jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ginevra Lalle | Alessandro Maurizi | Anna Maria Giusti | G. Leahu | G. Cesarini | Emilija Petronijevic | Alesandro Belardini | R Li Voti
Source: Condensed Matter (article, 2026-02-18)
Field: Chemistry — Analytical Chemistry

Laser-Spot Step-Heating Thermography for Non-Destructive Evaluation of Thermal Diffusivity in Apples In this work, thermal imaging is employed to study the opto-thermal response of apples (Malus domestica Borkh.), assessing their post-harvest evolution through the estimation of thermal diffusivity. A non-destructive experimental procedure based on mid-wave infrared (MWIR) thermal camera (3–5 µm) and localized heating with a visible laser is developed, enabling spatially and temporally resolved surface temperature measurements. Temperature fields are recorded at different time points and radial distances from the heated spot. A theoretical model based on Fourier thermal diffusion equation is formulated to describe the spatio-temporal evolution of surface temperature. After validation on a reference sample, the method is applied to Golden and Red Delicious apples over a 28-day storage period at room temperature. Red Delicious apple exhibits higher mean diffusivity values without significant temporal changes, whereas a progressive increase in diffusivity is observed for Golden Delicious apples. These results show that thermal diffusivity is sensitive to post-harvest physiological changes in apple tissue and may be associated with intrinsic properties such as tissue density and water content. By relating laser-induced temperature fields to the estimation of thermal diffusivity, this approach enables the non-destructive, quantitative assessment of thermal diffusivity, showing potential for fruit maturity and quality assessment, which are of high importance in agri-food monitoring applications. Thermal diffusivity ; Thermography ; Laser flash analysis ; Infrared ; Thermal ; Diffusion ; Laser ; Heat equation Spectroscopy and Chemometric Analyses ; Food Drying and Modeling ; Greenhouse Technology and Climate Control
Authors: Aline Knab | Shannon Handley | Xiaohu Xu | Max Mackevicius | Ayad G. Anwer | Jared M. Campbell | Yuan Tian | Ewa M. Goldys | Akanksha Bhargava
Source: Communications Biology (article, 2026-03-23)
Field: Chemistry — Analytical Chemistry

Multidimensional data analysis and classification using SMIAL Label-free imaging enables multidimensional data acquisition across spectral and temporal domains and is increasingly used in life sciences. However, users working with multidimensional label-free data often lack the programming expertise needed to build reproducible image analysis pipelines. Existing software require custom programming or switching between fragmented platforms for feature generation and classification tasks. In this work, we introduce SMIAL, an open-source graphical user interface (GUI) software available as a stand-alone package runnable as a 64-bit Windows executable that enables end-to-end machine learning (ML) workflows for multichannel 2D imaging data without requiring programming skills. Users can input multichannel image stacks directly in SMIAL or work with pre-processed multidimensional data via feature tables or pre-trained ML models. Additionally, SMIAL supports parameter saving and reloading to ensure reproducibility across its pre-processing, segmentation, feature generation, feature selection and classification panels. We demonstrate SMIAL's utility across three representative applications of label-free, multispectral imaging: melanoma detection, tracking mitochondrial response to rotenone treatment and non-invasive assessment of food quality. Workflow ; Software ; Executable ; Feature (linguistics) ; Interface (matter) ; Feature selection ; Graphical user interface ; Application programming interface ; Skyline Spectroscopy and Chemometric Analyses ; Cell Image Analysis Techniques ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Tebelay L. Andualem | Reddy Prasad D.M. | Meketaye A. Edenshaw | Sahlu Mhiret Gela | Ermias A. Tesema
Source: Scientific Reports (article, 2026-04-06)
Field: Materials Science — Polymers and Plastics

