jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ganesh Tupe*1, Monali Khatake2, Kanchan Vetal3, Archana Tupe4, Ashwini Amrutkar5
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-12-05)
Field: Chemistry — Spectroscopy

Recent Advances in High- Performance Liquid Chromatography (HPLC): Principles, Method Development Strategies, Modern Innovations and Applications on Pharmaceutical Analysis High-Performance Liquid Chromatography (HPLC) remains a cornerstone analytical technique widely applied in pharmaceutical, biological, food, and environmental sciences due to its exceptional sensitivity, accuracy, and versatility. Recent technological advancements have significantly enhanced its performance, with innovations such as ultra-high-performance liquid chromatography (UHPLC), improved instrumentation enabling faster analysis, greater resolution, and reduced solvent consumption. The emergence of multidimensional and hyphenated techniques including comprehensive two-dimensional liquid chromatography (2D-LC), LC–MS/MS, and high-resolution, structural elucidation, and trace-level quantification essential for complex matrices like biopharmaceuticals, natural products, and metabolomics. Parallel to these advancements, green analytical chemistry has driven the adoption of eco-friendly solvents, solvent-minimized approaches, nano-LC formats, and microextraction techniques, supporting sustainability without compromising analytical performance. Additionally, Analytical Quality by Design (AQbD) principles and automation, supported by statistical tools such as Design of Experiments (DoE), ensure robust method development and improved method reliability. The integration of artificial intelligence (AI) and machine learning has further accelerated chromatographic optimization, enabling automated gradient prediction, peak deconvolution, retention-time modelling, and efficient method transfer across instruments. Despite these innovations, regulatory-compliant method validation remains essential to confirm that HPLC procedures are fit for purpose, with parameters such as accuracy, precision, specificity, linearity, range, LOD, LOQ, and robustness defined by international guidelines from ICH, WHO, and FDA. Collectively, these technological, methodological, and regulatory advancements highlight the continuing evolution of HPLC as a powerful, future-ready analytical platform supporting high-quality research, quality control, and regulatory compliance. Robustness (evolution) ; Quality by Design ; High-performance liquid chromatography ; Instrumentation (computer programming) ; Quality (philosophy) ; Analytical technique Analytical Chemistry and Chromatography ; Metabolomics and Mass Spectrometry Studies ; Pesticide Residue Analysis and Safety
Authors: Jia Cheng | Xiyuan Chen | Haifan Li | Yongheng Zhao | Han Li | Weizhe Sun | Yuan Li
Source:  (article, 2025-06-20)
Field: Chemistry — Analytical Chemistry

