jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hiroyuki Koizumi | T. Kitagawa | Kai Okubo | Takuma Kozono | Ryota Kose
Source: Cellulose (article, 2025-11-20)
Field: Materials Science — Biomaterials

Large-area TEM imaging for characterization of fibrillated cellulose across microscale to nanoscale Abstract Fibrillated cellulose, such as micro- and nanofibrillated cellulose (MNFC) and fines, plays a significant role in papermaking. However, characterizing its complex morphology across multiple scales remains challenging due to the limitations of conventional microscopy. Optical microscopy lacks the resolution to detect nanoscale fibrils, and electron and atomic force microscopy are limited by the trade-off between resolution and field of view. To overcome these limitations, we employed large-area transmission electron microscopic (TEM) imaging. The automatic stitching of thousands of images generated a single image as large as 523 × 886 µm 2 , enabling the visualization of both fibril length and width across multiple scales, from micrometers to nanometers. Moreover, large-area TEM imaging revealed distinct morphological differences between two samples that have previously been considered comparable by a standard optical method: one, slender and fibrillar, prepared by the aqueous counter collision method; the other, sheet-like with a broader size distribution, prepared by a grinder. Microscale chemistry ; Nanoscopic scale ; Characterization (materials science) ; Transmission electron microscopy ; Cellulose ; Image stitching ; Resolution (logic) ; Microscopy ; Atomic force microscopy ; Electron microscope Advanced Cellulose Research Studies ; Material Properties and Processing ; Electron and X-Ray Spectroscopy Techniques
Authors: William E. Acree | Costas Panayiotou
Source: Preprints.org (preprint, 2025-08-18)
Field: Chemistry — Physical and Theoretical Chemistry

Dispersion, Polar, and Hydrogen-Bonding Contributions to Solvation Free Energies A new method is presented for the estimation of contributions to solvation free energy from dispersion, polar and hydrogen-bonding (HB) intermolecular interactions. COSMO-type quantum chemical solvation calculations are used for the development of four new molecular descriptors of solutes for their electrostatic interactions. The new model needs one to three solvent –specific parameters for the prediction of solvation free energies. The widely used Abraham’s LSER model is used for providing the reference solvation free energy data for the determination of the solvent-specific parameters. Extensive calculations in 80 solvent systems have verified the good performance of the model. The very same molecular descriptors are used for the calculation of solvation enthalpies. The advantages of the present model over Abraham’s LSER model is discussed along with the complementary character of the two models. Enthalpy and free-energy solvation information for pure solvents is translated into partial solvation parameters (PSP) analogous to the widely used Hansen solubility parameters and enlarge significantly their range of applications. The potential and the perspectives of the new approach for further molecular thermodynamic developments is discussed. Solvation ; Polar ; Hydrogen bond ; Dispersion (optics) ; London dispersion force ; Hydrogen ; Chemical physics ; Materials science ; Chemistry ; Computational chemistry ; Atomic physics ; Physics ; Organic chemistry ; Quantum mechanics ; Molecule Various Chemistry Research Topics
Authors: Jiancheng Zhong | Yicheng Luo | Chen Yang | Maoqi Yuan | Shaokai Wang
Source: Interdisciplinary Sciences Computational Life Sciences (article, 2025-05-17)
Field: Chemistry — Spectroscopy

