jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Alessandro Genoni | Maurizio Sironi
Source: Journal of Applied Crystallography (article, 2025-07-01)
Field: Chemistry — Physical and Theoretical Chemistry

Inversion of the X-ray restrained wavefunction equations: a first step towards the development of exchange–correlation functionals based on X-ray data The X-ray restrained wavefunction (XRW) method is a quantum crystallographic technique that enables the determination of wavefunctions compatible with experimental X-ray diffraction data. Extensive research has demonstrated that this strategy inherently captures electron correlation and polarization effects on the electron density, while also providing consistent electron distributions. These findings suggest that the approach could be valuable in the development of new exchange-correlation (xc) functionals for density functional theory (DFT) calculations. This is particularly relevant in light of recent observations and recommendations by Medvedev <i>et al</i>. [<i>Science</i> (2017), <b>355</b>, 49-52], who stressed the importance that xc functionals give both accurate energy values and exact electron densities, in line with the original spirit of DFT. Motivated by this perspective, this paper presents a preliminary investigation that aims at extracting and visualizing for the first time the perturbation potentials arising from the use of X-ray diffraction data as restraints in XRW calculations. In the present work, these potentials are simply obtained as orbital-averaged potentials through straightforward inversions of the XRW equations, where theoretical or high-quality experimental X-ray structure factors are employed in XRW computations at the restricted Hartree-Fock level for atoms (neon, argon and krypton) and simple molecules (dilithium and urea). Features and limitations of the resulting preliminary potentials are illustrated, while future perspectives on the use of the XRW method for the development of xc functionals are also discussed. Wave function ; Inversion (geology) ; X-ray ; Correlation ; Physics ; Computational chemistry ; Mathematical physics ; Quantum mechanics ; Mathematics ; Chemistry ; Geometry ; Geology Crystallography and molecular interactions ; Advanced NMR Techniques and Applications ; Advanced Chemical Physics Studies
Authors: Ubaid Khan | Manas Sheth
Source: The Bombay Technologist (article, 2025-04-11)
Field: Materials Science — Biomaterials

Recent Advances in Biopolymers for Biomedical and Packaging Applications Biopolymers have gained significant attention as sustainable, biodegradable, and biocompatible materials for wide-ranging applications ranging from biomedical, construction, packaging, adhesives, and many more. This review highlights various biopolymers such as Polysaccharides, Proteins, and Synthetic biopolymers and their applications in the fields of Biomedical and Packaging applications. Nanotechnology ; Computer science ; Biochemical engineering ; Engineering ; Materials science biodegradable polymer synthesis and properties
Authors: Chengli Song | Wei Li | Chunhui Li | Lifeng Li | Jinheng Luo | Lixia Zhu
Source: Processes (article, 2025-05-08)
Field: Materials Science — Materials Chemistry

Model for Predicting Corrosion in Steel Pipelines for Underground Gas Storage The response surface methodology (RSM) is utilized to construct a corrosion prediction model for steel pipelines for underground gas storage (UGS). Four key corrosion-influencing factors—the CO2 partial pressure, Cl− concentration, temperature, and flow rate—are identified by investigating the operating parameters of 14 UGS extracted pipelines (Nos. S1–S14) in China. Based on the operating parameters, 29 sets of high-temperature and high-pressure autoclave corrosion tests are designed and carried out. A quadratic regression equation model for corrosion rate prediction is fitted using the data from the corrosion test results. The p-values of the model’s four influencing factors are &lt;0.01, indicating that the influencing factors are significant and reasonable. The F-value of the model is greater than the critical value, and the noise probability p-value is &lt;0.01, indicating that the model has good fitness. The determination coefficient R2 of the model is 0.9753, which is close to 1. Therefore, the observed value and the response value of the model are obviously correlated: i.e., the model has a high degree of truth. The model is used to predict the corrosion rate of 14 UGS pipelines: S3 and S14 are severely corroded, while the others are moderately corroded. Corrosion ; Pipeline transport ; Petroleum engineering ; Environmental science ; Underground storage tank ; Storage tank ; Metallurgy ; Forensic engineering ; Materials science ; Engineering ; Waste management ; Environmental engineering Material Properties and Failure Mechanisms ; Corrosion Behavior and Inhibition ; Structural Integrity and Reliability Analysis
Authors: Yuchen Li | Derya Baran | Dachang Du | Dongchu Wei | Xiaojing Lü | Hanying Li | Wee‐Liat Ong
Source: Advanced Sustainable Systems (article, 2025-04-18)
Field: Materials Science — Materials Chemistry

