jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jacob Lynge Elholm | Paulius Baronas | Paul Augustin Gueben | Victoria Gneiting | Helen Hölzel | Kasper Moth‐Poulsen
Source: Digital Discovery (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

An automated photo-isomerisation and kinetics characterisation system for molecular photoswitches Physical chemistry parameters such as absorbance, photoconversion quantum yield, and thermal half-lives are crucial for the characterisation of new molecular photoswitch systems. In a traditional workflow, these parameters are challenging and time-consuming to measure. In this paper, a high-throughput flow-based photoswitch characterisation platform with a built-in broad-spectrum LED array and thermal back-conversion capabilities is developed with UV-Vis spectroscopic analysis tools to reduce materials consumption, limit laborous workflows, and improve experimental reproducibility. Following the experiments, an in-house developed Python program is used for easy and fast data analysis. The program is designed to be able to analyse different types of photoswitches depending on the molecular properties. The specific components and configurations are detailed, enabling reproducibility and adaptation to various experimental needs. This system demonstrates the potential for efficient, high-throughput analysis in spectroscopic studies. Wide applicability is underlined by showing the results and comparison of three different photoswitch types, norbornadienes, bicyclooctadienes, and azobenzenes. The results we obtain are in good agreement with reported values in the literature. Photoswitch ; Isomerization ; Kinetics ; Quantum yield ; Molecular machine ; Photochromism ; Workflow ; Photochemistry ; Quantum ; Chemistry ; Nanotechnology ; Materials science ; Computer science ; Physics ; Organic chemistry ; Optics ; Catalysis Photochromic and Fluorescence Chemistry ; Molecular Junctions and Nanostructures ; Photochemistry and Electron Transfer Studies
Authors: Amar Mukherjee | Mohammad Reza Chalak Qazani | B. M. Jewel Rana | Shamima Akter | Amir Hossein Mohajerzadeh | Nusrat Jahan Sathi | Lasker Ershad Ali | Md. Salauddin Khan | Houshyar Asadi
Source: Applied Soft Computing (article, 2025-06-18)
Field: Chemistry — Analytical Chemistry

SMOTE-ENN resampling technique with Bayesian optimization for multi-class classification of dry bean varieties The imbalanced classification problem poses a significant challenge in machine learning, often resulting in biased models and poor performance for minority classes. This study introduces an innovative hybrid resampling technique combining Synthetic Minority Oversampling Technique and Edited Nearest Neighbours (SMOTE-ENN), optimized using Bayesian Optimization, to address these limitations. The proposed framework integrates advanced feature pre-processing, hybrid resampling, and machine learning models to enhance classification performance. Using the publicly available dry bean dataset containing 16 geometric features of seven seed varieties, the methodology demonstrates remarkable improvements in predictive accuracy and class balance. Employing cutting-edge classifiers, the improved Light Gradient Boosting Machine (LBM) with Bayesian optimization achieved an unprecedented accuracy of 99.59%, outperforming traditional approaches. Results reveal the potential of hybrid resampling techniques and Bayesian optimization in effectively capturing feature patterns, enhancing model diversity, and ensuring robust classification of imbalanced datasets. This research underscores the application of soft computing methods to real-world multi-class classification challenges, offering practical insights for similar domains. ∙ An efficient machine learning framework is designed to meet the demands of producers and customers for food security. ∙ Feature selection strategy is used to reduce the risk of overfitting by removing redundant features and strengthen the model predictive power. ∙ Hybrid resembling technique improves the model’s ability to generalize to new seed or unknown seed, reducing classification bias towards the majority classes. ∙ Confusion matrix is followed to represents how the classification model is confused when it makes predictions. That is, it provides insight not only the errors which are made by the classifier but also types of errors that are being made. ∙ Feature importance is analyzed to illustrate, which types of geometric features influence the model’s predictions significantly. Resampling ; Bayesian probability ; Class (philosophy) ; Computer science ; Pattern recognition (psychology) ; Artificial intelligence ; Naive Bayes classifier ; Dry bean ; Machine learning ; Mathematics ; Support vector machine ; Botany ; Biology Spectroscopy and Chemometric Analyses ; Advanced Algorithms and Applications ; Smart Agriculture and AI
Authors: James H. Zothantluanga | Dipak Chetia | Yasangam Umbon | T. C. Lalhriatpuii | Dhritiman Roy | Nadine Lamare | Salem Lalvenhimi
Source: Future Journal of Pharmaceutical Sciences (article, 2025-02-06)
Field: Chemistry — Analytical Chemistry