ResNeXt-Based Rescoring Model for Proteoform Characterization in Top-Down Mass Spectra In top-down proteomics, the accurate identification and characterization of proteoform through mass spectrometry represents a critical objective. As a result, achieving accuracy in identification results is essential. Multiple primary structure alterations in proteins generate a diverse range of proteoforms, resulting in an exponential increase in potential proteoform. Moreover, the absence of a definitive reference set complicates the standardization of results. Therefore, enhancing the accuracy of proteoform characterization continues to be a significant challenge. We introduced a ResNeXt-based deep learning model, PrSMBooster, for rescoring proteoform spectrum matches (PrSM) during proteoform characterization. As an ensemble method, PrSMBooster integrates four machine learning models, logistic regression, XGBoost, decision tree, and support vector machine, as weak learners to obtain PrSM features. The basic and latent features of PrSM are subsequently input into the ResNeXt model for final rescoring. To verify the effect and accuracy of the PrSMBooster model in rescoring proteoform characterization, it was compared with the characterization algorithm TopPIC across 47 independent mass spectrometry datasets from various species. The experimental results indicate that in most mass spectrometry datasets, the number of PrSMs obtained after rescoring with PrSMBooster increases at a false discovery rate (FDR) of 1%. Further analysis of the experimental results confirmed that PrSMBooster improves the accuracy of PrSM scoring, generates more mass spectrometry characterization results, and demonstrates strong generalization ability. Computer science ; Support vector machine ; Identification (biology) ; Generalization ; Artificial intelligence ; Characterization (materials science) ; Machine learning ; Set (abstract data type) ; Biology ; Mathematics Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Stefano Cantella | Silvia Badini | Carlotta Bollati | Mushtaq Alam Madar Saheb | Roberto Vigano | Carmen Lammi | Raffaele Pugliese | Serena Graziosi
Source: Scientific Reports (article, 2025-12-24)
Field: Materials Science — Biomaterials

Development of a 3D-printable bioactive polycaprolactone–collagen peptides filament for biomedical applications This study presents a scalable, nontoxic method for fabricating a 3D printable polycaprolactone (PCL)-collagen peptides composite filament via solvent-assisted blending and a customized desktop filament extrusion system. Virgin and recycled PCL feedstocks were used to study the matrix characteristics. Scanning electron microscopy, X-ray diffraction, differential scanning calorimetry, thermogravimetric analysis, and intrinsic fluorescence spectroscopy confirmed that the composite maintains PCL's inherent crystalline and thermal properties. It also exhibits intrinsic bioactive capabilities provided by the collagen peptides. Filaments with diameters suitable for the Fused Filament Fabrication 3D printing were obtained. Tests demonstrated that the recycled matrix and the lab-scale process reduce the tensile modulus of the material. At the same time, collagen peptides enhanced tensile stiffness by creating intermolecular hydrogen bonding with the PCL. The biocompatibility of the composite has also been confirmed, while degradability studies have shown the tunability of the PCL degradation rate. Additionally, examples of 3D-printed scaffolds based on Triply Periodic Minimal Surfaces have been successfully fabricated using the PCL-collagen peptide filament. Therefore, this study demonstrates that integrating collagen peptides into a PCL matrix represents a viable, nontoxic, affordable, and promising approach for developing customized and bioactive implants, scaffolds, and other regenerative medicine applications. Protein filament ; Polycaprolactone ; Composite number ; Ultimate tensile strength ; Extrusion ; Thermogravimetric analysis ; Biocompatibility ; Differential scanning calorimetry Collagen: Extraction and Characterization ; Silk-based biomaterials and applications ; Polymer Surface Interaction Studies
Authors: Yang Cheng | Binhua Zhang | Xueyang Li | Hongyu Yu | Changsong Xu | Hongjun Xiang
Source: ArXiv.org (preprint, 2025-05-08)
Field: Materials Science — Materials Chemistry

Efficient construction of effective Hamiltonians with a hybrid machine learning method The effective Hamiltonian method is a powerful tool for simulating large-scale systems across a wide range of temperatures. However, previous methods for constructing effective Hamiltonian models suffer from key limitations: some require to manually predefine interaction terms limited flexibility in capturing complex systems, while others lack efficiency in selecting optimal interactions. In this work, we introduce the Lasso-GA Hybrid Method (LGHM), a novel approach that combines Lasso regression and genetic algorithms to rapidly construct effective Hamiltonian models. Such method is broadly applicable to both magnetic systems (e.g., spin Hamiltonians) and atomic displacement models. To verify the reliability and usefulness of LGHM, we take monolayer CrI_3 and Fe_3 GaTe_2 as examples. In both cases, LGHM not only successfully identifies key interaction terms with high fitting accuracy, but also reproduces experimental magnetic ground states and Curie temperatures with further Monte Carlo simulations. Notable, our analysis of monolayer Fe_3 GaTe_2 reveals that the single-ion anisotropy and Heisenberg interaction lead to an out-of-plane ferromagnetic ground state, while the fourth-order interactions contribute significantly to the high Curie temperature. Our method is general so it can be applied to construct other effective Hamiltonian models. Hamiltonian (control theory) ; Ferromagnetism ; Heisenberg model ; Curie temperature ; Monte Carlo method ; Anisotropy ; Key (lock) Machine Learning in Materials Science ; Iron-based superconductors research ; 2D Materials and Applications
Authors: Mohammad Ali Khan | Christopher Greenfield
Source:  (article, 2025-12-27)
Field: Materials Science — Materials Chemistry