Thermal Management Performances of Low Environmental‐Impact Metallocene‐Catalyzed Poly‐α‐Olefin (mPAO) Liquid for High‐Power‐Density Applications Abstract Direct‐contact liquid cooling has emerged as one of the most effective thermal management techniques for high‐power‐density applications. In this study, key physical properties, including density, viscosity, heat capacity, and thermal conductivity are experimentally measured and simulated for three different metallocene‐catalyzed poly‐α‐olefin (mPAO) with different branch lengths and numbers. The results indicate minimal differences in density, heat capacity, and thermal conductivity, but a significant change in the viscosity, with longer and more branched molecules exhibiting higher viscosity. A comparative analysis with common coolants highlights mPAO's superior heat transfer and environmental attributes, positioning it as a competitive environmentally friendly coolant. Using molecular dynamics simulations, mPAO's convective heat transfer behavior of mPAOs in nanochannels is examined to discover enhanced convective heat transfer with increased wall‐liquid atomic interactions and reduced liquid inter‐molecular interactions. These enhancements arise from the denser atomic arrangement in the liquid and closer proximity to the wall. The results indicate that for forced convection under laminar flow in smooth‐walled nanochannels, the Nusselt number depends only on the normalized Kapitza length. It is independent of wall and liquid materials. Olefin fiber ; Metallocene ; Catalysis ; Materials science ; Thermal ; Post-metallocene catalyst ; Chemical engineering ; Organic chemistry ; Composite material ; Chemistry ; Polymerization ; Thermodynamics ; Polymer ; Engineering ; Physics Thermal properties of materials ; Tribology and Wear Analysis ; Semiconductor materials and devices
Authors: Reza Abazari | K. Yildirim
Source: Učenye zapiski Kazanskogo gosudarstvennogo universiteta. Seriâ Fiziko-matematičeskie nauki/Učënye zapiski Kazanskogo universiteta. Seriâ Fiziko-matematičeskie nauki (article, 2025-12-21)
Field: Materials Science — Materials Chemistry

