jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yutong Han (5447690) | Haozhe Zhuang (20138230) | Ziyang Yin (20998587) | Zhengqing Long (22049244) | Yue Li (102191) | Yu Yao (428722) | Qibin Zheng (19859133) | Zhigang Zhu (522211)
Source: Figshare (article, 2025-08-13)
Field: Materials Science — Materials Chemistry

A Machine-Learning-AlgorithmEnhanced Multi-FunctionalGas Sensor for Self-Humidity Compensation and Partial Discharge Detection Gas-Insulated switchgear (GIS) is prone to partial discharges (PDs) in high electric field environments, and the concentration of generated NO<sub>2</sub> is an essential indicator for determining the PD types and severity of faults. Notably, environmental humidity greatly influences the insulation performance of gas-insulated switchgear and the signals of NO<sub>2</sub> gas sensors. Thus, the simultaneous detection of humidity and NO<sub>2</sub> and the decoupling of signals has practical importance. Herein, a groundbreaking sensor is developed to achieve self-calibrated sensing of humidity and NO<sub>2</sub> gas, which is realized by a multifunctional WS<sub>2</sub>/ZnO sensitive material with an innovative self-humidity compensation algorithm of DF-MT1DCL. This synergistic system delivers dynamic, real-time humidity adaptive calibration and also enables precise recognition of partial discharge types. The sensor exhibited simultaneous response and a wide detection range (100 ppb–10 ppm of NO<sub>2</sub>, 10.8–94.3% RH) exposed to NO<sub>2</sub> and humidity at room temperature. As a result, simultaneous monitoring and decoupling of signals can be realized. Further, a multitask deep learning model DF-MT1DCL combined 1D-CNN with LSTM was proposed to complete the humidity adaptive calibration based on a single WS<sub>2</sub>/ZnO sensor, which realizes the simultaneous prediction of humidity and NO<sub>2</sub> concentration, with <i>R</i><sup>2</sup> values of 99.1% and 93.5% respectively. The WS<sub>2</sub>/ZnO sensor with excellent humidity and NO<sub>2</sub> sensing performance and the DF-MT1DCL algorithm assistance was applied to partial discharge monitoring in a simulated gas-insulated switchgear, and high-precision classification of partial discharge types was achieved with 100% classification accuracy. Therefore, the constructed WS<sub>2</sub>/ZnO multifunctional sensor combined with the DF-MT1DCL algorithms improves the resistance to humidity interference of NO<sub>2</sub> detection and also accurately recognizes the partial discharge type, which provides a new perspective for the intelligent sensing technology for health monitoring of electric power equipment. Switchgear ; Humidity ; Partial discharge ; Decoupling (probability) ; Calibration ; Compensation (psychology) ; Voltage High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Advanced Sensor and Energy Harvesting Materials
Authors: Cong Xia | Qianshi Zhang | Zongheng Wang | Xiaojiang Li | Zhiwei Sun | Yinan Zhang | Shuangyi Ren
Source: Materials Horizons (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Rational design of dual-atom nanozymes for deep infection healing guided by an interpretable intrinsic descriptor Deep infections (DIs) continue to pose substantial threats to global public health and represent a critical challenge requiring a new generation of antibacterial agents. Oxidase (OXD)-like nanozymes, which directly activate oxygen, offer a promising approach for treating DIs. However, the vast design space has limited progress in discovering efficient OXD-like nanozymes. In this study, we developed a simple, interpretable intrinsic descriptor <i>φ</i><sub>ABC</sub> = min<i>θ</i>0.2d × min<i>χ</i><sup>0.3</sup> × (<i>θ</i><sub>d</sub>·1 × <i>θ</i><sub>d</sub>·2)<sup>0.1</sup> by integrating density functional theory calculations, machine learning, and experimental validation. The descriptor <i>φ</i><sub>ABC</sub> offered deep physical insights by incorporating atomic properties (A), bimetallic synergistic effects (B), and coordination environments (C). Guided by this descriptor, we discovered FeZn dual-atom nanozymes (DAzymes) exhibiting a <i>k</i><sub>cat</sub> value of 0.59 s<sup>-1</sup> and a <i>k</i><sub>cat</sub>/<i>K</i><sub>m</sub> value of 1.43 × 10<sup>10</sup> mM<sup>-1</sup> s<sup>-1</sup> for OXD-like reactions. This performance surpassed that of previously reported state-of-the-art nanozymes by three orders of magnitude. With the aid of near-infrared (NIR) irradiation, FeZn DAzyme achieved a methicillin-resistant <i>Staphylococcus aureus</i> (MRSA) clearance rate of 99.99% and exhibited a superior healing effect compared with vancomycin, effectively overcoming bacterial resistance, eliminating bacterial biofilms, and promoting DI recovery. Our work integrates descriptor-guided design with biological applications, which ultimately provides a new paradigm for screening nanozymes and elucidating structure-mechanism-activity-therapeutic relationships. Dual (grammatical number) ; Rational design ; Atom (system on chip) ; Dual role ; Nanotechnology ; Materials science ; Biological system ; Chemistry ; Computer science ; Combinatorial chemistry ; Biology ; Philosophy ; Embedded system Advanced Nanomaterials in Catalysis ; Advanced Photocatalysis Techniques ; Advanced biosensing and bioanalysis techniques
Authors: Rui Ren | Kai Guo | Yulan Gu | Guifen Li | Lirong Zhang | Yafu Wang | Q. Zhang | Qin Wang | Jiangwei Zhang
Source: Nano Research (article, 2025-07-18)
Field: Materials Science — Materials Chemistry