XENON-LITHIUM COMPOSITION: REDEFINING PLASMA CONTAINMENT For decades, fusion energy has been hindered by one major limitation: plasma containment time. Traditional tokamak designs rely on lithium as a plasma-facing material, but lithium suffers from rapid neutron interactions, excessive cooling, and material degradation. It is severely limiting plasma sustainment and energy efficiency. By applying quantum mechanics—specifically the momentum operator, de Broglie wavelength, and Planck’s radiation law—we identified xenon as a superior alternative due to its high atomic mass, low reactivity, and ability to suppress radiative losses. Our solution? A matrix of xenon and lithium that combines the neutron-absorbing benefits of lithium with the stability and wave-diffraction properties of xenon. This approach minimizes plasma losses, extends containment time beyond 30 minutes to over an hour: eliminates lithium’s cooling drawback. By overcoming fusion’s greatest bottleneck, this breakthrough paves the way for commercially viable fusion energy, something the industry has yet to achieve. Tokamak ; Lithium (medication) ; Xenon ; Limiting ; Plasma ; Containment (computer programming) ; Fusion power ; Neutron Fusion materials and technologies ; Fusion and Plasma Physics Studies ; Magnetic confinement fusion research
Authors: Shiva Shankar | Ranjit Kumar Paul | Md Yeasin | S. G. Patil
Source: The Indian Journal of Agricultural Sciences (article, 2025-08-22)
Field: Chemistry — Analytical Chemistry

Comparative study of neural network variants for potato (Solanum tuberosum) price modeling The intricate nature of agricultural price data possesses a formidable challenge in the modeling process, necessitating the careful selection and fine-tuning of methodologies. Deep learning emerges as a potent tool for enhancing the predictive accuracy and understanding the complexities of agricultural prices. The effectiveness of deep learning methodologies in handling the complex patterns of agricultural price datasets was demonstrated using monthly potato (Solanum tuberosum L.) price data collected from the National Horticultural Board across four distinct markets. The study was carried out during 2023 aimed to compare the performance of deep learning models, including Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) with feed forward Artificial Neural Networks (ANN) using the error metrics such as Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE). The GRU model performed best for the Chandigarh (16.26% MAPE) and Delhi (6.09% MAPE) markets where LSTM model showed superior performance in the Dehradun market (17.81% MAPE) and CNN for Shimla market (12.53% MAPE). The error percentage of deep learning models were remarkably low when compared to the machine learning model. Deep learning ; Mean absolute error ; Artificial neural network ; Mean squared error ; Convolutional neural network ; Mean absolute percentage error ; Agriculture ; Mean squared prediction error Spectroscopy and Chemometric Analyses
Source: Nanotechnology and Nanomaterials Research (article, 2025-04-30)
Field: Materials Science — Biomaterials

Nanoparticle Drug Delivery Systems: Revolutionizing Drug Delivery and Unlocking New Frontiers in Cardiovascular, Neurological, and Cancer Treatments Nanoparticle drug delivery systems have emerged as a revolutionary approach in modern medicine, offering unprecedented precision and efficiency in delivering therapeutics to targeted sites within the body. Drug delivery ; Drug ; Medicine ; Cancer ; Cancer drugs ; Targeted drug delivery ; Pharmacology ; Nanotechnology ; Internal medicine ; Materials science Nanoparticle-Based Drug Delivery ; Nanoparticles: synthesis and applications
Authors: Adikanda Parida | Manas Ranjan Senapati | S.R. Das
Source: International Journal of Computer Trends and Technology (article, 2025-11-30)
Field: Chemistry — Analytical Chemistry

