jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Suneel Prajapati | Megha Sapkota | Aditi Mishra | Shilpi Singh | Tanvi Sharma | Lakshmi Panicker | S. Meghna Lakshmi
Source: Discover Plants. (review, 2025-01-14)
Field: Chemistry — Organic Chemistry

An overview of forensic analysis methods of Pterocarpus Santalinus L.f. and their suitability to combat biodiversity crime, adulteration and environmental exploitation: a review Pterocarpus santalinus L.f., commonly referred to as red sandalwood, carries substantial ecological, economic, and cultural importance. It is a forensically significant species as it is frequently involved in illegal logging, trafficking, and counterfeiting. This paper provides a comprehensive investigation of the approaches used in the accurate identification of P. santalinus within the realm of forensic studies. It also delves into the distinct traits and practical uses of P. santalinus, highlighting its traditional medicinal roles, contributions to dye production, and significance in the woodworking sector. This study reviews the available morphoanatomical features, biochemical DNA barcode and machine learning methods to accurately identify species, addressing the critical issue of trafficking, which leads to overexploitation. This review also highlights the significance of forensic sciences in wood identification. Integration of morphoanatomical features, biochemical procedures DNA barcodes and machine learning techniques with forensic wood identification SOPs, could prove to be an effective weapon in combating biodiversity crime, adulteration, and environmental exploitations. Forensic science ; Biodiversity ; Geography ; Environmental science ; Biology ; Ecology ; Archaeology Wood and Agarwood Research ; Ethnobotanical and Medicinal Plants Studies ; Biological Stains and Phytochemicals
Authors: F. Calzavara | Niccolò Di Eugenio | Federico Ledda | Daniele Torsello | Antonio Trotta | Erik Gallo | F. Laviano
Source: Applied Sciences (article, 2025-10-29)
Field: Materials Science — Materials Chemistry

Systematic Monte Carlo Analysis of Binary Compounds for Neutron Shielding in a Compact Nuclear Fusion Reactor Compact fusion reactors are receiving increasing interest as a promising route for accelerating the path toward commercial fusion, thanks to their reduced size and cost. However, this compactness introduces new technological challenges, including higher radiation loads on critical functional components, such as the magnet system. Neutron shielding is therefore of utmost importance to guarantee the expected lifetime of the device, and its selection must account for the harsh environment imposed by the high radiation flux. Shielding materials should be structurally stable, not melt within the operational temperature windows, and be relatively low-cost. For nuclear reactor applications, binary compounds are typically the preferred choice as they often meet these requirements, particularly in terms of availability and cost. In this work, we present a systematic Monte Carlo analysis of more than 700 binary compounds, exposed to the neutron spectrum at the most loaded position of the vacuum vessel in a simplified model of a compact fusion reactor. Shielding performances were evaluated in a toroidal geometry in terms of neutron attenuation, power deposition, and activation, leading to the identification of several promising compositions for effective neutron shielding in future fusion applications. Electromagnetic shielding ; Neutron ; Monte Carlo method ; Fusion power ; Neutron radiation ; Neutron transport ; Fusion ; Neutron temperature ; Binary number Radiation Shielding Materials Analysis ; Nuclear Physics and Applications ; Nuclear reactor physics and engineering
Authors: S. Aditya Kudva | Gajanan Anne | Prakash Kumar | Ashwini Prabhu | S. Ramesh
Source: Scientific Reports (article, 2025-11-24)
Field: Materials Science — Biomaterials