Sustainable valorization of animal tallow and Aloe abyssinica gel for bar soap formulation with physicochemical properties and quality assessment This study investigates the sustainable production of bar soap using locally sourced Aloe abyssinica gel and animal tallow as primary ingredients. Animal tallow was rendered from local butcher waste and characterized, showing a saponification value of 181.78 mg/g NaOH, acid value of 17.32 mg/g NaOH, pH of 5.45, and specific gravity of 0.929 all within acceptable ranges for soap manufacturing. Fresh A. abyssinica gel was extracted from its leaves and characterized by exhibiting 99.23% moisture content, pH of 4.82, refractive index of 1.33, total dissolved solids of 3.350°Brix, and viscosity of 1.359 cP. Twelve soap formulations were prepared using the cold process method, incorporating varying concentrations of A. abyssinica gel (0–25%) with tallow (50–75%), supplemented with coconut oil (15%) and either olive oil or palm oil (10%). Physicochemical analysis revealed that formulations containing 15% A. abyssinica gel demonstrated optimal properties: pH values of 9.75–9.92, total fatty matter content of 73.33–76.8% (Grade I quality), enhanced foamability (4.8–7.3 cm), superior cleansing power, and reduced free caustic alkali (0.08–0.13%). Results confirm that combining A. abyssinica gel with animal tallow produces high-quality, skin-compatible soap while promoting waste valorization and sustainable manufacturing practices. Quality assessment ; Tallow ; Raw material ; SOAP ; Quality (philosophy) Polymer Science and PVC ; Waste Management and Recycling ; Antimicrobial agents and applications
Authors: Péter Görög | Ákos Török
Source: Bulletin of Engineering Geology and the Environment (article, 2026-02-01)
Field: Materials Science — Biomaterials

Influence of water on the physical properties of marl, calcareous marl, clayey marl, and limestone: a comparative study Abstract The paper outlines the physical parameters and textural differences of clay marl, marl, and calcareous marl, focusing on the physical changes that are caused by water. The characterization of marls is often very difficult since their properties are variable, covering a broad range from low-strength friable rock to highly cemented durable lithologies. Consequently, the prediction of their physical parameters requires further consideration. The paper addresses this issue by physically and texturally characterizing Eocene marly lithologies and comparing their properties to various limestones. Density, water absorption, ultrasonic pulse velocity, uniaxial compressive strength (UCS), Brazilian tensile strength, Young’s modulus, and their interdependencies are explained. The link between physical properties and various micro-fabrics is also outlined, with the help of new equations. Our tests show that UCS of calcareous marl can be as high as 100 MPa, but it decreases when the sample is water saturated. Relationships were calculated between the physical parameters of air-dry and water-saturated samples. Correlations between Young’s moduli and uniaxial compressive strength are also explained. The results of our analyses were compared to published physical parameters of marls and limestone, and relationships were outlined between the parameters of these carbonates. Marl ; Calcareous ; Lithology ; Compressive strength ; Ultimate tensile strength ; Water content ; Sedimentary rock Calcium Carbonate Crystallization and Inhibition ; Clay minerals and soil interactions ; Building materials and conservation
Authors: Yanshuang Liu | Juanjuan Yu | Haotian Wang | Y. Li | Weiwei Ren | Can Wang | Xiaofeng Xu | Xia Han | Zhen Wu | Meihong Sun | Shaojun Dai
Source: The Plant Journal (article, 2026-01-01)
Field: Chemistry — Inorganic Chemistry

Chloroplast glutaredoxin <scp>S12</scp> regulates photosynthesis and growth through redox modulation of <scp>SufB</scp> Redox homeostasis and Fe-S protein maturation are critical for chloroplast function. Chloroplast-localized glutaredoxins (Grxs) are versatile enzymes that function either as oxidoreductases or Fe-S cluster transferases. However, their target proteins and underlying molecular mechanisms remain incompletely understood. Here, we demonstrate that the cysteine 34 within the active site motif of chloroplast-localized Arabidopsis GrxS12 is the key residue governing its oxidoreductase activity, while the C-terminal cysteine 92 plays a secondary role. These cysteines undergo glutathionylation or form an intramolecular disulfide bond, which can be reduced by Trx-m1. Although GrxS12 is unable to bind Fe-S clusters itself, it interacts with and reduces the Fe-S cluster assembly protein SufB, thereby regulating Fe-S protein maturation. Loss of GrxS12 induces redox alterations of multiple chloroplast proteins, increased oxidation of SufB, and decreased abundances of several Fe-S proteins. These changes likely underlie the observed structural damage to chloroplasts, reduced photosynthetic efficiency, and slower growth in grxs12 mutants. Our results reveal a novel mechanism by which GrxS12 regulates chloroplast Fe-S cluster biogenesis through its oxidoreductase activity. Glutaredoxin ; Chloroplast ; Oxidoreductase ; Biogenesis ; Arabidopsis ; Cysteine ; Photosynthesis ; Redox ; Iron–sulfur cluster Metal-Catalyzed Oxygenation Mechanisms ; Metalloenzymes and iron-sulfur proteins ; Redox biology and oxidative stress
Authors: Mehrab Mahdian | Ferenc Ender | Tamás Párdy
Source: arXiv (Cornell University) (article, 2026-03-29)
Field: Materials Science — Biomaterials