Optimized Radial Basis Function Neural Network for Automated Classification of Apple Quality Apples are still very much a global health food, which also plays a large role in agriculture. We must see to it that we have the same high standards in sorting and distribution, which in turn will do much to reduce post-harvest waste. This study reports on a developed “Radial Basis Function Neural Network (RBFNN)” model for the automatic classification of apples into good and defective. We have put together a system that uses a set of numerical features from our data, which includes size, weight, sugar content, crispness, juice content, ripeness, and acid level, which also serves to eliminate the need for extra feature extraction or image processing. We also report that we have improved upon radial center and spread parameters, which in turn have improved the accuracy and learning convergence. The model achieves notable performance on 1200 test samples, recording an accuracy of 89.83%, precision of 94.14%, recall of 88.33%, specificity of 92.06%, and an F1-score of 91.12%. The findings demonstrate that the optimized RBFNN effectively balances computational efficiency with predictive reliability, making it a suitable solution for real-time quality inspection in automated fruit sorting and supply chain systems. Sorting ; Artificial neural network ; Radial basis function ; Precision and recall ; Set (abstract data type) ; Function (biology) ; Pattern recognition (psychology) ; Feature (linguistics) Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Postharvest Quality and Shelf Life Management
Authors: Maximilian Fleck | Marcelle B.M. Spera | Samir Darouich | Timo Klenk | Niels Hansen
Source: Digital Discovery (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Generalized DeepONets for viscosity prediction using learned entropy scaling references Deep Entropy Scaling architecture (DeepESNet) to predict viscosities over large ranges of state points proposing and using generalized DeepONets (GenDeepONet). Scaling ; Entropy (arrow of time) ; Binary entropy function ; Viscosity Machine Learning in Materials Science ; Anomaly Detection Techniques and Applications
Authors: Xiaoxiang Wu | Yanpeng Hao | Haixin Wu | Jikai Bi | Zijian Wu | Lei Huang
Source: Micromachines (article, 2025-10-31)
Field: Materials Science — Materials Chemistry

Study on Multi-Parameter Physical Processes and Flashover Threshold of Silicone Rubber Plate During AC Discharge in Salt Fog External insulation of coastal power grids transmitting offshore wind power faces significant threats from salt fog flashovers. Current arc monitoring and early warning technologies for flashover are severely inadequate. Research on salt fog discharge processes and determining the threshold at the flashover brink for transmission equipment external insulation is crucial for ensuring the safe operation of coastal grids delivering offshore wind power. Fiber Bragg Grating (FBG), with its advantages of compact size, excellent insulation, and fast response, enables effective discharge monitoring and identification of the critical flashover state on external insulation surfaces. In this study, FBGs were embedded at the interfaces of typical external insulation specimens, including silicone rubber plates and epoxy resin plates, to conduct contaminated AC salt fog discharge tests. Synchronized measurements of visible light images, infrared thermal images, and FBG interface temperature were conducted to investigate the discharge physical processes on silicone rubber insulating surfaces and the flashover threshold based on FBG temperature rise rate. The results indicate that discharge process can be divided into three phases: arc initiation, extension, and flashover based on the characteristics of arc visible light images. By comparing arc locations in infrared and visible light images with the corresponding FBG interface temperature rise, the arc phase criterion of FBG interface temperature rise rate and position were proposed. Furthermore, through multiple experiments, it has been found that flashover occurs when both interface temperatures reached above 4.6 × 10<sup>-2</sup> °C/s. This study provides a novel research methodology for physical process of external insulation discharge and flashover warning in coastal salt fog environments. Arc flash ; Silicone rubber ; Electric arc ; Power cable ; Temperature measurement ; Infrared ; Natural rubber ; Power transmission High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Lightning and Electromagnetic Phenomena
Authors: Muhammad Akhlaq | Muhammad Saqlain | Zunaira Wasif | Abdul Hanan | Ayesha Iddrise | Tariq Habib | Nasiba Faiz | Areeba Wasif | Muhammad Khaleeq Alum
Source: Journal of medical & health sciences review. (review, 2025-05-14)
Field: Materials Science — Biomaterials