Pharmacognostic analysis and antimalarial evaluation of quercetin in Ilex umbellulata bark using HPTLC, in vitro screening, molecular docking, and network pharmacology Abstract Background The bark of Ilex umbellulata is traditionally used for the treatment of many diseases such as malaria. Despite its traditional relevance, the pharmacognostic parameters and pharmacological properties remained unexplored. In this study, we aim to develop the missing pharmacognostic parameters with modern analytical techniques and carry out multi-step computational studies to study the antimalarial potential of I. umbellulata . Results The bark was 2–6 mm thick, composed of different colored layers, and was bitter-sweet in taste. Powdered microscopy revealed the presence of starch granules, calcium oxalate crystals, cork cells, trichomes, and fibers. Physicochemical properties such as ash values (total, acid-insoluble, and water-soluble), extractive values (petroleum ether, chloroform, ethyl acetate, methanol, aqueous, 80% MeOH), moisture content, swelling index, fluorescence, and pH of the bark were determined. FT-IR fingerprint profiling of petroleum ether, chloroform, ethyl acetate, methanol, aqueous, and 80% MeOH extracts revealed characteristic bands at different wavelengths that are indicative of the presence of certain functional groups. HPTLC fingerprint profiling with a mobile phase of hexane: ethyl acetate: formic acid (4.5:5.5:0.5 v/v) revealed 9 characteristic peaks. With a mobile phase of toluene: ethyl acetate: formic acid (5:4:0.2 v/v), the validated TLC densitometric studies revealed the presence of 2.07 mg of quercetin ( R f = 0.477 ± 0.005) in 100 mg of 80% MeOH bark extract of I. umbellulata . JazQSAR web tool previously developed by us predicts the IC 50 of quercetin against Plasmodium falciparum as 3.88 ± 0.35 µM, which was not far from the practically observed value for quercetin. Multi-target molecular docking with a validated docking protocol revealed that quercetin could potentially interact with 20 proteins of P. falciparum that are highly expressed during the schizont and trophozoite stages. Network pharmacology studies revealed that quercetin could potentially alleviate malaria mainly by inhibiting pro-inflammatory response through the action of IL-4, IL-10, and IL-13 and by triggering the immune system. Conclusions The pharmacognostic parameters of I. umbellulata bark may be used as quality control parameters to aid in identification and authentication and to prevent adulteration. The results obtained from the multi-target molecular docking and network pharmacology studies support the use of I. umbellulata as a traditional herbal remedy against malaria. Chemistry ; Pharmacology ; Traditional medicine ; Quercetin ; In vitro ; Chromatography ; Medicine ; Biochemistry Heavy Metals in Plants ; Morinda citrifolia extract uses ; Pharmacological Effects of Medicinal Plants
Authors: Ankita Parashar - | Lalit Kumar Tyagi | Vandana Arora Sethi | Kuldeep Bansal
Source: International Journal of Newgen Research in Pharmacy & Healthcare (article, 2025-08-01)
Field: Materials Science — Biomaterials