Electrospinning-Data.org: A FAIR, Structured Knowledge Resource for Nanofiber Fabrication Electrospinning is a versatile nanofabrication technique whose outcomes emerge from a complex, high-dimensional interplay between solution properties, processing parameters, and environmental conditions. Optimizing this parameter space for targeted fiber morphology is inherently challenging, often driving extensive trial-and-error experimentation and generating vast experimental data across laboratories worldwide. Yet this knowledge remains fragmented and underutilized due to inconsistent reporting and a pervasive bias toward successful outcomes, limiting reproducibility and hindering data-driven research. Here we introduce Electrospinning-Data.org, a FAIR-aligned data aggregation infrastructure that organizes dispersed electrospinning experiments into structured, reusable, and failure-aware scientific records. The platform is built around a unified process-structure-property data model linking experimental inputs, environmental conditions, and nanofiber morphology, annotated through a controlled vocabulary, within a consistent, machine-readable schema. A two-stage moderation pipeline combining automated validation with expert review supports data quality and long-term interoperability. The resulting structured, failure-inclusive corpus provides a framework for data-driven research, including predictive modelling, inverse design of target morphologies, and systematic mapping of instability regimes that would otherwise require extensive trial-and-error experimentation. Pipeline (software) ; Resource (disambiguation) ; Electrospinning ; Limiting ; Nanofiber ; Quality (philosophy) ; Process (computing) ; Bridge (graph theory) Electrospun Nanofibers in Biomedical Applications ; Machine Learning in Materials Science ; Microfluidic and Capillary Electrophoresis Applications
Authors: Yuyan Huang | Yongkuai Chen | Chuanhui Li | Tao Wang | Chengxu Zheng | Jian Zhao
Source: Foods (article, 2026-01-12)
Field: Chemistry — Analytical Chemistry

A Model for Identifying the Fermentation Degree of Tieguanyin Oolong Tea Based on RGB Image and Hyperspectral Data The fermentation process of oolong tea is a critical step in shaping its quality and flavor profile. In this study, the fermentation degree of Anxi Tieguanyin oolong tea was assessed using image and hyperspectral features. Machine learning algorithms, including Support Vector Machine (SVM), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU), were employed to develop models based on both single-source features and multi-source fused features. First, color and texture features were extracted from RGB images and then processed through Pearson correlation-based feature selection and Principal Component Analysis (PCA) for dimensionality reduction. For the hyperspectral data, preprocessing was conducted using Normalization (Nor) and Standard Normal Variate (SNV), followed by feature selection and dimensionality reduction with Competitive Adaptive Reweighted Sampling (CARS), Successive Projections Algorithm (SPA), and PCA. We then performed mid-level fusion on the two feature sets and selected the most relevant features using L1 regularization for the final modeling stage. Finally, SHapley Additive exPlanations (SHAP) analysis was conducted on the optimal models to reveal key features from both hyperspectral bands and image data. The results indicated that models based on single features achieved test set accuracies of 68.06% to 87.50%, while models based on data fusion achieved 77.78% to 94.44%. Specifically, the Pearson+Nor-SPA+L1+SVM fusion model achieved the highest accuracy of 94.44%. This demonstrates that data feature fusion enables a more comprehensive characterization of the fermentation process, significantly improving model accuracy. SHAP analysis revealed that the hyperspectral bands at 967, 942, 814, 784, 781, 503, 413, and 416 nm, along with the image features H<sub>σ</sub> and H, played the most crucial roles in distinguishing tea fermentation stages. These findings provide a scientific basis for assessing the fermentation degree of Tieguanyin oolong tea and support the development of intelligent detection systems. Hyperspectral imaging ; Pattern recognition (psychology) ; Principal component analysis ; RGB color model ; Feature selection ; Dimensionality reduction ; Normalization (sociology) ; Preprocessor ; Outlier Spectroscopy and Chemometric Analyses ; Tea Polyphenols and Effects ; Advanced Chemical Sensor Technologies
Authors: Tiancheng Zhao | Tianqi Bai | Yang  (1992 - ) He | Wenhui Xu | Xinxin Yu | Ruochen Shi | Zhenyu Qu | Jiaxin Liu | Rui Shen | Haodong Jiang | Yeliang Wang | Jiaxin Ding | Dongchen Sui | Shibin Zhang | Lei Zhu | Ailun Yi | Kai Huang | Min Zhou | Huarui Sun | Zhonghui Li | Peng Gao | Tiangui You | Xin Ou
Source: arXiv (Cornell University) (article, 2026-03-16)
Field: Materials Science — Materials Chemistry