Numerical study of the 2D Sivashinsky equation for binary alloy solidification problems Binary alloy solidification involves the transition of a liquid mixture of two metals into a solid phase and presents several complex challenges that researchers aim to address. These problems can be categorized into issues related to thermodynamics, diffusion, and macro- and microstructural evolution during the cooling process. The Sivashinsky equation is a fourth-order nonlinear partial differential equation that arises in the mathematical modeling of binary alloy solidification problems. In this article, we apply the Fourier spectral method combined with the Euler method to numerically solve the 2D Sivashinsky equation with periodic boundary conditions. A numerical study of the Sivashinsky equation is important because its analytical solution does not exist, except for trivial solutions. The error estimation of the approximate solution is provided. Furthermore, we show, both theoretically and numerically, that the proposed method preserves the decreasing mass condition of the obtained numerical solutions. Finally, to validate the theoretical results, three examples with different initial conditions are investigated. Partial differential equation ; Boundary value problem ; Binary number ; Binary alloy ; Spectral method ; Numerical analysis ; Fourier transform ; Phase (matter) ; Differential equation Solidification and crystal growth phenomena ; Thermoelastic and Magnetoelastic Phenomena ; Phase Change Materials Research
Authors: <p>Liye Xie<sup>1</sup>, Xueyan Jiao<sup>1,2</sup></p>
Source: Academic Journal of Materials & Chemistry (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Study on Arrhenius Constitutive Model of Ti-22Al-24Nb-0.5Mo Alloy Based on Strain Compensation Tensile tests at elevated temperatures were performed on the Ti-22Al-24Nb-0.5Mo alloy using an MTS universal testing machine to investigate its hot deformation behavior. The experiments were conducted at temperatures ranging from 875 to 950 ℃ and strain rates of 0.001 to 0.55 s⁻¹. Flow stress-strain curves of the alloy were obtained under these conditions, and the effects of deformation temperature and strain rate on flow stress were analyzed. A fifth-order polynomial was used to model the relationship between strain and various material parameters, achieving a correlation coefficient (R) greater than 0.99. An Arrhenius type constitutive model with strain compensation was established. The calculated results demonstrated a correlation coefficient of 0.994 between the predicted and experimental flow stress values, with an average relative error of 4.24%. This confirms the model’s high accuracy, making it suitable for numerical simulations. Flow stress ; Constitutive equation ; Deformation (meteorology) ; Strain rate ; Arrhenius equation ; Strain (injury) ; Alloy High-Velocity Impact and Material Behavior ; Metallurgy and Material Forming ; Intermetallics and Advanced Alloy Properties
Authors: Sushil Pandit Dalvi | Vaibhav A. Dixit | Shruti Singh | Bhavika Lalwani | Prerna Patil
Source: Journal of Scientific Advances (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Image Processing for Quality Analysis of Fruits Guaranteeing high quality standards in agricultural produce remains vital because it enables satisfied customers and ensures safe food consumption. The current practice of visual inspection produces results that are both opinion-based and unproductive. The research develops a new method which employs image analysis to achieve automatic produce quality assessment. Professional algorithms analyze high-resolution images to check for imperfections while simultaneously evaluating ripeness and deciding the general state of quality. The Convolutional Neural Network (CNN) analyzes important product attributes such as color, texture and shape after being trained on a vast collection of labelled images. The detection solution provides better precision together with standardized results than existing manual approaches. The system achieves accurate classification and evaluation of produce through experimental evaluation data. The technology aims to restructure agricultural quality surveillance which minimizes product loss while strengthening product attractiveness. Such an evaluation method creates advantages for all stages of the food supply chain by improving both efficiency and sustainability. Quality (philosophy) ; Image processing ; Computer science ; Artificial intelligence ; Image (mathematics) ; Philosophy Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Chemical Sensor Technologies
Authors: Tanaya S. Bahadure - | Sakshi G. Kancharlawar - | Rohan S. Chavhan - | Ajinkya V. Gulhane - | M. D. Kitukale
Source: International Journal For Multidisciplinary Research (review, 2025-01-21)
Field: Chemistry — Analytical Chemistry

Advancements in Spectrophotometric Techniques for Antipsychotic Drug Determination: A Comprehensive Review of Methodologies and Applications Antipsychotic drugs are crucial for managing mental health disorders, necessitating precise analytical techniques for their determination in clinical and forensic contexts. Spectrophotometry, owing to its simplicity, cost-effectiveness, and adaptability, is widely used for this purpose. This review comprehensively explores the advancements in spectrophotometric techniques for antipsychotic drug determination, covering chemometric applications, novel derivatization methods, and environmentally sustainable practices. Challenges such as limited sensitivity, matrix complexity, and interference are discussed alongside emerging solutions, including AI-driven chemometric models, nano-sensors, and quantum dots. Comparisons with advanced analytical techniques like LC-MS underscore spectrophotometry's relative strengths and limitations. The review also highlights future directions, emphasizing the need for enhanced sensitivity, bioanalytical applications, and high-throughput methodologies. Innovations in instrumentation, such as portable devices and 3D-printed spectrophotometers, offer promising avenues for research and practical implementation. By addressing current challenges and leveraging technological advancements, spectrophotometry can continue to play a vital role in pharmaceutical and forensic sciences. Antipsychotic drug ; Drug ; Antipsychotic ; Medicine ; Computer science ; Pharmacology ; Management science ; Engineering ; Psychiatry ; Schizophrenia (object-oriented programming) Analytical Methods in Pharmaceuticals ; Electrochemical sensors and biosensors ; Electrochemical Analysis and Applications
Authors: Jason Walsh | Levent Görgü | Émilie Cavel | Vincent Poulain | Laurent Gutierrez | Eleni Mangina | Sónia Negrão
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-09-17)
Field: Chemistry — Analytical Chemistry

