jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yuening Wang | Lin Qiu | Jian Yu | Wen Ma | Xiaoyu Song | Dongke Zhang | Yujiao Xie | Aochi Liu | Li Sun | Xiangyu Meng | Jie Lin | Xiaotian Wang
Source: Rare Metals (article, 2026-01-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Progress in Surface‐Enhanced Raman Scattering‐Based Volatile Organic Compounds Detection: Materials, Application, and Prospects ABSTRACT The detection of volatile organic compounds (VOCs) holds significant implications in environmental monitoring and disease diagnosis. Traditional gas detection technologies are constrained by complex operation and high cost, thereby failing to satisfy real‐time detection demands. Surface‐enhanced Raman scattering (SERS) provides a noble approach for trace VOCs detection, as it possesses single‐molecule sensitivity, rapid response, and the ability to analyze chemical structures without being affected by water molecules. Researchers have successfully achieved precise identification of trace VOCs through the meticulous design of SERS substrates, demonstrating excellent application potential in practical detection. This review comprehensively summarizes the research progress and application of SERS technology in VOCs detection, covering the structural design of SERS substrates and the transformation of actual gas detection methods. Specifically, three core substrate structures include noble metal nanostructures, porous semiconductor composite nanostructures, and noble metal–semiconductor composite porous nanostructures that combine the advantages of both were delved into deeply. Furthermore, it also provides a detailed account of the technological innovations in VOCs detection based on SERS technology, expanding the application scope of SERS technology. Nevertheless, the SERS technology still faces significant challenges in VOC gas detection, including nonspecific adsorption in complex matrices, insufficient long‐term substrate stability, the need to simultaneously identify multiple components in a mixed gas, etc. This review summarizes the current challenges in detail and looks forward to future research directions and development prospects. Substrate (aquarium) ; Raman scattering ; Noble metal ; Scope (computer science) ; Adsorption ; TRACE (psycholinguistics) ; Raman spectroscopy ; Nanostructure Gold and Silver Nanoparticles Synthesis and Applications ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Malvika Viswanathan | Leqi Yin | Yashwant Kurmi | You Cun Chen | Xiaoyu Jiang | Junzhong Xu | Aqeela Afzal | Zhongliang Zu
Source: Physics in Medicine and Biology (article, 2026-02-03)
Field: Materials Science — Materials Chemistry

A rapid and accurate guanidine CEST imaging in ischemic stroke using a machine learning approach <i>Objective.</i>Rapid and accurate mapping of brain tissue pH is crucial for early diagnosis and management of ischemic stroke. Amide proton transfer (APT) imaging has been used for this purpose but suffers from hypointense contrast and low signal intensity in lesions. Guanidine chemical exchange saturation transfer (CEST) imaging provides hyperintense contrast and higher signal intensity in lesions at appropriate saturation power, making it a promising complementary approach. However, quantifying the guanidine CEST effect remains challenging due to its proximity to water resonance and the influence of multiple confounding effects. This study presents a machine learning (ML) framework to improve the accuracy and robustness of guanidine CEST quantification with reduced scan time.<i>Approach.</i>The model was trained on<i>partially synthetic data</i>, where measured line-shape information from experiments were incorporated into a simulation framework along with other CEST pools whose solute fraction (<i>f</i><sub>s</sub>), exchange rate (<i>k</i><sub>sw</sub>), and relaxation parameters were systematically varied. Gradient-based feature selection was used to identify the most informative frequency offsets to reduce the number of acquisition points.<i>Main results.</i>The proposed model achieved significantly higher accuracy than polynomial fitting, multi-pool Lorentzian fitting, and ML models trained solely on synthetic or<i>in vivo</i>data. Gradient-based feature selection identified the most informative frequency offsets, reducing acquisition points from 69 to 19, a 72% reduction in CEST scan time without loss of accuracy.<i>In vivo</i>, conventional fitting methods produced unclear lesion contrast, whereas our model predicted clear hyperintense lesion maps. The strong negative correlation between guanidine and APT effects supports its physiological relevance to tissue acidosis.<i>Significance.</i>The use of partially synthetic training data combines realistic spectral features with known ground-truth values, overcoming limitations of purely synthetic or limited<i>in vivo</i>datasets. Leveraging this data with ML, enables robust quantification of guanidine CEST effects, showing potential for rapid pH-sensitive imaging. Pattern recognition (psychology) ; Guanidine ; Magnetic resonance imaging ; Feature selection ; Robustness (evolution) ; Magnetization transfer ; Feature (linguistics) ; Neuroimaging Lanthanide and Transition Metal Complexes ; Magnetism in coordination complexes ; Electron Spin Resonance Studies
Authors: Alexander Meßler | Hilke Bahmann
Source: Journal of Chemical Information and Modeling (article, 2026-01-29)
Field: Materials Science — Materials Chemistry