Monolithic integration of diverse crystalline thin films on diamond for near-junction thermal management The pursuit of extreme miniaturization and high power in 6G RF front-ends has cast thermal dissipation as the central challenge. Here, we have demonstrated the monolithic integration of functionally distinct single-crystal thin films, including \b{eta}-Ga2O3, Si, GaN, and LiTaO3, onto a single diamond substrate using a multi-step transfer printing technique. Focusing on the critical \b{eta}-Ga2O3/diamond interface, we achieve an exceptional interfacial thermal conductance (ITC) of 149 MW m-2 K-1 through ultra-high vacuum (UHV) annealing, creating an atomically sharp interface featuring covalent bonding. Vibrational electron energy-loss spectroscopy (EELS) analysis combining with molecular dynamics (MD) simulations reveal that distinctive interfacial phonon modes at the \b{eta}-Ga2O3/diamond heterointerface dominate ultrahigh ITC. We experimentally demonstrate that by improving the ITC, the thermal resistance (Rth) of a diamond-based \b{eta}-Ga2O3 MOSFET is driven to a record-low value of 1.58 K mm W-1, underscoring the critical role of interface engineering in near-junction thermal management for diamond-integrated devices. This work demonstrates a scalable, diamond-based monolithic integration platform designed to solve the near-junction thermal challenges in high-power RF front-ends. Miniaturization ; Diamond ; Thin film ; Thermal ; Thermal conductivity ; Phonon ; Thermal management of electronic devices and systems ; Substrate (aquarium) Thermal properties of materials ; Ga2O3 and related materials ; Thermal Radiation and Cooling Technologies
Authors: Lei Li | Pu Wang | Yong Pei
Source: Chemical Science (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Photoluminescence of ligand-protected gold nanoclusters: progress in experimental and theoretical studies Ligand-protected gold nanoclusters, with their small sizes and atomically precise structures, exhibit unique photophysical properties that position them as promising candidates for applications in bioimaging, sensing, and optoelectronics. However, except for a few ligand-protected gold nanoclusters, their photoluminescence quantum yield (PLQY) is still relatively low, and the underlying emission mechanisms are not yet fully elucidated. This review highlights recent experimental and theoretical advances aimed at enhancing and elucidating the photoluminescence (PL) mechanisms of ligand-protected gold nanoclusters. Experimentally, strategies such as heterometallic doping, ligand engineering, and structural rigidification have been employed to promote radiative transitions and suppress nonradiative decay, resulting in substantial improvements in PLQY. Theoretically, methods including density functional theory (DFT), time-dependent density functional theory (TDDFT), and nonadiabatic molecular dynamics (NA-MD) have provided important insights into the PL origin, emission pathways, and excited-state dynamics of ligand-protected gold nanoclusters. These advances will deepen our understanding of structure-property relationships in ligand-protected gold nanoclusters and pave the way for the rational design of highly emissive gold nanoclusters. Photoluminescence ; Quantum dot ; Gold alloys ; Semiconductor materials Nanocluster Synthesis and Applications ; Gold and Silver Nanoparticles Synthesis and Applications ; Advanced Chemical Physics Studies
Authors: Shaoming Pan | Wei Zhang | Yuan Ma | Yi Su | Wei Huang
Source: Electronics (article, 2026-03-06)
Field: Materials Science — Materials Chemistry