Botanic Spectrum Analyser: A Deep Learning GUI for Plant Image Segmentation in Hyperspectral and RGB Phenotyping Abstract Plant phenotyping systematically quantifies plant traits such as growth, morphology, physiology, or yield, assessing genetic and environmental influences on plant performance. The integration of advanced phenotyping technologies, including imaging sensors and data analytics, facilitates the non-destructive and longitudinal acquisition of high-throughput data. Nevertheless, the sheer volume of such phenotyping data introduces significant challenges for researchers, particularly related to data processing. To overcome these challenges, researchers are turning to artificial intelligence (AI), a tool that can autonomously process and learn from large amounts of data. Despite this advantage, accurate image segmentation remains a key hurdle due to the complexity of plant morphology and environmental noise. In this study, we present the Botanical Spectrum Analyser (BSA), a user-friendly graphical user interface (GUI) that integrates a modified U-Net deep neural network for plant image segmentation. Designed for accessibility, BSA enables non-technical users to apply advanced AI segmentation to RGB and hyperspectral (VNIR and SWIR) imagery. We evaluated BSA’s performance across three case studies involving wheat, barley, and Arabidopsis, demonstrating its robustness across species and imaging modalities. Our results show that BSA achieves an average accuracy of 99.7%, with F1-scores consistently exceeding 98% and strong Jaccard and recall performance across datasets. For challenging root segmentation tasks, BSA outperformed commercial algorithms, achieving a 76% F1-score compared to 24%, representing a 50% improvement. These results highlight the adaptability of the BSA framework for diverse phenotyping scenarios, bridging the gap between advanced deep learning methods and accessible plant science applications. Hyperspectral imaging ; RGB color model ; Deep learning ; Segmentation ; Robustness (evolution) ; Jaccard index ; Pattern recognition (psychology) ; Artificial neural network Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture
Authors: Neeru Jain | Anuj Kumar Sharma | Arinjay Jain | Arindam Chatterjee | Sumit Durgapal | Krishana Kumar Sharma* | Vaibhav Rathore
Source: Journal of Carcinogenesis (article, 2025-01-01)
Field: Materials Science — Biomaterials

Nanotechnology-Based Smart Drug Delivery Systems for Targeted Cancer Therapy Nanotechnology has revolutionized biomedical sciences by introducing advanced strategies for drug delivery, particularly in the field of oncology. Conventional cancer therapies such as chemotherapy and radiation therapy are limited by systemic toxicity, drug resistance, and poor tumor selectivity. Smart drug delivery systems (SDDS) based on nanotechnology are emerging as effective platforms to overcome these barriers by utilizing biosensors, pH-responsive polymers, and magnetic guidance to deliver drugs directly to tumor sites. These nanoparticle-mediated delivery platforms exploit tumor microenvironmental characteristics, such as acidic pH and abnormal vasculature, to achieve targeted drug release while minimizing damage to healthy cells. Biosensor-integrated nanoparticles allow real-time monitoring of tumor progression and therapeutic outcomes. Similarly, pH-sensitive polymers ensure drug release specifically in the acidic tumor microenvironment, enhancing efficacy and reducing systemic side effects. Magnetic nanoparticles further enable remote guidance and controlled drug release under external magnetic fields, ensuring spatial precision. This paper systematically reviews recent developments in these nanotechnology-based systems, focusing on optimization strategies, efficacy, safety, and clinical translation. A comparative analysis highlights the unique advantages and limitations of each delivery platform. The study emphasizes the need for multidisciplinary approaches combining materials science, biotechnology, and clinical oncology to accelerate clinical applications. Ultimately, nanotechnology-based SDDS hold the potential to transform cancer therapy by enhancing treatment precision, minimizing toxicity, and improving patient survival outcomes. Drug delivery ; Drug ; Cancer therapy ; Targeted drug delivery ; Radiation therapy ; Cancer treatment ; Cancer ; Multidisciplinary approach ; Targeted therapy ; Systemic therapy Nanoparticle-Based Drug Delivery ; Graphene and Nanomaterials Applications ; Advanced biosensing and bioanalysis techniques
Authors: Tushliha Ayyuni Fariha | Sari Delviana Marbun | Sudarmanto | Narto | Adik Bahanawan | Prabu Satria Sejati | Teguh Darmawan | Dimas Triwibowo | Danang Sudarwoko Adi | Yusup Amin | Sarah Augustina | Wahyu Dwianto | Rita Kartika Sari | Irsan Alipraja | Imam Wahyudi | EM. Latif R Kusuma
Source: Indonesian Journal of Forestry Research (article, 2025-04-17)
Field: Chemistry — Organic Chemistry