Feature Selection of Insulating Paper Near-Infrared Spectroscopy by SHAP for Enhancing the Interpretability In recent years, near-infrared (NIR) spectroscopy has been used for the nondestructive assessment of the degree of polymerization (DP) of insulating paper, which is a key indicator of its aging degree. The NIR spectra of insulating paper typically contain hundreds or even thousands of spectral features, and the complex nonlinear relationship between these high-dimensional features and DP can be captured by machine learning algorithms, enabling DP prediction model. However, only a small subset of these features is truly relevant to the aging of insulating paper. Identifying and selecting these key features can improve model’s accuracy and interpretability. In this study, SHapley Additive exPlanations (SHAP) was used to interpret the impact of spectral features on the DP prediction model and extract key features. Insulating paper samples with different aging degrees were prepared by accelerated thermal aging experiment, their NIR spectra were collected, and their DP was determined. A DP prediction model was then developed using all spectral features and Random Forest. SHAP analysis was used to evaluate feature contribution to the model’s predictions of the entire dataset, as well as high and low DP subsets. Based on SHAP feature importance, key features were selected for further modeling, and the performance of models using different numbers of features was compared. The results show that the feature at 1115 nm is the key feature for identifying the aging degree of insulating paper, and the root mean square error of the model modeled using the key features is lower than that of the model modeled using all features (60.0725 vs. 68.2673). Interpretability ; Feature (linguistics) ; Key (lock) ; Feature selection ; Pattern recognition (psychology) ; Spectral shape analysis Spectroscopy and Chemometric Analyses ; Power Transformer Diagnostics and Insulation ; Advanced Cellulose Research Studies
Authors: Shuyan Zhao (1844692) | Boyan Zhao (14996973) | Yong Wang (12837) | Yanrong Li (1762939) | Essy Kouadio Fodjo (11413650) | Wei Deng (17330) | Dan Li (106345)
Source: Figshare (article, 2025-12-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Utilizing a SHAP-AssistedMachine Learning Algorithmfor Optimization of Plasmonic Properties of Gold Nanostructure Gold nanostructures (GNSs) have tunable optical absorption and catalytic properties and thus are increasingly used in molecular sensing and nanomanufacturing. However, the rapid design of GNSs with specific optical properties is limited by various factors, making it difficult to achieve efficient design by conventional trial-and-error methods. Here, we report a SHapley Additive exPlanations (SHAP)-assisted extreme gradient boosting (XG-Boost) model for conducting the preparation of GNSs with preferential plasmonic features using silver nitrate (AgNO<sub>3</sub>) and ascorbic acid in different surfactants at room temperature. The XG-Boost model achieves exceptional predictive accuracy (<i>R</i><sup>2</sup> > 0.98, RMSE < 0.03). Importantly, SHAP elucidates the underlying mechanism linking synthetic conditions to plasmonic properties of GNSs, particularly revealing the contribution of surfactants to predictions, which was previously underestimated. Guided by the XG-Boost-SHAP strategy, GNSs with tunable localized surface plasmon resonance peaks and high stability are designed and precisely synthesized in various shapes. The GNSs were successfully applied to thiourea detection, Hg<sup>2+</sup> recognition, glucose analysis, and fabrication of luminescent chips. The results demonstrate the remarkable potential of the machine-learning framework in tailoring GNSs with personalized features to realize various objectives. Nanostructure ; Plasmon ; Surface plasmon resonance ; GNSS applications ; Fabrication ; Stability (learning theory) Gold and Silver Nanoparticles Synthesis and Applications ; Machine Learning in Materials Science ; Laser-Ablation Synthesis of Nanoparticles
Authors: Caini Zheng (11178283) | Ke Luo (4786086) | Soumi Das (11783304) | Pengyu Chen (1312830) | Zhengyuan Shen (6831536) | Joseph Jaye (21850456) | Daoyuan Li (9753239) | Michelle A. Calabrese (11516796) | Theresa M. Reineke (1271100) | Mahesh K. Mahanthappa (1326048) | Kevin D. Dorfman (1436092) | Timothy P. Lodge (1271460) | J. Ilja Siepmann (1308504)
Source: Figshare (article, 2025-07-31)
Field: Materials Science — Biomaterials

Exploring the Self-Assemblyof Glycolipids into Three-DimensionalNetwork Phases Glycolipids are sugar-based amphiphiles that play crucial roles in many biological processes. Under thermotropic and lyotropic conditions, glycolipids self-assemble into a variety of mesophases, including cocontinuous network phases, such as the double gyroid. In this work, a two-stage molecular dynamics simulation workflow is developed to probe network formation for solvent-free amphiphiles at different temperatures. In the first stage, the structural evolution of systems initiated in lamellar and hexagonally packed cylinder arrangements is examined as an indicator of the likelihood of network formation. In the second stage, initial configurations for four network phases are obtained by applying an external guiding field to overcome any nucleation barriers, and the stability of these network phases after switching off the guiding field is investigated. A convolutional neural network is trained and applied to assign a morphology to each snapshot from the simulation. Three anomerically pure and water-free glycolipids (2-decyl-tetradecyl-d-maltoside, 2-decyl-tetradecyl-d-cellobioside, and 2-decyl-tetradecyl-d-galactoside) were examined in this study, and the predicted phase diagrams show good alignment with experimental observations from differential scanning calorimetry and temperature-dependent small-angle X-ray scattering. The workflow presents a facile approach to probe network phase stability and paves the way for the discovery of new network-forming amphiphiles. Thermotropic crystal ; Glycolipid ; Lyotropic ; Amphiphile ; Lamellar structure ; Nucleation ; Differential scanning calorimetry ; Workflow Supramolecular Self-Assembly in Materials ; Lipid Membrane Structure and Behavior ; Surfactants and Colloidal Systems
Authors: Shurui Cao | Lihong Mou | Jie Tian | Hongtao Su | Yao Tang | Zhiqiong Chen
Source: Journal of Water Process Engineering (article, 2025-10-25)
Field: Materials Science — Materials Chemistry

Multimethod synergy: Predicting opioids adsorption and dissecting mechanisms via experiments, machine learning, and quantum calculations Adsorption ; Density functional theory ; Hydrogen bond ; Opioid ; Molecule ; Morphine ; Quantum chemical Machine Learning in Materials Science
Authors: Shaoyang Qu
Source:  (article, 2025-11-21)
Field: Materials Science — Materials Chemistry