Development of biodegradable Mg-Zn-Zr-based temporary implant materials through multidirectional forging and laser shock peening Biodegradable magnesium alloys have been a fascinating but challenging research arena, as development of such alloys for clinical applications can be a game changer in orthopedics, while controlling the degradation rate of magnesium alloys in physiological conditions is an area of concern. In quest of solution to the existing issue, this study involves development of biodegradable Mg-4Zn-0.6Zr alloy subjected to multidirectional forging (MDF) for bulk property improvement and laser shock peening for surface property improvement. The MDF process was carried out for 5 passes and resulted in significant grain refinement of the alloy from 180 ± 12 μm to 16 ± 5 μm after 5 MDF passes as observed through Scanning Electron Microscope (SEM), Laser Shock Peening (LSP) induced further grain refinement in nano scale as evident through Transmission Electron Microscope (TEM). After 5 MDF passes, the average microhardness of the alloy increased to 101.8 HV due to grain refinement and strain, further rising to 112.6 HV post-LSP from additional grain refinement and dislocations, while tensile tests showed a 141% improvement in Ultimate Tensile Strength (UTS) after 3 MDF passes compared to the homogenized state. The results of the electrochemical corrosion tests indicated that both MDF and LSP resulted in the least corrosion rate, about a factor of one order of magnitude reduction in comparison to the homogenized sample. The key results including mechanical properties, corrosion behaviour in Hanks Balanced Salt Solution and cytotoxicity evaluation employing MG-63 osteoblast-like cells suggest that the considered Mg-4Zn-0.6Zr alloy could be a worthy candidate to be further explored as a bioimplant material. Ultimate tensile strength ; Forging ; Magnesium alloy ; Peening ; Indentation hardness ; Laser peening ; Shock (circulatory) ; Corrosion ; Alloy Magnesium Alloys: Properties and Applications ; Surface Treatment and Residual Stress ; Titanium Alloys Microstructure and Properties
Authors: Peng Zhang | Xingwang Huang | Jingang Su | Zhen Liu | Xianhai Pang | Zihao Wang | Yidong Chen
Source: Polymers (article, 2025-11-13)
Field: Materials Science — Polymers and Plastics

Research on Diagnostic Methods for Gas Generation Due to Degradation of Cable PVC Materials Under Electrical and Thermal Stress Polyvinyl chloride (PVC), owing to its excellent electrical properties and low cost, is widely applied in the inner insulation and outer sheath of cables. To achieve early fault warning based on characteristic gases, this study integrates experimental testing with molecular simulations to systematically reveal the decomposition and gas generation characteristics of different PVC layers under electrical and thermal stresses. The results indicate that inner-layer PVC under electrical stress predominantly generates small-molecule olefins and halogenated hydrocarbons, while outer-layer PVC during thermal decomposition mainly produces hydrogen chloride, alkanes, and fragments of plasticizers. The surrounding atmosphere significantly regulates the gas generation pathways: air promotes the formation of CO<sub>2</sub> and H<sub>2</sub>O, whereas electrical discharges accelerate the release of unsaturated hydrocarbons such as acetylene. By employing TG-FTIR, ReaxFF molecular dynamics, and DFT spectral calculations, a normalized infrared spectral library covering typical products was established and combined with the non-negative least squares method to realize quantitative deconvolution of mixed gases. Ultimately, a diagnostic system was constructed based on the concentration ratios of characteristic gases, which can effectively distinguish the failure modes of inner and outer PVC layers as well as different stress types. This provides a feasible approach for early detection of cable faults and supports intelligent maintenance strategies. Polyvinyl chloride ; Degradation (telecommunications) ; Deconvolution ; Thermal ; Stress (linguistics) ; Thermal decomposition ; Decomposition ; Derating ; Hydrogen Polymer Science and PVC ; High voltage insulation and dielectric phenomena ; Polymer crystallization and properties
Authors: Laura Andria | Giancarlo Capitani | Barbara La Ferla | Heiko Lange | Melissa Saibene | Luca Zoia | Barbara Vercelli
Source: Nanomaterials (article, 2025-09-22)
Field: Materials Science — Materials Chemistry