Uncertainty-Aware Prediction of <sup>195</sup> Pt Chemical Shifts from Limited Data Platinum (Pt) complexes are highly relevant for medicinal chemistry and homogeneous catalysis. In the development of novel Pt-based chemotherapeutic agents and catalysts, characterization of the compounds using nuclear magnetic resonance (NMR) spectroscopy of the <sup>195</sup>Pt nucleus is standard. However, measuring <sup>195</sup>Pt-NMR signals can be tedious due to the large chemical shift range and limited resolution. To facilitate experimental measurements by narrowing down the shift range, reliable predictions of the chemical shift are needed. Especially for lighter nuclei such as <sup>1</sup>H and <sup>13</sup>C, machine learning (ML) methods predict chemical shifts accurately, while analogous models for heavier nuclei are scarce. In this work, we propose Gaussian Process Regression (GPR) models for the prediction of <sup>195</sup>Pt chemical shifts. The underlying data set comprises 292 structures and three different descriptors were used to encode structural and chemical features of the molecules. Based on the prediction uncertainties derived from the posterior variance of the models, a reasonably narrow shift range can be estimated for a given Pt complex. The most robust model yields a mean absolute error (MAE) of 114 ppm on the holdout test set, which is significantly more accurate than relativistic DFT calculations. Chemical shift ; Gaussian ; Gaussian process ; Range (aeronautics) ; Chemical process ; Regression ; Variance (accounting) ; Kriging ; Test set ; Linear regression Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning in Healthcare
Authors: Sebin Kariachan | Joshin Shibu | Prajitha Velayudhan | Sisanth Krishnageham Sidharthan | Pravitha Velayudhan | Sanu Mathew Simon | Hitoshi Kasai | Kohei Okubo | Sabu Thomas | Kouki Oka | Jibin Keloth Paduvilan
Source: RSC Advances (article, 2026-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Exploring the advances in 2D materials as a quest for energy storage electrode materials The increasing demand for effective energy storage solutions has spurred research into innovative materials with exceptional properties. Among these, two-dimensional (2D) materials have become popular alternatives due to their unique mechanical, chemical, and electrical characteristics. This review highlights recent advances in using 2D materials-such as graphene, MXenes, and transition metal dichalcogenides (TMDs) for energy storage devices like batteries and supercapacitors. Graphene, known for its high conductivity and surface area, enhances charge storage when used as an anode. MXenes, a newer class with variable compositions, provide high capacitance for both anodes and cathodes. The large surface area and capacitance of graphene, along with the flexibility of MXenes, offer promising advantages. Additionally, combining 2D materials with carbon nanotubes and metal nanoparticles further improves storage performance. New hybrid architectures and composites that incorporate 2D materials optimise energy storage devices, increasing metrics such as energy density and cycling stability. Moreover, integrating 2D materials into flexible, wearable devices addresses the needs of portable electronics and IoT. The review links the structure, synthesis, and characterisation to the electrochemical behavior of each material and also captures recent advances in the last decade related to battery and supercapacitor applications. In addition, the review includes two tables that compare the performance of each type of material as well as an increased focus on emerging hybrid structures for next-generation energy storage systems. Supercapacitor ; Energy storage ; Electrochemical energy storage ; Flexibility (engineering) ; Battery (electricity) ; Capacitance ; Anode ; Electronics Supercapacitor Materials and Fabrication ; MXene and MAX Phase Materials ; 2D Materials and Applications
Authors: Yujie Wang | Donghua Wang
Source: Nanomaterials (article, 2026-02-15)
Field: Materials Science — Materials Chemistry

