; Biosensors and Analytical Detection
Authors: Zhicheng Zong | Yangjun Qin | Jiahong Zhan | Haisheng Fang | Nuo Yang
Source: International Journal of Heat and Mass Transfer (article, 2025-12-02)
Field: Materials Science — Materials Chemistry

Machine learning potential for predicting thermal conductivity of θ-phase and amorphous tantalum nitride Thermal conductivity ; Amorphous solid ; Thermal ; Molecular dynamics ; Tantalum ; Thermal conduction Thermal properties of materials ; Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research
Authors: Kaveh Rahimi Mamaghani | Nader Parvin
Source: Journal of Alloys and Compounds Communications (article, 2025-09-11)
Field: Materials Science — Materials Chemistry

Cold sintering for quantum ceramics: A low-temperature route to topological and spin–orbit-coupled materials Fabrication ; Sintering ; Ceramic ; Dopant ; Topology (electrical circuits) ; Quantum ; Doping ; Spintronics Ferroelectric and Piezoelectric Materials ; Topological Materials and Phenomena ; Advanced Condensed Matter Physics
Authors: Xu Zhang | Yuanhao Gong | Fei Xie | Ping Sun | Saihua Jiang
Source: ACS Applied Materials & Interfaces (article, 2025-06-24)
Field: Materials Science — Materials Chemistry

Dual-Mode Temperature–Pressure MXene Sensor for Enhanced Firefighter Safety and Deep Learning-Enhanced Smart Gloves Dual-mode sensors capable of detecting multiple physical stimuli simultaneously offer significant advantages for advanced applications in human-machine interaction, robotics, and healthcare. However, the flammability of conventional materials limits their widespread adoption. The recently developed two-dimensional transition metal carbide (MXene) combines exceptional thermoelectric properties, metallic-like conductivity, and flame retardancy, making it a promising solution to these limitations. In this study, we present multifunctional sensors designed to accurately detect temperature and pressure stimuli by integrating MXene sheets onto flexible, fire-resistant polyimide (PI) substrates. These sensors demonstrate excellent performance in both temperature and pressure sensing, making them ideal for smart firefighting suits, which can monitor firefighters' health in real time. Moreover, when integrated with deep-learning algorithms, the sensors, assembled into smart gloves, can recognize objects of varying weights and temperatures. This work provides a simple and effective method for preparing multifunctional wearable sensors capable of distinguishing multiple stimuli across diverse environments. Materials science ; Dual mode ; Pressure sensor ; MXenes ; Dual (grammatical number) ; Mode (computer interface) ; Composite material ; Nanotechnology ; Automotive engineering ; Optoelectronics ; Mechanical engineering ; Electronic engineering ; Computer science ; Human–computer interaction ; Engineering MXene and MAX Phase Materials ; Advanced Sensor and Energy Harvesting Materials ; Graphene research and applications
Authors: Yuqi Wang | Jinrong Huang | Guiyan Zhao | Rui Chen | Jinrui Huang | Xiaohua Chang | Jiusheng Li | Yutian Zhu
Source: Composites Communications (article, 2025-10-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Flexible conductive hydrogels through optimized interfacial connectivity of hybrid fillers toward efficient electromagnetic interference shielding and wearable strain sensors Electromagnetic shielding ; EMI ; Self-healing hydrogels ; Electromagnetic interference ; Electrical conductor ; Composite number ; Wearable computer ; Carbon nanotube ; Gauge factor Electromagnetic wave absorption materials ; Advanced Sensor and Energy Harvesting Materials ; Dielectric materials and actuators
Authors: G. Sneha | R.D. Eithiraj
Source: Results in Engineering (article, 2025-06-20)
Field: Materials Science — Materials Chemistry