Advanced Nanotheranostics: Evolving Strategies in Cancer Therapy and Diagnosis The review article delves into the emerging field of nanotheranostics, which synergistically combines therapeutic and diagnostic applications to fight cancer more effectively. With an aim to monitor treatment response, enhance drug efficacy, and ensure patient safety, nanotheranostics represents a cutting-edge frontier in personalized medicine. This review examines the most recent progress in cancer imaging and diagnosis, discussing the use of innovative molecular imaging techniques. It emphasizes the development of various nanotheranostic agents, including gold nanoparticles, silica, liposomes, dendrimers, and other metal-based agents, and their respective targeting mechanisms for tumor cells. Importantly, the article sheds light on the complexities in targeting and delivering therapeutic agents to the correct site of action, highlighting multifunctional and hybrid nanoparticle systems as particularly potent tools in the early detection and treatment of cancer. The review also discusses the specific interactions of nanosystems with cancer cells, touching upon the crucial aspects of receptor binding and nanosystem internalization. Challenges such as the intricate and sometimes unclear mechanisms of nanoparticles, their biodistribution, and their potential hazardous behavior are discussed as critical concerns that need to be addressed for nanotheranostics to reach its full potential in clinical settings. The present review underscores the transformative promise of nanotheranostics in cancer care, advocating for its advancement towards meaningful clinical applications. Cancer therapy ; Cancer ; Medicine ; Intensive care medicine ; Internal medicine Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Nanoparticles: synthesis and applications
Authors: Samuel D. Dunham | Yinling Zhang | Nan Chen | Coleman Alleman | Curt A. Bronkhorst
Source: International Journal of Plasticity (article, 2025-02-03)
Field: Materials Science — Materials Chemistry

Attribution of heterogeneous stress distributions in low-grain polycrystals under conditions leading to damage In high-purity polycrystalline metallic materials, voids tend to favor grain boundaries as nucleation sites due to the elevated stress states produced by granular interactions and the weakened grain boundary from the relative atomic disorder. To quantify the key factors of this elevated stress state, simple compression of a small multi-grain cylinder of body-centered cubic tantalum was simulated using a single crystal plasticity model that incorporates non-Schmid effects. Four increasingly complex synthetic microstructures were created to tractably incorporate grain boundary interactions, and a statistically significant number of combinations were performed by varying the initial crystallographic orientations of the microstructure. Most of these simulations produce the maximum von Mises stress on a grain boundary and less frequently at the multi-grain junctions. To build a statistical model for the maximum von Mises stress at the grain boundary, physically based features that could contribute to the elevated stress state were selected. Then, a learning algorithm based on information theory was used to identify which of these features contributed the most information to the data set. The identified features include a grain’s propensity to accommodate both elastic and plastic deformations and their directional components. The misalignment of the direction of each grain’s mechanical response was found to be strongly correlated to the magnitude of the stress near the grain boundary. For all of the synthetic microstructures, the statistical models produce a residual distribution that is nearly Gaussian with a variance of, at most, 10% of the prior distribution. The successful performance of the statistical model implies the correct identification of the physical features that cause severe stress localization in polycrystalline materials . The statistical models constructed here can be used to formulate a physically motivated void nucleation model which is sensitive to a microstructure’s propensity to produce elevated stress states. These statistical models also enable the design of material microstructures , in which the crystallographic orientation is chosen to resist void nucleation. • A crystal plasticity model for BCC Ta that accounts for non-Schmid effects is presented. • Four synthetic microstructures are deformed under conditions that lead to damage. • The distribution of maximum stress near grain boundaries is non-Gaussian. • Statistical models are developed using microstructural information. • The statistical model accurately characterizes the results in each microstructure. Materials science ; Stress (linguistics) ; Composite material ; Metallurgy ; Forensic engineering Material Properties and Failure Mechanisms ; Microstructure and mechanical properties ; High-Velocity Impact and Material Behavior
Authors: Radovan Búreš | V. V. Milyutin | Mária Fáberová | Zuzana Birčáková | P. Kollář | J. Füzer
Source: Scientific Data (article, 2025-01-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