Analysis of thermal transport and optical absorption in XBi2 monolayers based on first-principles calculations This paper aims to provide theoretical support for the design of high-performance thermoelectric-photoelectric integrated devices. First, the geometric structure of the XBi<sub>2</sub> monolayer is optimized based on density functional theory to verify its thermodynamic and kinetic stability. Second, the Heyd-Scuseria-Ernzerhof 2006 (HSE06) hybrid functional is used to correct the electronic band structure, and the Seebeck coefficient, electrical conductivity, and thermoelectric figure of merit are calculated by combining Boltzmann transport theory. Finally, the Sheng Boltzmann Transport Equation (ShengBTE) program combined with the High-dimensional Force Constant Potential (HiPhive) machine learning method is employed to simulate the phonon thermal conductivity, and the Green’s function (G) and screened Coulomb interaction (W) combined with Bethe-Salpeter Equation (GW-BSE) method is used to study the influence of exciton effects on light absorption properties. The results show that at 300 K, the maximum Seebeck coefficient of the SnBi<sub>2</sub> monolayer is 240 &mu;V·K<sup>-1</sup>, the power factor reaches 1.37 × 10<sup>-3</sup> W·m<sup>-1</sup>·K<sup>-2</sup>, and the highest thermoelectric figure of merit is 1.14. In terms of optical properties, the XBi<sub>2</sub> monolayer has a minimum absorption edge energy of 0.83 eV, a maximum absorption coefficient exceeding 10<sup>5</sup> cm<sup>-1</sup>, and an exciton binding energy as high as 430~520 meV, demonstrating strong exciton effects and excellent optical response capabilities. The XBi<sub>2</sub> monolayer materials combine excellent thermal transport and optical absorption properties, making them potential multifunctional two-dimensional (2D) energy materials. Monolayer ; Thermoelectric effect ; Seebeck coefficient ; Boltzmann equation ; Exciton ; Absorption (acoustics) ; Figure of merit ; Phonon ; Density functional theory Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Topological Materials and Phenomena
Authors: Li, Yong | Hu, Kaixiong | Lilleodden, Erica | Huber, Norbert
Source: TUHH Open Research (article, 2025-11-04)
Field: Materials Science — Materials Chemistry

Datasets for structural and mechanical properties of nanoporous networks from FIB reconstruction Nanoporous ; Volume fraction ; Finite element method ; Volume (thermodynamics) ; Iterative reconstruction ; 3D reconstruction ; Elastic modulus ; Compression (physics) Nanoporous metals and alloys ; Cellular and Composite Structures ; Anodic Oxide Films and Nanostructures
Authors: Man Zhang (466843) | Yunqiang Zhang (661232) | Yanchao Zhang (4905979) | Wantong Zhang (5749799) | Xiangmin Zhang (813476) | Jingxin Ding (11918763) | Nianrong Sun (1774744) | Chunhui Deng (1609939)
Source: Figshare (article, 2025-10-16)
Field: Chemistry — Spectroscopy

Serum Fingerprinting-BasedIntegrative Dual-OmicsMachine Learning for Endometriosis-Associated Ovarian Cancer Dual-omics, by integrating molecular information from two distinct dimensions, can offer more comprehensive perspective for complex disease. Herein, we developed an efficient functionalized mesoporous nanoparticle-coupled laser desorption/ionization mass spectrometry (fMNPLDI-MS) platform capable of extracting high-quality serum metabolic fingerprints (SMFs) and serum peptide fingerprints (SPFs) from trace serum samples within 50 s. Integrating these SMFs and SPFs markedly improved early screening and subtyping of endometriosis-associated ovarian cancer (EAOC) more than single omics. Specifically, leveraging machine learning on the identified 6 metabolites and 6 peptides, the integration strategy attained an area under the receiver operating characteristic curve (AUC) value of 0.989 and accuracy of 93.1%, compared with 0.964/89.7% for metabolomics alone and 0.960/87.9% for peptidomics alone in distinguishing EAOC from benign controls. Similarly, the dual-omics integration strategy achieved an AUC value of 0.875 and accuracy of 86.7%, surpassing individual metabolomics (AUC: 0.869, accuracy: 83.3%) and peptidomics (AUC: 0.733, accuracy: 76.7%), in subtype classification. This high-throughput fMNPLDI-MS dual-omics platform provides a powerful tool for EAOC screening and subtyping, paving the way toward early detection and precision management of this malignancy. Subtyping ; Ovarian cancer ; Metabolomics ; Receiver operating characteristic ; Cancer ; Ovarian carcinoma Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; GDF15 and Related Biomarkers
Authors: Yong Lyu | Yunhai Jia | Weihao Wan | Xiaofen Zhang | Sheng Liang | Haizhou Wang
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — General Materials Science