Advances in Porous Silicon Materials for Sensing, Energy Storage, and Microelectronics Porous silicon (PSi), characterized by its high specific surface area and highly tunable morphology, presents significant potential across optoelectronics, energy storage, and biomedical applications. This review provides a systematic analysis of the synthesis methodologies, interfacial chemical engineering, and diverse applications of PSi. Initially, fabrication techniques are examined, contrasting the pore formation mechanisms of electrochemical anodization, metal-assisted chemical etching (MACE), and emerging vapor-phase etching methods, while elucidating the control of geometric parameters from microporous to macroporous scales. To address the thermodynamic instability of the hydride-terminated surface, this review systematically evaluates modification strategies such as thermal oxidation, hydrosilylation, carbonization, and atomic layer deposition (ALD). We critically analyze their efficacy in mitigating oxidative drift and enabling specific functionalization. Subsequently, the review summarizes current applications in sensing (refractive index and photoluminescence modulation), energy storage (lithium-ion battery anodes and supercapacitors), and microsystem technologies (radio frequency (RF) isolation, gettering, and micro-electro-mechanical systems (MEMS) sacrificial layers), emphasizing the critical role of structure-property relationships. Finally, an objective assessment is provided regarding the challenges in translating PSi technology to industrial scales, specifically addressing the trade-offs between biodegradability and stability, wafer-scale process uniformity, and the compatibility of wet-chemical processing with standard complementary metal-oxide-semiconductor (CMOS) integration flows. Microelectronics ; Microsystem ; Porous silicon ; Etching (microfabrication) ; Isotropic etching ; Silicon ; Anode ; Microporous material Silicon Nanostructures and Photoluminescence ; Nanowire Synthesis and Applications ; Mesoporous Materials and Catalysis
Authors: Bohayra Mortazavi | František Karlický | Xiaoying Zhuang | Masoud Shahrokhi
Source: Results in Chemistry (article, 2026-01-24)
Field: Materials Science — Materials Chemistry

First-principles investigation of novel stable, strong, and highly attractive semiconducting nanoporous C3N and CN monolayers In recent breakthroughs in the field of nanoporous carbon-nitride two-dimensional (2D) nanomaterials, two novel covalent organic frameworks (COFs) with a C3N stoichiometry (J. Am. Chem. Soc. 2024, 146, 18151 &amp; Angew. Chem. 2024, 136, e202415624) have been synthesized. Based on the realized C3N lattices, we also designed a new COF with CN stoichiometry and s-triazine core molecules. First-principles calculations based on the density functional theory and machine learning interatomic potentials were performed to investigate the dynamical and thermal stability, electronic band structure, optical, excitonic and mechanical properties of the free-standing C3N and CN monolayers. The results demonstrate remarkable thermal and dynamical stability of the C3N and CN nanosheets. Additionally, despite their highly porous structures, the C3N and CN monolayers are predicted to be able to withstand high tensile loads up to approximately 14 GPa. Electronic band structure calculations using the hybrid HSE06 functional indicate band gaps of around 3 eV in the considered C₃N and CN monolayers, which also lead to strong photon absorption spanning the ultraviolet to visible spectrum as well as interesting excitonic effects, highlighting their potential for optoelectronic applications. Additionally, their high work function suggests promising roles as hole injection layers in optoelectronic devices and as electron-blocking layers in energy-related applications. Presented first-principles results confirm the decent thermal/dynamical stability and mechanical robustness of semiconducting C₃N and CN nanosheets, positioning them as appealing candidates for designing flexible optoelectronic devices and high-efficiency energy storage/conversion systems. Nanoporous ; Density functional theory ; Band gap ; Monolayer ; Hybrid functional ; Electronic structure ; Thermal stability ; Ultraviolet ; Work function Covalent Organic Framework Applications ; Graphene research and applications ; 2D Materials and Applications
Authors: Xing Chen | Yu-Lin Sun | Xiumei Lin | Dong Jin-Chao | Jianfeng Li
Source: Nano-Micro Letters (article, 2026-01-05)
Field: Materials Science — Materials Chemistry

