jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Haoxiang Yu | Xuxi Zhou | Yunlai Fu | Yuanqing Wan | Weijun Liu | Quan Yuan | Shuming Wang
Source: Small Methods (article, 2025-04-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Angle‐Insensitive Broadband Multispectro‐Polarimetric Encoding Based on Inverse Design of Mosaic Metasurfaces Multimodal optical information detection allows precise characterization of light-matter interactions. Current multimodal optical detection systems often encounter challenges such as low spectral resolution, limited polarization sensitivity, or bulky designs that impede seamless integration. Here, a broadband multispectro-polarimetric filter (BMSPF) metasurface array enables simultaneous spectral is proposed and full-Stokes polarimetric imaging in a single snapshot. By combining Metal-Insulator-Metal (MIM) metasurfaces with a mosaic optimization design, angle-robust multispectro-polarimetric encoding under a 30° field-of-view is achieved. The encoded grayscale information is captured via a monochrome image sensor and decoded using a neural network. Operating across the visible-to- near-infrared range (400-1100 nm), the system attains an impressive spectral resolution of 6 nm. This method expands the application scenarios of multispectro-polarimetric imaging and provides a pathway for the manufacturability of metasurface devices. Polarimetry ; Broadband ; Computer science ; Polarimeter ; Optics ; Achromatic lens ; Physics Metamaterials and Metasurfaces Applications ; Optical Wireless Communication Technologies ; Plasmonic and Surface Plasmon Research
Authors: Andrew Leduc | Shanshan Zheng | Purvi Saxena | Nikolai Slavov
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-02-12)
Field: Chemistry — Spectroscopy

Impact of protein degradation and cell growth on mammalian proteomes Cellular protein concentrations are controlled by rates of synthesis and clearance, the latter including protein degradation and dilution due to growth. Thus, cell growth rate may influence the mechanisms controlling variation in protein concentrations. To quantify this influence, we analyzed the growth-dependent effects of protein degradation within a cell type (between activated and resting human B-cells), across human cell types and mouse tissues. This analysis benefited from deep and accurate quantification of over 12,000 proteins across four primary tissues using plexDIA. The results indicate that growth-dependent dilution can account for 40 % of protein concentration changes across conditions. Furthermore, we find that the variation in protein degradation rates is sufficient to account for up to 50 % of the variation in concentrations within slowly growing cells as contrasted with 7 % in growing cells. Remarkably, degradation rates differ significantly between proteoforms encoded by the same gene and arising from alternative splicing or alternate RNA decoding. These proteoform-specific degradation rates substantially determine the proteoform abundance, especially in the brain. Thus, our model and data unify previous observations with our new results and demonstrate substantially larger than previously appreciated contributions of protein degradation to protein variation at slow growth, both across proteoforms and tissue types. Degradation (telecommunications) ; Dilution ; Proteome ; Protein degradation ; Chemistry ; Cell biology ; Biology ; Computer science ; Biochemistry ; Physics Advanced Proteomics Techniques and Applications
Authors: Dongsheng Yang | Jian Li | Wen Yao Mak | Aole Zheng | Xiao Zhu | Qingfeng He | Yuzhu Wang | Xiaoqiang Xiang
Source: CPT Pharmacometrics & Systems Pharmacology (review, 2025-02-18)
Field: Chemistry — Spectroscopy

<scp>PBPK</scp> Modeling: Empowering Drug Development and Precision Dosing in China Physiologically based pharmacokinetic (PBPK) modeling, a cornerstone of model-informed drug development and model-informed precision dosing, simulates drug disposition in the human body by integrating physiological, biochemical, and physicochemical parameters. While PBPK modeling has advanced globally since the 1970s, China's adoption of this technology has followed a distinctive path, characterized by accelerated growth over the past 2 decades. This review provides a comprehensive analysis of China's contributions to PBPK modeling, addressing knowledge gaps in publication trends, application domains, and platform preferences. A systematic literature search yielded 266 original PBPK research articles from PubMed up to August 08, 2024. The analysis revealed that drug disposition and drug-drug interaction studies constitute the largest proportion of PBPK analyses in China. Chinese universities and hospitals emerge as the leading contributors to PBPK research among institutions in China. Although established commercial PBPK platform such as GastroPlus and Simcyp remain popular within the Chinese pharmaceutical industry, open-source platforms like PK-Sim are gaining significant traction in PBPK applications across China. This review underscores the transformative potential of PBPK modeling in drug development within China, offering valuable insights into future directions and challenges in the field. Physiologically based pharmacokinetic modelling ; China ; Drug development ; Pharmacology ; Drug ; Pharmacokinetics ; Medicine ; Geography Analytical Chemistry and Chromatography ; Pharmacogenetics and Drug Metabolism ; Microfluidic and Capillary Electrophoresis Applications
Authors: Siti Anis Dalila Muhammad Zahir | Mohd Faizal Jamlos | Ahmad Omar | Muhammad Aqil Hafizzan Nordin | Muna E. Raypah | Rizalman Mamat | Mohd Aminudin Jamlos | Jelena Munćan
Source: Smart Agricultural Technology (article, 2025-02-27)
Field: Chemistry — Analytical Chemistry