Advanced spectroscopic, microscopic, and compositional techniques in catalytic material characterization: Applications and progress Catalytic reactions play a key role in energy production, green chemistry and chemical synthesis, and are the cornerstone for addressing global challenges such as environmental pollution and energy crisis. The design and performance optimization of efficient catalysts rely on a deep understanding of their structural characteristics, electronic states and kinetic behaviors during reactions, and advanced characterization techniques provide key technical support. This review summarizes the applications, advantages and limitations of spectroscopic techniques (X-ray absorption spectroscopy, Nuclear magnetic resonance, Raman spectroscopy, Infrared spectroscopy and Electron paramagnetic resonance), Microscopic imaging techniques (Transmission electron microscopy, Scanning electron microscopy and Atomic force microscopy) and component analysis techniques (X-ray photoelectron spectroscopy, X-ray diffraction and Inductively coupled plasma mass spectrometry) in catalytic research. These techniques can provide multi-dimensional insights into the microstructure of catalysts, the properties of active sites and their evolution during reactions, laying a solid foundation for elucidating catalytic mechanisms and optimizing catalyst performance. Although current characterization methods still face challenges in spatial resolution, compatibility with extreme reaction conditions and data processing complexity, significant progress is expected through emerging strategies such as multi-technique integration and artificial intelligence-assisted analysis. This review aims to provide a reference for researchers in the field of catalysis and a forward-looking perspective for the development of characterization techniques. Characterization (materials science) ; Nanotechnology ; Materials science ; Catalysis ; Chemistry ; Organic chemistry Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Hydrocarbon exploration and reservoir analysis
Authors: Roshan Sharma | Nikolaos Meimetis | Arjana Begzati | Sheetal J. Nagar | Benjamin P. Kellman | Hratch Baghdassarian
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-02-20)
Field: Chemistry — Spectroscopy