The first experimentally obtained dataset on processing parameters and properties of soft magnetic composites The present work describes the process of the creation and analysis of the first dataset containing processing parameters and functional properties of soft magnetic composites (SMC). All data were obtained experimentally using Fe-3% MgO system. When creating samples, parameters such as a size of MgO nanoparticles, pressing pressure, sintering temperature, time and atmosphere were varied. In total, 282 samples with a unique combination of processing parameters were obtained. In each sample, density, real part of complex magnetic permeability at different frequencies, coercivity, and resonant frequency were measured. This allowed us to create the first experimentally obtained dataset devoted to SMC. Such dataset is necessary for implementing data-driven research in the field of SMCs, as well as for studying correlations in the chain: processing parameters - structure - properties. The dataset is currently being expanded both in terms of expanding the set of variable independent parameters and in terms of expanding the set of controlled properties. The dataset is hosted in Figshare open repository. Coercivity ; Pressing ; Materials science ; Sintering ; Magnetic field ; Permeability (electromagnetism) ; Set (abstract data type) ; Biological system ; Computer science ; Composite material ; Data mining ; Physics ; Condensed matter physics Magnetic Properties and Applications ; Metallic Glasses and Amorphous Alloys ; Magnetic Properties of Alloys
Authors: Yuqi He | Xinyan Zhao | Maoying Yu | Dixiong Yang | Lian Chen | Ce Tang | Yi Zhang
Source: Molecules (review, 2025-01-30)
Field: Chemistry — Analytical Chemistry

Affinity Ultrafiltration Mass Spectrometry for Screening Active Ingredients in Traditional Chinese Medicine: A Review of the Past Decade (2014–2024) Discovering targets in natural products is a critical and challenging task in new drug development. Rapid and efficient screening of active ingredients from complex systems like traditional Chinese medicine (TCM) is now crucial in drug research. Affinity ultrafiltration (AUF) technology is widely used to screen active ingredients in natural medicines. AUF-liquid chromatography-mass spectrometry (AUF-LC-MS) leverages the affinity between natural medicine extracts and targets to isolate active ingredients from complex matrices, employing LC-MS for detection and activity assessment. This review discusses the developments in employing AUF-LC-MS to analyze TCM and TCM compound preparations over the last decade. This review succinctly presents the advantages and limitations of AUF-LC-MS, illustrating its benefits through the example of screening for active ingredients in natural pharmaceuticals. Active ingredient ; Ultrafiltration (renal) ; Traditional Chinese medicine ; Chromatography ; Chemistry ; Mass spectrometry ; Traditional medicine ; Biochemical engineering ; Medicine ; Pharmacology ; Engineering ; Alternative medicine Chromatography in Natural Products ; Pharmacological Effects of Natural Compounds ; Analytical Chemistry and Chromatography
Authors: Zhuo Han | Hang Liu | Dongya Zhao | Yurong Chen | Yupeng Xing | Zixuan Zhang
Source: Processes (article, 2025-04-29)
Field: Materials Science — Materials Chemistry

Monte Carlo Sensitivity Analysis for a Carbon Capture, Utilization, and Storage Whole-Process System Carbon capture, utilization, and storage (CCUS) is an emerging technology with significant potential for large-scale emissions reduction. To reduce the overall system costs of CCUS, this study first establishes a comprehensive economic cost model for the entire CCUS process. Subsequently, a Monte Carlo-based Sobol’ global sensitivity analysis method is proposed to calculate both first-order and total-order sensitivity indices, followed by qualitative and quantitative analyses of parameter sensitivity. Additionally, convergence analyses of the results and their engineering applicability are examined. The findings reveal that the total-order sensitivity indices for electricity price, flue gas inlet flow rate, pipeline diameter, pipeline material price, pipeline inlet pressure, and injection pressure are 0.6578, 0.3857, 0.5585, 0.3823, 0.2205, and 0.1949, respectively, which are significantly higher than those of the other parameters. This indicates that these parameters have a dominant impact on energy consumption costs through the processes of capture and compression, pipeline transportation, and storage injection. These results provide a basis for selecting decision variables when optimizing the entire CCUS process. Monte Carlo method ; Sensitivity (control systems) ; Process (computing) ; Computer science ; Carbon fibers ; Process engineering ; Environmental science ; Statistics ; Engineering ; Mathematics ; Algorithm Graphite, nuclear technology, radiation studies ; Probabilistic and Robust Engineering Design ; Nuclear reactor physics and engineering
Authors: Tolcha Techane Alemu | Pilairuk Intipunya | Bogale Abebe Gebeyo
Source: Discover Agriculture (review, 2025-10-01)
Field: Materials Science — Biomaterials