Unlocking the structural, vibrational, electronic, optical and thermoelectric properties of K2X (X=S, Se, Te) monolayers via DFT and ML Two-dimensional alkali-metal chalcogenides with direct and indirect band gaps are designed for applications in electronics, optoelectronics, thermoelectrics and spintronics through ab-initio methods. Employing the ab-initio structural, electronic, optical and thermoelectric calculations for the series of dipotassium chalcogenide K2X (X = S, Se, Te) monolayers, intriguing results have been obtained. The computational analysis that involves density functional theory (DFT) was achieved by WIEN2k software. The obtained results reveal that the K2X (X = S, Se, Te) trigonal monolayers are indirect band semiconductors with electronic band gaps of 2.00 eV, 1.95 eV and 2.01 eV. Covering the studied optical features of K2X (X = S, Se, Te) monolayers, the dielectric functions and absorption coefficient have been computed and analysed up to the energy range of 10 eV. These monolayers show broad absorption band in the order of 104 cm-1 in the UV–Vis region and may appear to have applications in optoelectronics. The thermoelectric features as a function of temperature show better thermoelectric performance with inclusiveness of lattice thermal conductivity. AFLOW-PLMF model was deployed for the electronic band gap value predictions. Machine learning algorithms namely Decision Tree Regressor and Gradient Boosting Regressor out-performs the other ML models with least root mean square error (RMSE) and accurate R2 values. The K2Se monolayer shows favourable ZT value than K2S and K2Te making it a desirable one for thermoelectric applications. Monolayer ; Thermoelectric effect ; Materials science ; Condensed matter physics ; Optoelectronics ; Crystallography ; Computational chemistry ; Nanotechnology ; Chemistry ; Physics ; Thermodynamics 2D Materials and Applications ; Chalcogenide Semiconductor Thin Films ; Inorganic Chemistry and Materials
Authors: Nan Feng | Hao‐Cheng Thong | Ke Wang | Ke Bi | Ben Xu
Source: Physical review. B./Physical review. B (article, 2025-01-13)
Field: Materials Science — Materials Chemistry

Atomic-scale polarization switching driven by terahertz light in ferroelectric <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msub><mml:mi>KNbO</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:mrow></mml:math> Nonlinear phononics in the terahertz fields offers transformative potential for novel optoferroic devices by enabling precise control over ferroelectric polarization in ferroelectrics via anharmonic phonon couplings. The atomic-scale response of ferroelectrics to terahertz fields is closely linked to the dynamics of multimode nonlinear interactions, and real-time structural and polarization evolution. However, capturing these dynamics experimentally and theoretically remains a challenge. Herein, we propose a molecular-dynamics-based approach, incorporating machine learning interatomic potentials, to realize accurate simulations of the real-time polarization switching behavior of ferroelectric ${\mathrm{KNbO}}_{3}$, capturing the full anharmonicity. We highlight the capabilities of the deep potential in predicting anharmonic features of density functional theory. We report that the ferroelectric mode, driven by the ${A}_{1}$ mode (at \ensuremath{\sim}17.56 THz) through the quadratic-linear coupling, initiates a fully switched state within 1 ps. Our observation enables an alternative route to investigating ultrafast polarization dynamics for terahertz excitation and may advance energy-efficient optoferroic applications. Ferroelectricity ; Terahertz radiation ; Polarization (electrochemistry) ; Scale (ratio) ; Physics ; Optoelectronics ; Chemistry ; Quantum mechanics ; Physical chemistry Ferroelectric and Piezoelectric Materials ; Photorefractive and Nonlinear Optics ; Acoustic Wave Resonator Technologies
Authors: Xue‐Qiang Zhang | Xihang Zhang | Jin Hui | Qingjun Zhou | Fuchun Zhang | Xinghui Liu
Source: Rare Metals (article, 2025-07-01)
Field: Chemistry — Inorganic Chemistry