THE SURFACE CHARACTERISTICS AND PHYSICAL PROPERTIES OF SENGON WOOD AT HIGH-TEMPERATURE HEATING TREATMENTS Sengon (Falcataria moluccana Miq.) is a fast-growing timber species widely distributed in Indonesia. However, its dimensional instability and low surface quality have limited its widespread use. Wood modification is essential for enhancing these properties, and one effective approach is heat treatment. This study investigated the effects of different heat treatment methods and durations on color change, surface roughness, weight loss (WL), decreased density, and dimensional stability of sengon wood. The heat modification process was conducted using two methods: oven-heating and hot press-heating, with temperatures set at 200°C for durations ranging from 1 to 5 hours. The results indicated that oven-heated samples exhibited higher surface roughness, weight loss, density reduction, and dimensional stability while showing less color change than hot press-heated samples. Additionally, the hot press-heated samples displayed more significant color changes (darkening) and smoother surface roughness. WL and decreased density were also more pronounced with longer heating durations, except for the 4- and 5-hour hot press-heating treatments. Notably, oven-heated samples demonstrated higher dimensional stability than hot press-heated samples as the duration of heating increased. Based on the results, the optimal treatment varies depending on the desired product characteristics. For improved surface qualities with consideration of WL, the optimum treatment is a 2-hour hot press-heating treatment. Higher dimensional stability can be achieved through a 3-hour oven-heating treatment. Materials science ; Environmental science ; Composite material Wood and Agarwood Research ; Lignin and Wood Chemistry ; Wood Treatment and Properties
Authors: M. I. Hurei
Source:  (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

AI-driven decarbonization: a pathway to carbon neutrality Nature Conservation as an Essential Prerequisite for Preserving Humanity (March 19–20, 2025. Riga, the Republic of Latvia) : International scientific conference. Riga, Latvia : Baltija Publishing, 2025. 92 pages. Carbon fibers ; Neutrality ; Materials science ; Carbon neutrality ; Engineering physics ; Electrical engineering ; Physics ; Engineering ; Political science ; Composite material ; Renewable energy Machine Learning in Materials Science
Authors: Alexios Vardakas | Maria – Argyro Kapranou | Panagiotis Stathopoulos | Maria Xenaki | Charalampos Proestos | Aris E. Giannakas
Source: Preprints.org (preprint, 2025-07-30)
Field: Chemistry — Organic Chemistry

Evaluation of Olive Leaves of Different Varieties and Climatic Cultivation Conditions as a Source of Bioactive Substances Using Sustainable Extraction Methods This study investigates the impact of olive tree variety and environmental stress on the production of bioactive compounds in olive leaves, focusing on phenolic content and antioxidant capacity. Three olive varieties—Kalamata, Koroneiki, and wild olive (Olea europaea var. Silvestris)—from two climatically distinct regions in Greece (Stamna and mountainous Paravola) were analyzed. Using three sustainable extraction methods, Ultrasound-Assisted Extraction (UAE), Enzyme-Assisted Extraction (EAE), and Microwave-Assisted Extraction (MAE), the total polyphenolic content (TPC) and antioxidant activity (AA%) of the olive leaf extracts were quantified. Results revealed significant variation in polyphenolic yield depending on both extraction technique and environmental conditions. EAE consistently outperformed other methods in polyphenol recovery, especially from wild olive leaves in the higher-stress environment of Paravola, which showed the highest TPC. This work underscores the synergistic role of varietal and climatic factors in secondary metabolite accumulation and highlights the potential of green extraction technologies in valorizing olive by-products. Extraction (chemistry) ; Horticulture ; Chemistry ; Environmental science ; Botany ; Biotechnology ; Food science ; Biology ; Chromatography Edible Oils Quality and Analysis ; Spectroscopy and Chemometric Analyses ; Essential Oils and Antimicrobial Activity