In situ Studies of Electrochemical Energy Conversion and Storage Technologies: From Materials, Intermediates, and Products to Surroundings Escalating global energy demands and climate urgency necessitate advanced electrochemical energy conversion and storage technologies (EECSTs) like electrocatalysis and rechargeable batteries. Improving their performance relies on elucidating reaction mechanisms and structure-performance relationships via in situ studies. This review summarizes recent in situ studies of EECSTs through a variety of advanced characterization techniques aiming at mapping reaction pathways for the rational design of overall high-performance reaction systems. We outline the principles, capabilities, advantages, and limitations of various in situ techniques. Their applications in in situ studies of fuel cells, water/CO<sub>2</sub> electrolysis, and lithium batteries are highlighted with representative examples. These studies enable dynamic tracking of chemical and structural evolution of overall reaction systems, including materials, intermediates, products, and surroundings during operation, providing insights critical to rational system design. Future advancements will involve integrating multimodal in situ/operando approaches with artificial intelligence to enable real-time monitoring at practical scales. Such integration promises precise mechanistic insights and robust structure-performance correlations, ultimately accelerating the development of high-performance EECSTs aligned with sustainability and market requirements. Electrochemical energy storage ; Electrochemical energy conversion ; Energy transformation ; Energy storage ; In situ ; Electrocatalyst ; Rational design ; Lithium (medication) Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Electrocatalysts for Energy Conversion
Authors: Ruinan Guo | Junxiang Rong
Source: International Journal of Electrochemical Science (article, 2026-02-11)
Field: Materials Science — Metals and Alloys

Machine learning in corrosion electrochemistry: A critical review of prediction and corrosion-type identification Recent progress in data driven approaches is reshaping corrosion electrochemistry by enabling quantitative interpretation of complex electrochemical responses that involve interactions among environmental variables, alloy composition and surface chemistry. This review examines research published after 2010 that applies machine learning methods such as artificial neural networks, support vector regression and ensemble learning to predict corrosion rates from multivariate electrochemical and environmental datasets. These studies demonstrate that data driven models can reproduce current density evolution, mass loss behaviour and other electrochemical indicators with improved accuracy compared with traditional empirical relations while also revealing mechanistic patterns such as the contribution of alloying elements or the influence of moisture controlled wet and dry cycles. Parallel developments in corrosion type identification have used supervised learning and unsupervised learning to analyse electrochemical noise data, in situ images and microscopy based measurements, allowing automated recognition of uniform attack, pitting initiation and deposit controlled localized degradation. A central point of debate is whether models trained on limited electrochemical conditions can generalize to new regimes. This issue is addressed through hybrid approaches that incorporate physics informed parameters, electrochemical rate expressions or structural constraints that enhance interpretability and improve extrapolation beyond the original training domain. By integrating evidence from more than one hundred peer reviewed studies, this review clarifies the demonstrated capabilities of machine learning in electrochemical corrosion research and highlights the methodological and data related challenges that must be resolved for widespread adoption in scientific and industrial practice. Identification (biology) ; Corrosion ; Pattern recognition (psychology) ; Training set Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition ; Structural Integrity and Reliability Analysis
Authors: Jyoti Kumari | Adarsh Singh | Akash Rawat | Suneel Kumar Srivastava | Ashok Kumar Gupta
Source: RSC Sustainability (article, 2026-01-01)
Field: Materials Science — Polymers and Plastics

Polyaniline-based ternary composites for the photocatalytic degradation of organic pollutants in wastewater: multifunctional properties, synthetic routes, and mechanistic insights Widespread use of pharmaceuticals, pesticides, dyes, and industrial chemicals has heavily polluted global water resources. Polyaniline-based ternary composites offer a promising approach for photocatalytic degradation of organic pollutants in wastewater. Ternary operation ; Pollutant ; Degradation (telecommunications) ; Photocatalysis ; Water pollutants ; Ternary numeral system Conducting polymers and applications ; Advanced Photocatalysis Techniques ; Covalent Organic Framework Applications
Authors: Yifan Wang | Rukai Zhao | Zhucheng Yang | Jianping Xie
Source: PNAS Nexus (article, 2026-01-02)
Field: Materials Science — Materials Chemistry