TARGETED AND CONTROLLED DRUG DELIVERY IN CANCER: A NANOTECHNOLOGICAL APPROACH IN CANCER TREATMENT: A REVIEW OF RECENT ADVANCES Nanotechnology has emerged as a transformative approach in cancer therapy, offering innovative solutions for improving drug delivery, diagnostics, and treatment precision. At the core of this advancement are nanoparticles, engineered to specifically target cancer cells while minimizing damage to healthy tissues. Their unique nanoscale properties allow for enhanced drug stability, selective delivery, and controlled release, overcoming many limitations of conventional therapies such as drug resistance, toxicity, and non-specific targeting. This review explores various types of nanoparticles used in cancer treatment, including metallic nanoparticles (e.g., gold and silver), polymeric nanoparticles (e.g., PLGA), and lipid-based nanoparticles (e.g., liposomes and solid lipid nanoparticles). Metallic nanoparticles provide versatile platforms for drug delivery and photothermal therapy, while polymeric nanoparticles offer sustained drug release and biodegradability. Lipid-based systems, known for their biocompatibility, facilitate targeted drug accumulation in tumors through both passive and active targeting mechanisms. The review also highlights the ability of nanoparticles to respond to stimuli such as pH and temperature changes, enabling site-specific drug release within the tumor microenvironment. Additionally, it discusses challenges related to the safety, scalability, and regulatory approval of nanoparticle-based therapies, which must be addressed for clinical translation. Overall, the integration of nanotechnology in oncology represents a promising frontier for developing next-generation cancer therapies. By enhancing treatment specificity and reducing adverse effects, nanoparticles hold the potential to revolutionize cancer care and significantly improve patient outcomes. Cancer ; Drug delivery ; Cancer drugs ; Targeted drug delivery ; Medicine ; Drug ; Nanotechnology ; Pharmacology ; Materials science ; Internal medicine Nanoparticle-Based Drug Delivery ; Graphene and Nanomaterials Applications ; Cancer Research and Treatment
Authors: Prithvi Singh | Nidhi Shekhawat
Source: Indian Journal of Biochemistry and Biophysics (article, 2025-01-01)
Field: Chemistry — Organic Chemistry

Aryloxyacetic acid derivatives as inhibitors of the HPPD enzyme: A quantitative structure-activity relationship analysis The present work involved a quantitative analysis of the inhibitory effects of aryloxyacetic acid derivatives against Arabidopsis thaliana 4-hydroxyphenylpyruvate dioxygenase (AtHPPD). The study is an important in silico approach to design potential inhibitors of AtHPPD on rational basis. Also, it is helpful to optimize the existing leads to improve their biological activities and to predict the biological activities of untested and sometimes yet unavailable compounds. Moreover, the study could assist to decipher the possible mechanism of action on to receptor site(s) at the molecular level. The rational approach minimizes the time and cost needed in the drug designing process in comparison to traditional drug discovery methods. A total of 51 compounds were examined for the inhibition actions in relation to their molecular structures. The investigation revealed 15 prominent regression models, sharing 20 chemometric descriptors. Each of these models was validated for its internal and external robustness, while the randomization test was performed to avoid any coincidental correlation. The importance of these descriptors was further elaborated through the partial least squares analysis. The highest significant model was employed to calculate/predict the inhibition activities of all analogs of the series. The applicability domain analysis suggested that the model, under consideration, has satisfactory predictability. The guidelines given in the discussion may be helpful in exploring new potential analogs of the series. Enzyme ; Chemistry ; Enzyme assay ; Biochemistry ; Stereochemistry Synthesis and biological activity ; Structural and Chemical Analysis of Organic and Inorganic Compounds ; Synthesis and Reactivity of Sulfur-Containing Compounds