Quantifying the impact of varied NPK fertilizer levels on oil palm plants during the nursery stage: A Vis-NIR spectral reflectance analysis The utilization of Vis-NIR spectroscopy, coupled with advanced statistical analysis methods, offers enhanced precision in monitoring the nutrient status of plants compared to traditional practices. This research aims to explore the efficacy of spectroscopy as a method for detecting and monitoring plant nutrient levels. The reflectance spectra of AA Hybrida 1S oil palm plants were measured in the 325–1075 nm wavelength range. Different quantities and application intervals of nitrogen, phosphorus, and potassium (NPK) fertilizers were systematically examined to assess their impact on the physical condition and reflectance of the plants. The relationship between different fertilization methods and nutrient levels is analysed using spectral index and principal component analysis (PCA), employing and testing several spectral pre-processing techniques, such as moving average smoothing, Savitzky-Golay derivatives, and standard normal variate (SNV) transformation. Spectral index calculation showed with statistical significance that 0.8 g of NPK supplied every two weeks improved the nutrient conditions in plants after five months. Moreover, PCA analysis showed that SNV pre-processing enabled the best classification between stressed and non-stressed nutrient status. Among the spectral bands analysed, the red-edge band (660–770 nm) demonstrated significantly better performance than the green band (540–560 nm) in identifying nutrient-stressed conditions. This is attributed to the strong light absorption by chlorophyll in the red region and the pronounced reflection caused by leaf cell structures in the NIR region. Palm oil ; Fertilizer ; Reflectivity ; Spectral analysis ; Environmental science ; Agronomy ; Stage (stratigraphy) ; Biology ; Agroforestry ; Optics ; Spectroscopy Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Oil Palm Production and Sustainability
Authors: Xuan Yang | Chu Zhang | Shunda Qiao | Ying He | Yanchen Qu | Yufei Ma
Source: Chinese Optics Letters (article, 2025-01-01)
Field: Chemistry — Spectroscopy

Non-resonant quartz-enhanced photoacoustic spectroscopy In this paper, a non-resonant quartz-enhanced photoacoustic spectroscopy (NR-QEPAS) sensor is reported for the first time, to the best of our knowledge.The non-resonant photoacoustic cell (PAC) serves as the region where the photoacoustic effect occurs.NR-QEPAS offers several advantages, including flexible quartz tuning fork (QTF) positioning, frequencymatching-free operation, and simplified optical alignment.A self-designed T-head QTF was utilized as an acoustic wave transducer.The sound pressure characteristics of the non-resonant PAC were simulated using the finite element method.A near-infrared distributed feedback (DFB) diode laser with a wavelength of 1650.96nm was selected as the excitation source.Methane (CH 4 ) was chosen as the target gas to validate the designed sensor's performance.The experimental results showed that the designed non-resonant PAC worked in the plane wave state, and the sound pressure in the cavity was nearly uniform.The minimum detection limit (MDL) of the designed NR-QEPAS sensor for CH 4 detection could be 1.09 ppm (1 ppm = 10 -6 ) when the average time was 760 s. Optics ; Photoacoustic spectroscopy ; Materials science ; Photoacoustic imaging in biomedicine ; Spectroscopy ; Quartz ; Physics Spectroscopy and Laser Applications ; Atmospheric and Environmental Gas Dynamics ; Atmospheric Ozone and Climate
Authors: Tristan Jocteur | Éric Bertin | Romain Mari | Kirsten Martens
Source: Physical review. E (article, 2025-02-03)
Field: Materials Science — Materials Chemistry