Functionalizing metal nanoclusters in water: Synthesis, interfacing, and emerging applications Atomically precise metal nanoclusters (MNCs) are metallic kernels wrapped by ligand shells whose chemistry dictates solubility, stability, and interfacial reactivity. Focusing on water-soluble MNCs, this review treats the interface as the bridge from synthesis to function. We map out both direct aqueous syntheses and gentle postsynthetic routes that install hydrophilic motifs while preserving atomic structures. Design rules are then codified: interfacial structure controls stability and mobility, recognition at the interface dictates selectivity, and the local environment sets charge transfer and photophysical responses. Furthermore, ligand shells can define nanoscopic reaction pockets that steer substrate approach and govern electron or energy transfer. These principles illuminate unique advantages in bioimaging and labeling, enzyme-compatible sensing, and controlled charge and mass transport for electrocatalysis. By consolidating synthesis, interfacial physics, and use cases within one framework, this review provides actionable guidelines for linking molecular structure to macroscopic performance and for rationally engineering aqueous MNCs for biomedical and catalytic applications. Nanoclusters ; Nanoscopic scale ; Ligand (biochemistry) ; Aqueous solution ; Metal ; Catalysis ; Charge (physics) ; Substrate (aquarium) Nanocluster Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Quantum Dots Synthesis And Properties
Authors: Chi Zhang | Zhengjie Guo | Fuyueyang Tan | Jinhui Zhou | X Li | Xi Cao | Yikun Yang | Yixian Xie | Yuying Feng | Chenyao Huang | Zaijin Li | Yi Qu | Lin Li
Source: Coatings (article, 2026-01-03)
Field: Materials Science — Materials Chemistry

SnSe: A Versatile Material for Thermoelectric and Optoelectronic Applications Tin selenide (SnSe) is a sustainable, lead-free IV–VI semiconductor whose layered orthorhombic crystal structure induces pronounced electronic and phononic anisotropy, enabling diverse energy-related functionalities. This review systematically summarizes recent progress in understanding the structure–property–processing relationships that govern SnSe performance in thermoelectric and optoelectronic applications. Key crystallographic characteristics are first discussed, including the temperature-driven Pnma–Cmcm phase transition, anisotropic band and valley structures, and phonon transport mechanisms that lead to intrinsically low lattice thermal conductivity below 0.5 W m−1 K−1 and tunable carrier transport. Subsequently, major synthesis strategies are critically compared, spanning Bridgman and vertical-gradient single-crystal growth, spark plasma sintering and hot pressing of polycrystals, as well as vapor- and solution-based thin-film fabrication, with emphasis on process windows, stoichiometry control, defect chemistry, and microstructure engineering. For thermoelectric applications, directional and temperature-dependent transport behaviors are analyzed, highlighting record thermoelectric performance in single-crystal SnSe at hi. We analyze directional and temperature-dependent transport, highlighting record thermoelectric figure of merit values exceeding 2.6 along the b-axis in single-crystal SnSe at ~900 K, as well as recent progress in polycrystalline and thin-film systems through alkali/coinage-metal doping (Ag, Na, Cu), isovalent and heterovalent substitution (Zn, S), and hierarchical microstructural design. For optoelectronic applications, optical properties, carrier dynamics, and photoresponse characteristics are summarized, underscoring high absorption coefficients exceeding 104 cm−1 and bandgap tunability across the visible to near-infrared range, together with interface engineering strategies for thin-film photovoltaics and broadband photodetectors. Emerging applications beyond energy conversion, including phase-change memory and electrochemical energy storage, are also reviewed. Finally, key challenges related to selenium volatility, performance reproducibility, long-term stability, and scalable manufacturing are identified. Overall, this review provides a process-oriented and application-driven framework to guide the rational design, synthesis optimization, and device integration of SnSe-based materials. Thermoelectric effect ; Spark plasma sintering ; Thermoelectric materials ; Band gap ; Semiconductor ; Photovoltaics ; Phonon scattering ; Electronic band structure ; Doping Advanced Thermoelectric Materials and Devices ; 2D Materials and Applications ; Chalcogenide Semiconductor Thin Films