A comprehensive review of edible coatings for postharvest management of fruits and vegetables: enhancing food and nutrition security Fruits and vegetables are perishable crops that need particular handling methods to preserve their quality and prolong shelf life. Among these methods, the utilization of edible coatings has emerged as a promising solution to reduce postharvest losses of fruits and vegetables, thereby improving food and nutrition security. However, the significant contribution of edible coatings in preserving fresh produce, reducing postharvest loss, and enhancing the food and nutritional value of fruits and vegetables is not well documented. Therefore, this review assesses the contribution of edible coatings to enhancing food and nutrition security by managing the postharvest loss of fruits and vegetables. Biodegradable, non-toxic, and ecologically friendly innovative technology, edible coatings increase the shelf life of fruits and vegetables by limiting oxidation, retarding biological and physiological processes, and preventing moisture loss. During the selection of an edible coating for fruits and vegetables, considering the biodegradability, protection, and barrier properties (moisture and oxygen), sustainability, cost, moderate gas permeability, and non-toxicity is very paramount. Edible coatings, with their properties of antioxidants and antimicrobials, preserve the nutritional values, quality, and shelf life of fruits and vegetables. The commonly used edible coatings are polysaccharide-based edible coatings (starch and its derivative, cellulose and its derivative, chitosan, aloe vera, mucilage of cactus trees, seaweed extracts), protein-based edible coatings, lipid-based edible coatings, and beeswax edible coatings. The review concluded that these edible coatings, when used at appropriate concentrations, lowered postharvest losses of fruit and vegetables during storage, thereby resulting in improved food and nutrition security. The concepts of comprehensive studies required for developing plant’s an essential oil edible coatings and films for food packaging applications, as well as overcoming challenges like consumer acceptance, regulatory requirements, and non-toxic scaling up to the commercial applications, have also been discussed. Postharvest ; Shelf life ; Food preservation ; Food quality ; Mucilage ; Food security ; Food spoilage ; Food packaging Nanocomposite Films for Food Packaging ; Postharvest Quality and Shelf Life Management
Authors: Xuanyi Li | Zhiwen Wang | Zefeng Chen | Zhichao Yu | Changsong Xu
Source: npj Computational Materials (article, 2025-05-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Dzyaloshinskii–Moriya interaction manipulation in multiferroic Janus monolayers The discovery of two-dimensional (2D) multiferroic materials leads to significant breakthroughs in condensed matter physics. However, the role of Dzyaloshinskii–Moriya interaction (DMI) in determining the magnetic and ferroelectric orderings of 2D multiferroics remains underexplored. In this study, we employ first-principles calculation methods to reveal the multiferroic nature of Janus NiXY monolayers (X, Y = I, Br, Cl). Our analyses demonstrate that, (i) Janus NiXY magnets exhibit a cycloid helical spin ground state propagating along the 〈110〉 direction, dominantly driven by intrinsic DMI; and (ii) macroscopic ferroelectric polarizations are intertwined with spin spiral orders, indicating their type-II multiferroicity. Notably, the inclination of the spin rotation plane directly correlates with the DMI strength, which suggests adjustable electric polarization when spin-spin interactions are modulated by external electrostatic fields. Therefore, our work not only indicates the DMI manipulation for tailoring magnetic and ferroelectric ground states but also highlights the intrinsic strong magnetoelectric coupling effects in multiferroic Janus materials. Janus ; Multiferroics ; Monolayer ; Materials science ; Condensed matter physics ; Nanotechnology ; Physics ; Ferroelectricity ; Optoelectronics Multiferroics and related materials ; 2D Materials and Applications ; Underwater Acoustics Research