Protocol dependence for avalanches under constant stress in elastoplastic models Close to the yielding transition, amorphous solids exhibit a jerky dynamics characterized by plastic avalanches. The statistics of these avalanches have been measured experimentally and numerically using a variety of different triggering protocols, assuming that all of them were equivalent for this purpose. In particular, two main classes of protocols have been studied, deformation under controlled strain or under controlled stress. In this work, we investigate different protocols to generate plasticity avalanches and conduct two-dimensional simulations of an elastoplastic model to examine the protocol dependence of avalanche statistics in yield-stress fluids. We demonstrate that when stress is controlled, the value and even the existence of the exponent governing the probability distribution function of avalanche sizes strongly depend on the protocol chosen to initiate avalanches. This confirms in finite dimension a scenario presented in a previous mean-field analysis. We identify a consistent stress-controlled protocol whose associated avalanches differ from the quasi-static ones in their fractal dimension and dynamical exponent. Remarkably, this protocol also seems to satisfy the scaling relations among exponents previously proposed. Our results underscore the necessity for a cautious interpretation of avalanche universality within elastoplastic models, and more generally within systems where several control parameters exist. Statistical physics ; Exponent ; Universality (dynamical systems) ; Scaling ; Plasticity ; Fractal dimension ; Renormalization group ; Fractal ; Physics ; Mechanics ; Mathematics ; Mathematical analysis ; Thermodynamics ; Condensed matter physics ; Geometry ; Quantum mechanics Material Dynamics and Properties ; Theoretical and Computational Physics ; Granular flow and fluidized beds
Authors: Robbe Devreese | Alireza Nameni | Arthur Declercq | Emmy Terryn | Ralf Gabriels | Francis Impens | Kris Gevaert | Lennart Martens | Robbin Bouwmeester
Source: Analytical Chemistry (article, 2025-07-08)
Field: Chemistry — Spectroscopy

Collisional Cross-Section Prediction for Multiconformational Peptide Ions with IM2Deep Peptide collisional cross-section (CCS) prediction is complicated by the tendency of peptide ions to exhibit multiple conformations in the gas phase. This adds further complexity to downstream analysis of proteomics data, for example for identification or quantification through feature finding. Here, we present an improved version of IM2Deep that is trained on a carefully curated data set to predict CCS values of multiconformational peptides. The training data is derived from a large and comprehensive set of publicly available data sets. This comprehensive training data set together with a tailored architecture allows for the accurate CCS prediction of multiple peptide conformational states. Furthermore, the enhanced IM2Deep model also retains high precision for peptides with a single observed conformation. IM2Deep is publicly available under a permissive open-source license at https://github.com/compomics/IM2Deep. Chemistry ; Ion ; Cross section (physics) ; Section (typography) ; Peptide ; Organic chemistry ; Biochemistry Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications
Authors: Taiyo Maeda | Toshio Osada | Shingo OZAKI
Source: Journal of the American Ceramic Society (article, 2025-05-12)
Field: Materials Science — Ceramics and Composites

Reliability evaluation scheme for ceramics based on defect size distribution inversely estimated from standardized tests Abstract Ceramic components fracture stochastically, resulting in scatter and size dependence of the strength. Therefore, the strength properties obtained from a standardized test cannot be directly used to determine the design strength of the arbitrary‐shaped components under different boundary conditions for brittle ceramics. In this study, we propose a novel strength evaluation scheme where an inversely analyzed defect distribution is used as a common indicator to evaluate the reliability of brittle ceramic components. First, the defect distribution of the target lot is inversely estimated as a common indicator based on experimental results of the bending strengths using the swarm intelligence optimization method. The scatter of the bending strength tested under different boundary conditions is then predicted using the estimated defect distribution. The analyzed defect distribution was found to be consistent with the observed distribution and can be used to predict other experimental results. The versatility and limitations of the scheme were discussed by examining the selection of a standardized strength test for optimization and the impact of model discretization on the inverse estimation of defect distribution. The results suggest that the proposed scheme can be applied to evaluate the strength scatter of components with arbitrary shapes and under arbitrary boundary conditions. Reliability (semiconductor) ; Reliability engineering ; Materials science ; Ceramic ; Composite material ; Engineering ; Thermodynamics ; Physics Advanced ceramic materials synthesis ; Aluminum Alloy Microstructure Properties ; Aluminum Alloys Composites Properties
Authors: Giannis Thalassinos | Daniel J. McCloskey | Alessandro Mameli | Alexander J. Healey | Charlie Pattinson | David Simpson | Brant C. Gibson | Alastair Stacey | Nikolai Dontschuk | Philipp Reineck
Source: APL Photonics (article, 2025-10-01)
Field: Materials Science — Materials Chemistry