Unraveling the catalytic potential of two‐dimensional conjugated metal–organic frameworks based on hexaazanaphthalene: machine learning‐driven insights into the origin of oxygen evolution‐reduction activity Abstract Inspired by hexaazanaphthalene‐based conjugated copper metal–organic framework (HATNA‐Cu‐MOF), we designed 161 HATNA‐TM‐MOF‐based SACs (TM@N x O 4− x ‐HATNA) with varying TM or ligands creating distinct coordination environments ( x = 0–4) with superior thermodynamic and electrochemical stabilities. Volcano plots can be constructed using (Δ G OOH* − Δ G O* )/Δ G O* as descriptors for oxygen evolution/reduction reaction (OER/ORR) activity, also serving as target parameters for machine learning (ML) models to identify high‐performance OER/ORR catalysts. The efficient monofunctional and bifunctional electrocatalysts were successfully predicted, where the ML prediction results well matched the DFT calculation results. We employed Shapley additive explanations (SHAP) for feature analysis and utilized sure independence screening and sparsification operator (SISSO) for generalization. ML analyses reveal that TM‐based OER/ORR activities predominantly correlate with three key descriptors: metallic atomic radius, d‐orbital electron population, and the heat of formation of the oxide, demonstrating the pivotal role of TM’s inherent electronic configuration and physicochemical characteristics in governing electrocatalytic efficacy. The constant‐potential approach emphasizes the key role of electric double‐layer capacitance in adjusting the kinetic barrier, where changes in the Fermi level influence the occupation of d‐orbitals. Variations in electrochemical potential significantly alter the electronic structure of representative Rh@N 1 O 3 ‐HATNA, affecting both the Fermi level and adsorption properties, with the unique 4d 8 5s 1 configuration leading to inverted O 2 adsorption energies as the potential decreases. This study contributes insights into the origin of oxygen evolution‐reduction activity for the HATNA‐TM‐MOF‐based SACs and reveals the fundamentals of structure–activity relationships for future applications. Conjugated system ; Materials science ; Oxygen reduction ; Catalysis ; Oxygen reduction reaction ; Reduction (mathematics) ; Metal ; Oxygen evolution ; Nanotechnology ; Oxygen ; Chemical engineering ; Metallurgy ; Organic chemistry ; Physical chemistry ; Polymer ; Chemistry ; Composite material ; Engineering ; Electrochemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Abiyot Kelecha Geletu | Kokob Teshome Wondimu | Ibrahim Umer Keru | Tesfalem Atnafu Woldegebreil | Megersa Abdisa Debelo
Source: Regional Studies in Marine Science (article, 2025-07-18)
Field: Materials Science — Materials Chemistry

Trends in monitoring of emerging contaminants: Advances and challenges in detection and assessment Environmental science ; Contamination ; Environmental planning ; Environmental resource management ; Environmental protection ; Ecology ; Biology Nanoparticles: synthesis and applications ; Microplastics and Plastic Pollution ; Recycling and Waste Management Techniques
Authors: Yucan Yang | Man Chen | Mingrui Chen | Xiao Dong Chen | Xu Zhi | Leiqing Pan | Weijie Lan
Source: Food Research International (article, 2025-02-21)
Field: Chemistry — Analytical Chemistry

Deep learning combined Monte Carlo simulation reveal the fundamental light propagation in apple puree: Monitoring the quality changes from different cultivar, storage period and heating duration Duration (music) ; Cultivar ; Monte Carlo method ; Period (music) ; Quality (philosophy) ; Food science ; Environmental science ; Statistics ; Chemistry ; Mathematics ; Horticulture ; Biology ; Physics Spectroscopy and Chemometric Analyses ; Horticultural and Viticultural Research ; Greenhouse Technology and Climate Control
Authors: Beiping Luo | Liang Xiao-li | Hong Ruan | Yuqi Li | Zhen Wen
Source: Advanced Functional Materials (article, 2025-10-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Bioinspired Asymmetric Porous Natural Rubber Films: Wearables for Electromagnetic Shielding, Fetal Monitoring, and Personal Thermal Management Abstract Materials that integrate flexibility, electromagnetic shielding, and Joule heating are crucial for the development of multifunctional wearable technologies. Inspired by the trabecular structure, this study used natural rubber as a matrix and added sodium alginate‐modified carbon nanotubes. A multifunctional composite (SCN) with a bone trabecular‐like asymmetric porous structure is prepared via dynamic crosslinking and a synergistic microphase separation‐induced effect. The asymmetric porous SCN exhibited exceptional tensile properties (7.75 MPa tensile strength, 445% elongation at break), excellent low‐strain sensing performance (gauge factor (GF) up to 3.46 at 1–10% strain), rapid Joule heating (63.2% thermal saturation in &lt;9 s), and effective electromagnetic interference (EMI) shielding (22.7 dB in the X‐band for a 109‐µm film). Machine learning achieved 95.56% accuracy in differentiating fetal‐movement frequencies, indicating the potential of SCN for fetal health monitoring. An SCN‐based thermal management system developed by a microcontroller unit (MCU) rapidly heated the film surface temperature to 35.4 °C within 30 s at 3 V. Furthermore, SCN composites exhibited antibacterial efficacy against Staphylococcus aureus (99.70% inhibition) and Escherichia coli (99.30% inhibition). These properties make the multifunctional asymmetric porous SCN a promising material for next‐generation wearable electronics, offering EMI shielding, personal temperature management, and health monitoring. Natural rubber ; Porosity ; Ultimate tensile strength ; Porous medium ; Composite number ; Electromagnetic shielding ; Wearable computer ; Thermal Electromagnetic wave absorption materials ; Advanced Sensor and Energy Harvesting Materials ; Dielectric materials and actuators
Authors: Bonface Orero | Freeman Ntuli | Caliphs Zvinowanda | Thandiwe Sithole | Tebogo Mashifana
Source: Next Materials (article, 2025-08-30)
Field: Chemistry — Organic Chemistry