Development and Application of an Instrument for Microstructure Matrix Inclusions Distribution Analysis in Oversized Metallic Materials Microstructure ; Matrix (chemical analysis) ; Materials science ; Metal ; Non-metallic inclusions ; Metallurgy ; Composite material Material Properties and Applications ; Aluminum Alloy Microstructure Properties ; Metallurgy and Material Forming
Authors: Ohmin Kwon (2606476) | Marco Gibaldi (13822259) | Kasturi Nagesh Pai (5469767) | Arvind Rajendran (1749316) | Tom K. Woo (1332735)
Source: Figshare (article, 2025-07-07)
Field: Chemistry — Inorganic Chemistry

Identificationof Metal–Organic Frameworksfor near Practical Energy Limit CO<sub>2</sub> Capture from Wet FlueGases: An Integrated Atomistic and Process Simulation Screening ofExperimental MOFs Metal–organic framework (MOF) materials have attracted significant attention as solid sorbents for low energy CO<sub>2</sub> capture with adsorption-based gas separation processes. In this work, an integrated screening workflow combining a series of atomistic and process simulations was applied to identify promising MOFs for a 4-step pressure-vacuum swing adsorption (P/VSA) process at three different CO<sub>2</sub> flue gas compositions (6%, 15% and 35%). Starting from 55,818 unique experimentally characterized MOFs, ∼19k porous MOFs were investigated via atomistic grand canonical Monte Carlo (GCMC) simulations and machine learning model-based process optimizations to accelerate the screening of a large candidate database. Thousands of MOFs were identified for each of the CO<sub>2</sub> compositions tested that could achieve within 4% of the practical energy limit of dry CO<sub>2</sub> capture for the P/VSA process while still meeting the 95% CO<sub>2</sub> purity and 90% recovery constraints. From this pool, 3D MOFs without open metal sites were subjected to the multicomponent (CO<sub>2</sub>/N<sub>2</sub>/H<sub>2</sub>O) GCMC simulations at 40% relative humidity. Based on these simulations, hundreds of MOFs were identified at each CO<sub>2</sub> composition that could retain 90% of their CO<sub>2</sub> capture at this humidity while also adsorbing a minimal amount of water. A geometric analysis of these high performing materials revealed that narrow, straight 1D-channels were a common structural motif for low energy wet flue gas CO<sub>2</sub> capture with P/VSA. Flue gas ; Monte Carlo method ; Process (computing) ; Limit (mathematics) ; Porosity ; Adsorption ; Workflow ; Metal-organic framework Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Covalent Organic Framework Applications
Authors: Georgia Kesisoglou | Evangelia Lampropoulou | Stilianos Arhondakis | Athanasia‐Maria Dourou
Source:  (article, 2025-06-04)
Field: Chemistry — Organic Chemistry