Pattern Recognition of GIS Partial Discharge Based on UHF Signal Characteristics The partial discharge (PD) caused by insulation defects of gas-insulated switchgear (GIS) threatens the secure and stable operation of power systems. Traditional PD pattern recognition methods exhibit limitations due to incomplete information utilization and unresolved correlations among characteristic parameters. Based on the partial discharge mechanisms of GIS, this paper establishes a GIS partial discharge simulation model using the finite element time-domain (FETD) method. The propagation rules and influence factors of ultra-high-frequency (UHF) signals are studied. Furthermore, a PD pattern recognition method based on a deep convolutional neural network (CNN) is proposed. Research results indicate that UHF signals generated by GIS partial discharge are significantly influenced by pulse current waveforms and discharge quantity. The peak-to-peak amplitude of the electric field (Epp) increases linearly with the current amplitude, while it decreases nonlinearly with increasing pulse width. The UHF signal remains a certain value while the pulse width exceeds a critical threshold (4 ns). The proposed CNN-based approach, utilizing full-wave UHF signals, overcomes the shortcomings of traditional methods reliant on manually extracted discrete feature parameters. Compared to other network architectures and optimization algorithms, the ConvNeXt-AdamW model demonstrates superior performance, achieving an average PD pattern recognition accuracy exceeding 96%. Partial discharge ; Ultra high frequency ; Switchgear ; Waveform ; Pattern recognition (psychology) ; SIGNAL (programming language) ; Convolutional neural network ; Pulse (music) High voltage insulation and dielectric phenomena ; Dielectric materials and actuators ; Power Transformer Diagnostics and Insulation
Authors: Bo Wen | J. Sebastian Paez | Chris Hsu | Daniele Canzani | Alexis Taylor Chang | Nicholas Shulman | Brendan MacLean | Matthew D. Berg | Judit Villén | William E. Fondrie | Lindsay K. Pino | Michael J. MacCoss | William Stafford Noble
Source: bioRxiv (Cold Spring Harbor Laboratory) (article, 2026-03-31)
Field: Chemistry — Spectroscopy

Carafe2 enables high quality <i>in silico</i> spectral library generation for timsTOF data-independent acquisition proteomics Data-independent acquisition (DIA) proteomics enables reproducible and systematic peptide detection and quantification, and trapped ion mobility spectrometry (TIMS) on the timsTOF platform further improves DIA by synchronizing ion mobility separation with quadrupole precursor sampling. Analyzing the highly multiplexed spectra generated by DIA typically relies on spectral libraries, and fully leveraging the additional ion mobility dimension requires these libraries to include accurate retention time, fragment ion intensity, and ion mobility annotations. Existing <i>in silico</i> spectral library generation tools either lack ion mobility support entirely or rely on models trained on data-dependent acquisition (DDA) data, that can introduce a mismatch that may not capture unique experiment-specific biases when applied to each respective timsTOF dataset. Carafe is a software tool that uses deep learning models to generate high-quality, experiment-specific <i>in silico</i> libraries by training directly on DIA data. In this study, we extend Carafe to generate libraries for timsTOF DIA data, which involves fine-tuning retention time (RT), fragment ion intensity, and ion mobility prediction models using timsTOF DIA data. Carafe2 operates directly on native timsTOF raw data (Bruker .d directories) without the need for data conversion. We demonstrate the performance of Carafe2 across a wide range of DIA applications, including global proteome, phosphoproteome, and plasma proteome datasets. Comparing Carafe2 fine-tuned RT, fragment ion intensity, and ion mobility prediction models with pretrained DDA models, we find that Carafe2 models outperform pretrained models on a variety of DIA datasets. We then demonstrate the utility of <i>in silico</i> libraries generated by Carafe2 for peptide detection on several different types of timsTOF DIA datasets by comparing with the libraries generated with DDA-trained AlphaPeptDeep models, DIA-NN built-in models, and empirical spectral libraries generated from DDA experiments. Ion-mobility spectrometry ; Preprocessor ; Proteome ; Fragment (logic) ; Range (aeronautics) ; Ion ; Data acquisition Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Photosynthetic Processes and Mechanisms