Robust quantification of the diamond nitrogen-vacancy center charge state via photoluminescence spectroscopy Nitrogen vacancy (NV) centers in diamond are at the heart of many emerging quantum technologies, all of which require control over the NV charge state. Hence, methods for quantification of the relative photoluminescence intensities of the NV0 and NV− charge states, i.e., a charge state ratio, are vital. Several approaches to quantify NV charge state ratios have been reported but are either limited to bulk-like NV diamond samples or yield qualitative results. We propose an NV charge state quantification protocol based on the determination of sample- and experimental setup-specific NV0 and NV− reference spectra. The approach employs blue (400–470 nm) and green (480–570 nm) excitation to infer pure NV0 and NV− spectra, which are then used to quantify NV charge state ratios in subsequent experiments via least squares fitting. We test our dual excitation protocol (DEP) for a bulk diamond NV sample and 20 and 100 nm nanodiamond particles and compare results with those obtained via other commonly used techniques such as zero-phonon line fitting and non-negative matrix factorization. We find that DEP can be employed across different samples and experimental setups and yields consistent and quantitative results for NV charge state ratios that are in agreement with our understanding of NV photophysics. By providing robust NV charge state quantification across sample types and measurement platforms, DEP will support the development of NV-based quantum technologies. Charge (physics) ; Diamond ; Photoluminescence ; Nanodiamond ; Spectroscopy ; Excitation ; Charge-coupled device ; Analytical Chemistry (journal) Diamond and Carbon-based Materials Research ; Semiconductor materials and devices ; High-pressure geophysics and materials
Authors: Yuzhi Xu | Yongrui Luo | Bo Li | Weikang Jiang | Jinyu Zhang | Jiangbo Wei | Hanzhi Bai | Zhiqiang Wang | Jiankai Ge | Ruiming Lin | Zehan Mi | Haozhe Zhang | Yifeng Tang | Michael S. Jones | Xiao-Tian Li | John Z. H. Zhang | Cheng‐Wei Ju
Source: JACS Au (article, 2025-06-30)
Field: Materials Science — Materials Chemistry

Exploring Optimized Organic Fluorophore Search through Experimental Data-Driven Adaptive β-VAE Designing organic fluorescent molecules with tailored optical properties has been a long-standing challenge. Recently, statistical models have opened new avenues for tackling this problem. Inverse design has attracted considerable attention in organic materials science; however, most existing approaches focus on arbitrary design or theoretical properties. Here, we introduce a strategy that enables the direct optimization of specific experimental properties during the inverse design process. Our method employs an adaptive β-variational autoencoder (adaptive β-VAE) combined with a latent vector-based prediction model. By dynamically tuning the Kullback-Leibler divergence scaling factor (β) and employing a separate training strategy, we enhance both the robustness of the generator and the diversity of the generated molecules. We demonstrate that latent vectors from the adaptive β-VAE serve as powerful inputs for downstream prediction models of experimental properties, such as fluorescence energy and quantum yield. Our optimized search framework for organic fluorescent materialsguided by gradients in latent space and validated by newly synthesized molecules sampled from optimal regions in the high-dimensional spaceshows strong potential for broader applications in the design of diverse organic materials. Fluorophore ; Biological system ; Computer science ; Artificial intelligence ; Chemistry ; Fluorescence ; Biology ; Physics ; Optics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Aditya Nandy
Source: Journal of Catalysis (article, 2025-05-02)
Field: Materials Science — Materials Chemistry