mRNA-Protein Coordination is Contextualized by Metastatic Biological Phenotypes Abstract A central goal of conducting omics measurements is to understand how molecular features inform higher-order cell- and tissue-level phenotypes. In particular, multi-omics offers insights into how information encoded by the genome is coordinated through biological layers, resulting in functional outputs 1 . Due to myriad post-transcriptional regulatory processes, the coordination between mRNA and protein cannot be simply reduced to gene-wise correlation. Yet, both modalities have been shown to serve as representations of biological state, and multi-omics integration has been used to improve these representations. Multi-omics approaches typically do not focus on how mRNA and protein features coordinate, but rather use the additional information for improved prediction or feature selection. Here, instead, we showed that standard linear machine learning models provide an understanding of transcriptomic and proteomic coordination in the context of a biological phenotype of interest, in this case cancer metastasis. We find that, in the context of metastasis, a select subset of proteomic features—reflecting a more concentrated signal relative to the broadly distributed transcriptomic signal—offers additional information to that encoded by transcriptomics, as demonstrated by improved model performance when integrating the two modalities and the relative feature importance of proteomics. Top features show a depletion of gene-product overlap across modalities, indicating that the model primarily leverages instances in which the two modalities are providing complementary information with respect to phenotype. However, in instances when both modalities are selected for a given gene product, there is high information consistency that synergistically bolsters phenotype prediction. Altogether, by using model fits that relate both modalities to phenotype, we observe a nuanced coordination of protein and mRNA, in which both modalities tend to provide consistent information about phenotype, yet benefits remain to incorporating a combination of both complementary and reinforcing signals across modalities. Joint (building) ; Proteomics ; Metastasis ; Computational biology ; Transcriptome ; Cancer metastasis ; Computer science ; Cancer ; Internal medicine ; Medicine ; Biology ; Engineering ; Gene ; Genetics ; Gene expression Advanced Proteomics Techniques and Applications ; Cancer Genomics and Diagnostics ; Gene expression and cancer classification
Authors: Claire Seydoux (20395820) | Matthew Bryan (655116) | Jean-Paul Barnes (1546744) | Pierre-Henri Jouneau (1555822)
Source: Figshare (article, 2025-06-18)
Field: Chemistry — Spectroscopy

Efficient Denoisingof Shot-Noise in Mass SpectrometryImages by PCA-Assisted Self-Supervised Deep Learning Mass spectrometry imaging (MSI) often suffers from inherent noise due to signal distribution across numerous pixels and low ion counts, leading to shot noise. This can compromise the accurate interpretation, especially for trace molecules. Recent advances in self-supervised deep learning denoising have demonstrated significant potential for enhancing data quality. In this Letter, we propose an optimized approach for using the Noise2Void (N2 V) algorithm for MSI denoising by applying a principal component analysis (PCA) preprocessing step. By rotating the data along its principal components prior to denoising, our method, Principal Component-Assisted Noise2Void (PCA-n2v), outperforms direct N2 V implementations and other state-of-the-art denoising techniques. The limitations of PCA-n2v are also evaluated using a synthetic MSI data set, revealing that bleedthrough artifacts may arise in images with extremely low signal-to-noise ratios. To facilitate adoption, an easy-to-use PCA-n2v implementation is provided via a GitHub repository. Overall, PCA-n2v advances MSI data processing, enabling higher-throughput and higher-resolution experiments with improved fidelity. Noise reduction ; Preprocessor ; Principal component analysis ; Pattern recognition (psychology) ; Noise (video) ; Deep learning ; Pixel ; Data pre-processing Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Jian Li (41607) | Yu Wu (54264) | Wenxuan Jiang (6613172) | Weiqing Xu (1279959) | Ying Zhou (25031) | Xin Yu (124814) | Yiwei Qiu (12555289) | Yifei Chen (534716) | Liuyong Hu (3553949) | Lirong Zheng (1461724) | Wenling Gu (1492708) | Chengzhou Zhu (1387101)
Source: Figshare (article, 2025-06-10)
Field: Materials Science — Materials Chemistry

Atomic Ga SiteEnables Photonanozymes with SpecificInhibition Modes for Primary Drug Screening Enzyme inhibition plays a crucial role in drug discovery by governing interactions between molecules and distinct enzymatic sites, facilitating the identification of early drug candidates. However, most nanozymes have been limited to single active site inhibition models, leaving gaps in understanding inhibitor interactions and their dynamic modulation by external stimuli. Hence, an in-depth understanding of nanozyme inhibition across different functional domains, particularly under external stimuli like light irradiation, remains challenging. Herein, we report a carbon nitride photonanozyme with atomically dispersed Ga–N<sub>3</sub>-coupled cyano sites (Ga-CN) for analyzing and screening antithyroid drugs via photoregulated inhibition modes. Ga sites modulate the electronic structure of cyano sites, enhancing photoinduced charge separation and synergistically boosting peroxidase-like activity. Inhibition kinetics reveal that thiol-containing drugs exhibit distinct mixed inhibition modes by targeting different active sites, with inhibition markedly enhanced under light. Leveraging these differential inhibition patterns, we developed a Ga-CN-based sensor array, integrating machine learning for precise antithyroid drug screening, facilitating early drug discovery. Active site ; Drug ; Enzyme inhibition ; Small molecule ; Drug discovery ; Enzyme ; Allosteric regulation ; Virtual screening Advanced Nanomaterials in Catalysis ; Nanoplatforms for cancer theranostics ; Nanocluster Synthesis and Applications
Authors: Gaohui Ge | Changlei Wu | Juntong Zhu | Jiazhen Zhang | Li Xiao | Jiang Wu | Guifu Zou | Hao Xu
Source:  (article, 2025-07-06)
Field: Materials Science — Materials Chemistry