Sustainable synthesis of activated carbon from water hyacinth via one-step carbonization-H3PO4 activation: Optimization and modeling via RSM-artificial intelligence approach This study explores the green synthesis of activated carbon (AC) from water hyacinth (WH), an invasive aquatic weed, using a one-step H₃PO₄ activation method, addressing both environmental waste management and sustainable material production. The present work aimed to optimize AC yield and surface area by evaluating the effects of impregnation ratio (0.4–1.0), temperature (600–800°C), and activation time (60–120 mins) via Response Surface Methodology (RSM) and advanced Artificial Intelligence (AI) techniques, including Artificial Neural Networks (ANN), ANN-Particle Swarm Optimization (PSO-ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and XGBoost. Characterization via XRD, FTIR, SEM, and BET confirmed the AC’s amorphous structure, functional groups, and porous morphology. RSM revealed that a two-factor interaction (2FI) model best described yield (R² = 0.9844), while a quadratic model fit surface area (R² = 0.9992). AC with a high surface area of 1025 m²/g and a yield of 44.5 % was achieved under optimum conditions (impregnation ratio of 0.785, temperature of 734°C, and time of 72 mins). The overall yield and surface area prediction accuracy followed the PSO-ANN>ANN>ANFIS>XGBoost order. PSO-ANN outperformed other models (R² = 0.9990 for surface area and 0.9767 for yield). The surface area was predicted more accurately than the yield response due to its less complex relationship with inputs. Based on cost constraints and performance requirements, the trade-off between yield and surface area is fundamental in AC production. This study offers a paradigm shift of invasive WH management, using it as a sustainable source of AC for industrial applications. • Invasive water hyacinth proved viable as activated carbon (AC) precursor. • One-step H 2 PO 4 activation produced AC: yield (44.5 %) and surface area (1025 m²/g). • Yield (56 %) and surface area (72 %) greatly depend on impregnation ratio. • Prediction accuracy followed PSO-ANN>ANN>ANFIS>XGBoost order. • Surface area was predicted more accurately by all models than yield response. Hyacinth ; Carbonization ; Activated carbon ; Process engineering ; Response surface methodology ; Carbon fibers ; Chemical engineering ; Pulp and paper industry ; Biochemical engineering ; Materials science ; Environmental science ; Computer science ; Waste management ; Engineering ; Chemistry ; Organic chemistry ; Machine learning ; Adsorption ; Composite material ; Composite number Nanomaterials for catalytic reactions ; Adsorption and biosorption for pollutant removal ; Ammonia Synthesis and Nitrogen Reduction
Authors: Mahantesh Khetri | Pawan K. Kanaujia | Mool C. Gupta
Source: Renewable Energy (article, 2025-06-09)
Field: Materials Science — Surfaces, Coatings and Films

Laser recycling of silver in bulk and nanoparticle form from silicon solar cells and deep learning for process automation Automation ; Silver nanoparticle ; Laser ; Process (computing) ; Silicon ; Materials science ; Nanoparticle ; Nanotechnology ; Engineering physics ; Process engineering ; Engineering ; Manufacturing engineering ; Computer science ; Optoelectronics ; Mechanical engineering ; Optics ; Physics Electron and X-Ray Spectroscopy Techniques ; Laser-Ablation Synthesis of Nanoparticles ; TiO2 Photocatalysis and Solar Cells