From pages to patterns: Towards extracting catalytic knowledge from structure and text for transition-metal complexes and metal-organic frameworks • Examine differences in catalysts studied by different communities, leading to fragmented structural databases, distinct formats, and unique challenges for finite versus periodic systems. • Revisit how simple analyses over large sets of data may unveil correlative trends or reveal anthropogenically biased assumptions made on smaller data sets. • Emphasize the importance of generating and curating experimental structural and property data in a FAIR (findable, accessible, interoperable, and reusable) format. • Demonstrate how linking structural data with properties appropriately is insightful over large sets. • Highlight how increasingly sophisticated generative models on text and structural modalities will help future catalysis studies. Chemistry ; Transition metal ; Catalysis ; Metal ; Metal-organic framework ; Key (lock) ; Organic chemistry ; Computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Izumi Takahara | Teruyasu Mizoguchi | Bang Liu
Source: Cell Reports Physical Science (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

Accelerated inorganic materials design with generative AI agents Designing inorganic crystalline materials with tailored properties is critical to technological innovation, yet current generative computational methods often struggle to efficiently explore desired targets with sufficient interpretability. Here, we present MatAgent, a generative approach for inorganic materials discovery that harnesses the powerful reasoning capabilities of large language models (LLMs). By combining a diffusion-based generative model for crystal structure estimation with a predictive model for property evaluation, MatAgent uses iterative, feedback-driven guidance to steer material exploration precisely toward user-defined targets. Integrated with external cognitive tools-including short-term memory, long-term memory, the periodic table, and a comprehensive materials knowledge base-MatAgent emulates human expert reasoning to vastly expand the accessible compositional space. Our results demonstrate that MatAgent robustly directs exploration toward desired properties while consistently achieving high compositional validity, uniqueness, and material novelty. This framework thus provides a highly interpretable, practical, and versatile AI-driven solution to accelerate the discovery and design of next-generation inorganic materials. Generative grammar ; Generative Design ; Property (philosophy) ; Generative model ; Variety (cybernetics) Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Material Selection and Properties
Authors: Quang Khánh Nguyễn | Thi Thanh Thuy Phan | Ngoc Bich Tran | Dong Duong Tran | Tuan Minh Nguyen | Bach Pham | Thi Anh Huong Nguyen | Duc Thang Pham | Thi Ngoc Mai Pham
Source: Materials Chemistry and Physics (article, 2025-03-23)
Field: Materials Science — Materials Chemistry

Enhanced adsorption of sulfamethoxazole antibiotic on amine grafted activated carbon using (3-Aminopropyl) triethoxysilane (APTES) Triethoxysilane ; Adsorption ; Activated carbon ; Amine gas treating ; Chemistry ; Sulfamethoxazole ; Polymer chemistry ; Nuclear chemistry ; Organic chemistry ; Antibiotics ; Biochemistry Covalent Organic Framework Applications ; Advanced Photocatalysis Techniques ; Membrane Separation and Gas Transport
Authors: Kai Zong | Kejie Jiang | Xiaojun Bao | Dongshun Deng
Source: Journal of environmental chemical engineering (article, 2025-03-05)
Field: Materials Science — Materials Chemistry

Preparation of highly sulfonated hyper-crosslinked polymers as promising ammonia adsorbents with excellent performance Adsorption ; Ammonia ; Polymer ; Chemical engineering ; Materials science ; Chemistry ; Polymer science ; Polymer chemistry ; Organic chemistry ; Composite material Covalent Organic Framework Applications ; Adsorption and biosorption for pollutant removal ; Ammonia Synthesis and Nitrogen Reduction
Authors: Lei Peng | Yixiong Lin | Huang Zhang | Wei Xing Zheng | Bin Du | Penghui Xiao | Huaqiang Yin | Xuedong He | Tao Ma
Source: Powder Technology (article, 2025-01-18)
Field: Materials Science — Materials Chemistry

Investigation of the effective diffusion coefficient of nitrogen gas using high-resolution 3D X-ray computed tomography images of nuclear-grade graphite IG-110 Graphite ; Nuclear graphite ; Diffusion ; X-ray ; Tomography ; Computed tomography ; Nitrogen ; Resolution (logic) ; High resolution ; Materials science ; Chemistry ; Physics ; Optics ; Geology ; Radiology ; Computer science ; Medicine ; Composite material ; Artificial intelligence ; Thermodynamics ; Remote sensing Graphite, nuclear technology, radiation studies ; Nuclear Physics and Applications ; Nuclear reactor physics and engineering