2D MoS2: Large-Scale Growth, Integrated Optoelectronics and Beyond Two-dimensional (2D) transition metal dichalcogenides (TMDs), which possess inherently mechanical flexibility and tunable bandgaps, atomic level thickness, as well as unique electronic and optical attributes, are prominent candidates for optoelectronics in the post-Moore period. To date, the growth of high-quality and large-scale monolayer MoS<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> has been one of the main challenges for practical applications. We have developed a MoS<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf>−OH bilayer-mediated method that can fabricate inch-sized monolayer MoS<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> on arbitrary substrates. The field-effect transistor arrays were fabricated, showing high on/off ratio up to ~10<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">7</sup> and large device mobility. Afterwards, a heterosynapse-inspired photodetector is proposed for the intelligent edge visual system to process spatiotemporal information. Meanwhile, a hippocampus-inspired device based on MoS<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> for illumination time encoding is fabricated, in which the encryption technology is employed for data security. Moreover, we have demonstrated the potential application of MoS<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> devices in artificial neural network. To further adjust the optical absorption range of TMDs, we have engineered the optical properties of MoS<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> via alloying with Se to extend its optical absorption to the NIR region, and the phototransistor was fabricated based on monolayer MoS2(1-x)Se<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2x</inf>. When under 780 nm (~1.59 eV) illumination, the device delivered a photoresponsivity of an external quantum efficiency up to 11230%. Optoelectronics ; Scale (ratio) ; Materials science ; Computer science ; Physics 2D Materials and Applications
Authors: Nevolianis, Thomas | Zheng, Jonathan | Müller, Simon | Baumann, Matthias | Tshepelevitsh, Sofja | Kaljurand, Ivari | Leito, Ivo | Smirnova, Irina | Green, William | Leonhard, Kai
Source: TUHH Open Research (article, 2025-08-27)
Field: Chemistry — Organic Chemistry

Solvation free energies of anions: from curated reference data to predictive models Predicting the physicochemical properties of ionizable solutes, including solubility and lipophilicity, is of broad significance. Such predictions rely on the accurate determination of solvation free energies for ions. However, the limited availability of high-quality reference data poses a challenge in developing accurate, inexpensive computational prediction methods. In this study, we address both issues of data quality and availability. We present three databases and models related to ionic phenomena: (1) 8,241 pKa data points across 8 solvents, (2) 5,536 gas-phase acidities from DLPNO-CCSD(T) QM calculations, and (3) 6,090 solvation free energies of anions across 8 solvents obtained from a thermodynamic cycle. We also report 6,088 solvation free energies of neutral conjugate solutes computed using the COSMO-RS method. The pKa data were obtained from the iBonD database, cleaned, and combined with a separate compilation of trustworthy reference pKa data. Gas-phase acidities were computed for most of the acids present in the pKa corpus. Leveraging these data, we compiled values for solvation free energies of anions. We then trained several graph neural network models, which can be used as an alternative to QM approaches to quickly estimate these properties. The pKa and gas-phase acidity models accept reaction SMILES strings of the acid dissociation as inputs, whereas the solvation energy model accepts the SMILES string of the anion. Our microscopic pKa model achieves good accuracy, with an overall test mean average error of 0.58 units on unseen solutes and 0.59 on the SAMPL7 challenge (the lowest error so far among multisolvent models). Our gas-phase acidity model had mean absolute errors slightly above 2 kcal mol-1 when evaluated against experimental data. The anionic solvation free energy model had mean absolute errors of less than 3 kcal mol-1 in several test evaluations, comparable to (though less reliable than) several widely used QM-based solvation models. Solvation ; Implicit solvation ; Reference data ; Ionic bonding ; Dissociation (chemistry) ; Solubility ; Acid dissociation constant ; Quantitative structure–activity relationship Free Radicals and Antioxidants ; Computational Drug Discovery Methods ; Machine Learning in Materials Science
Authors: Haoyu Wang | Jiaxin Wang | Shaotong Qi | Yijie Chen | Ming Li | Chunwei Zhang
Source: International Journal of Thermal Sciences (article, 2025-12-10)
Field: Materials Science — Materials Chemistry