A Machine Learning pipeline for the automated DNA-based classification of Italian olive cultivars: from leaves to olive oil The origin and identity of our food is becoming an essential question for the modern consumer. Therefore, it becomes imperative to adopt and employ technologies that enhance consumers’ trust. Among different applications towards this matter, the utilization of molecular markers as a tool for genetic characterization of olive oil and/or table olives are gaining ground. These DNA-based tools not only enhance product transparency, but bring a significant economic, environmental and societal positive impact to the industry. In the herein study we present a machine learning (ML) pipeline targeting the automated classification of olive cultivars by post-processing real-time PCR High-Resolution Melting (HRM) outputs using Single Nucleotide Polymorphism (SNP). As a case study we focus on four economically important Italian olive cultivars, namely Leccino, Moraiolo, Dolce d’Agogia and Rajo, with overall classification accuracy of the ML model reaching 0.92 as trained using raw data of 682 HRM experiments. Additionally, for the first time olive oil samples are classified using the herein proposed ML pipeline, adding promising results for future work targeting the varietal composition of olive oil. Concluding, the ML pipeline accelerate time to results, increases consistency as it minimizes human bias, and eliminates the necessity of certified reference samples. Olive oil ; Pipeline (software) ; Product (mathematics) ; Consistency (knowledge bases) ; Table (database) ; Raw material Edible Oils Quality and Analysis ; Identification and Quantification in Food ; Spectroscopy and Chemometric Analyses
Authors: Yuhang Li | Haisu Li | Li Wang | Shaghik Atakaramians | Guobin Ren | Li Pei
Source: Optics Express (article, 2025-11-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Inverse optimization design of terahertz topological waveguides towards on-chip communication This study proposes an inverse design framework for terahertz topological waveguides to achieve optimal on-chip communication performances. The topological waveguides are based on valley photonic crystals, which support two low-loss transmission bands (0.25 - 0.29 THz and 0.29 - 0.34 THz) within the topological bandgap. We train a deep neural network (DNN) to achieve high-precision prediction from photonic structural parameters to waveguide performances (including topological bandgap, group velocity dispersion, and transmission loss). After 30,000 epochs, the DNN prediction error is down to 10<sup>-6.5</sup>. Subsequently, combining the DNN of topological waveguides with a terahertz on-chip communication link, a direct mapping relationship (i.e., a forward design flow) between the waveguide structure and the communication performance [including the bit error rate (BER) and transmission bandwidth] is established. Furthermore, the particle swarm optimization is applied to the forward design model to inverse optimization the topological waveguide structure, aiming to maximize the communication bandwidth while meeting the forward error correction threshold (BER below 10<sup>-3</sup>). Optimization results show that at frequencies around 0.28 THz and 0.31 THz, the topological waveguide supports two low-BER 3.33-Gbps terahertz on-chip communication windows, with bandwidths of 14.81 GHz and 13.67 GHz, respectively. This research realizes the "on-demand" inverse design of microscopic waveguide structures based on macroscopic communication performance requirements, providing an efficient and convenient approach for intelligent optimization of terahertz on-chip communication waveguides. Terahertz radiation ; Particle swarm optimization ; Waveguide ; Inverse ; Topology (electrical circuits) ; Transmission (telecommunications) ; Photonics ; Topology optimization ; Bandwidth (computing) Metamaterials and Metasurfaces Applications ; Photonic Crystals and Applications ; Topological Materials and Phenomena
Authors: Min Wu (41570) | Songlong Jiao (7401200) | Lei Liu (5074)
Source: Figshare (article, 2025-09-04)
Field: Materials Science — Materials Chemistry

Nonmagnetic BiomarkerDetection by Nanocatalytic-AmplifiedQuantum Spin Relaxometry Nitrogen-vacancy (NV) centers in diamond demonstrate advantages in biosensing due to their exceptional photostability and long spin coherence time. However, clinical applications of NV centers are significantly limited because their spin states lack responsiveness to nonmagnetic biomolecules. This work presents a nanocatalytic-amplified quantum sensing platform targeting tyrosinase (TYR), a key biomarker for melanoma. The platform achieves ultrasensitive detection of nonmagnetic biomarkers through nanocatalytic signal transduction. Specifically, we develop plasmonic nanodiamond (FND@Au–Ag) functionalized hydrogel microneedles. The captured TYR triggers a cascade reaction that induces the nanocatalytic generation of hydroxyl radicals (•OH), thereby establishing an AND logic gate based on the dynamic response mechanism of spin relaxation time (<i>T</i><sub>1</sub>). By integrating three-modal signals (fluorescence, colorimetry, and <i>T</i><sub>1</sub>), the platform achieves a wide dynamic detection range of 0.01–200 U/mL and an ultralow detection limit (0.003 U/mL) for TYR. Ultimately, a genetic algorithm-optimized backpropagation neural network is developed to enhance the reliability of concentration prediction in complex biological matrices. This work proposes an approach to minimally invasive melanoma screening, overcoming the inherent limitations of traditional quantum sensing systems in detecting nonmagnetic biomarkers. Nanodiamond ; Quantum ; Spin (aerodynamics) ; Limiting ; Coherence (philosophical gambling strategy) ; Nanosensor ; Biomarker ; Quantum sensor ; Detection limit Diamond and Carbon-based Materials Research ; Graphene research and applications ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Limo Limo | Hassan Hassanzad Azar | Saeedeh Shojaee‐Aliabadi | Majid Aminzare
Source: SSRN Electronic Journal (article, 2025-01-01)
Field: Materials Science — Biomaterials

Galbanum Nanocomposite Coating Containing Satureja Khuzestanica Essential Oil for Prolonging Shrimp (Metapenaeus affinis) Shelf Life at Various Temperatures Shrimp ; Shelf life ; Fishery ; Coating ; Nanocomposite ; Chemistry ; Biology ; Food science ; Materials science ; Nanotechnology ; Organic chemistry Nanocomposite Films for Food Packaging