Multi-objective optimization of leaf-vein-inspired flow field in solid oxide fuel cells based on the non-dominated sorting genetic algorithm II Sorting ; Power density ; Reduction (mathematics) ; Microchannel ; Current density ; Genetic algorithm ; Thermal ; Bayesian optimization ; Surrogate model ; Flow (mathematics) Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials ; Electrocatalysts for Energy Conversion
Authors: Sean M. Brown (21259409) | Evan Allgair (21259412) | Robin Kryštůfek (10819256)
Source: Figshare (article, 2025-05-07)
Field: Chemistry — Spectroscopy

Mapping theEdges of Mass Spectral Prediction: Evaluationof Machine Learning EIMS Prediction for Xeno Amino Acids Mass spectrometry is one of the most effective analytical methods for unknown compound identification. By comparing observed <i>m</i>/<i>z</i> spectra with a database of experimentally determined spectra, this process identifies compound(s) in any given sample. Unknown sample identification is thus limited to whatever has been experimentally determined. To address the reliance on experimentally determined signatures, multiple state-of-the-art MS spectra prediction algorithms have been developed within the past half decade. Here we evaluate the accuracy of the NEIMS spectral prediction algorithm. We focus our analyses on monosubstituted α-amino acids given their significance as important targets for astrobiology, synthetic biology, and diverse biomedical applications. Our general intent is to inform those using generated spectra for detection of unknown biomolecules. We find predicted spectra are inaccurate for amino acids beyond the algorithms training data. Interestingly, these inaccuracies are not explained by physicochemical differences or the derivatization state of the amino acids measured. We thus highlight the need to improve both current machine learning based approaches and further optimization of <i>ab initio</i> spectral prediction algorithms so as to expand databases for structures beyond what is currently experimentally possible, even including theoretical molecules. Focus (optics) ; Identification (biology) ; Derivatization ; Mass spectrum ; Pattern recognition (psychology) ; Amino acid ; Spectral line Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Advanced Proteomics Techniques and Applications
Authors: Mattia Turchi (8491857) | Ivan Lunati (11894315)
Source: Figshare (article, 2025-10-03)
Field: Materials Science — Materials Chemistry

Segmentation forLearning Adsorption Patterns andResidence-Time Kinetics on Amorphous Surfaces Heterogeneous surfaces such as amorphous silica are characterized by highly heterogeneous local atomic environments that govern the adsorption of gas molecules through spatial arrangements. These surfaces exhibit properties that are particularly relevant for adsorption and catalytic applications. Here, we investigate CO<sub>2</sub> adsorption landscapes, captured by CO<sub>2</sub> density maps, which display complex patterns requiring machine learning (ML) segmentation for systematic analysis. We present an optimized segmentation protocol based on a modified Random Forest (RF) classifier designed to control the morphology and spatial extent of the segmented regions via feature smoothing and standardized training parameters. While broadly applicable for specific modeling goals and properties of interest, here, the method is tailored to identify high-density regions that dominate heterogeneous adsorption dynamics. For these regions, we extract residence-time statistics that deviate from exponential behavior, revealing multiple time scales associated with distinct surface defects on amorphous surfaces. The extracted kinetics provide essential information for coarse-grained models of adsorption on disordered surfaces. Such models, parametrized using atomistic simulations, enable the prediction of macroscopically measurable adsorption and desorption rates, which can be directly compared with experiments also under conditions not limited by mass transfer. Adsorption ; Amorphous solid ; Segmentation ; Smoothing ; Kinetics ; Desorption ; Feature (linguistics) Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Advanced Chemical Physics Studies