Carbon Quantum Dots Interactions with Pyrogallol, Benzoic Acid, and Gallic Acid: A Study on Their Non-Covalent Nature Understanding the interactions between carbon quantum dots (<b>CDs</b>) and promising food preservatives (<b>FPs</b>), like pyrogallol (<b>PG</b>), benzoic acid (<b>BA</b>), and gallic acid (<b>GA</b>), is highly relevant. This knowledge is crucial for designing <b>CD</b>-based sensors capable of determining the safe levels of these molecules in food and beverages. Additionally, such sensors could be exploited in the development of sustainable, intelligent packaging that controls food shelf life. Based on those considerations, in this study, we post-functionalized blue-emitting <b>CDs</b>, prepared according to a synthetic approach previously developed, with the <b>FP</b> molecules <b>PG</b>, <b>BA</b>, and <b>GA</b> to obtain <b>CD-(FP)</b> systems. UV-vis absorption and FTIR spectroscopy confirmed the presence of the <b>FP</b> molecules on the <b>CD</b> surface. The appearance of a new vibrational band at 1196 cm<sup>-1</sup> in the FTIR spectra of all <b>CD-(FP)</b> systems suggested that the three <b>FP</b> molecules interact with the <b>CD</b> surface via electronic interactions between the aromatic and delocalized electron systems. Further electrochemical analyses of the <b>CD-(PG)</b> and <b>CD-(GA)</b> systems show that the interactions between <b>PG</b> and <b>GA</b> benzene rings and <b>CDs</b> prevent their oxidation to the corresponding quinone forms. Gallic acid ; Molecule ; Pyrogallol ; Benzene ; Quantum dot ; Resorcinol ; Benzoic acid ; Absorption spectroscopy ; Fourier transform infrared spectroscopy Carbon and Quantum Dots Applications ; Electrochemical sensors and biosensors ; Dye analysis and toxicity
Authors: David Bodesheim | Alexander Croy | Gianaurelio Cuniberti
Source: ChemRxiv (preprint, 2025-04-06)
Field: Materials Science — Materials Chemistry

Elastic properties of defective 2D polymers from regression driven coarse-graining Two-dimensional polymers (2DPs) are an interesting class of polymers due to their reticular synthetic assembly which make them an ideal platform for designing materials with specific target properties. Predicting and understanding their elastic behavior is crucial for their application. However, a realistic calculation of their properties remains computationally challenging due to the ubiquitous presence of defects in synthesized 2DPs. Here, we introduce a coarse-graining (CG) approach based on elastic beams called MikadoRR with parameters extracted from simple regression-based fitting. This approach allows us to accurately calculate the elastic properties of defective 2DPs up to the microscale. Furthermore, we show that design principles of 2DPs for tailored elastic properties can be derived from this CG model. Granularity ; Polymer ; Regression ; Statistical physics ; Mathematics ; Materials science ; Statistics ; Physics ; Computer science ; Composite material Machine Learning in Materials Science
Authors: James Suri | Ryan Gilmour
Source: Angewandte Chemie (article, 2025-02-12)
Field: Chemistry — Organic Chemistry

Expediting Glycospace Exploration: Therapeutic Glycans via Automated Synthesis Abstract Glycans regulate a vast spectrum of disease‐related processes, yet effectively leveraging these important mediators in a therapeutic context remains a frontier in contemporary medicine. Unlike many other classes of clinically important biopolymers, carbohydrates derive from discrete biosynthetic pathways and are not produced directly from genes. The conspicuous absence of a biological blueprint to achieve amplification creates a persistent challenge in obtaining well‐defined glycostructures for therapeutic translation. Isolating purified sugars from biological sources is not without challenge, rendering synthetic organic chemistry the nexus of this advancing field. Chemical synthesis has proven to be an unfaltering pillar in the production of complex glycans, but laborious syntheses coupled with purification challenges frequently introduce reproducibility issues. In an effort to reconcile these preparative challenges with the societal importance of glycans, automated glycan synthesis was conceptualised at the start of the 21st century. This rapidly expanding, multifaceted field of scientific endeavor has effectively merged synthetic chemistry with technology and engineering to expedite the precision synthesis of target glycans. This minireview describes the structural diversity and function of glycans generated by automated glycan synthesis platforms over the last five years. The translational impact of these advances is discussed together with current limitations and future directions. Expediting ; Chemistry ; Glycan ; Combinatorial chemistry ; Biochemistry ; Engineering ; Systems engineering Carbohydrate Chemistry and Synthesis ; Glycosylation and Glycoproteins Research ; Chemical Synthesis and Analysis
Authors: Christian Brosseau
Source: Polymer Composites (article, 2025-07-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Exploring Routes to Calculating the Effective Magnetic Permeability of Polymer Magnetic Composites ABSTRACT Our understanding of the effective magnetic permeability of composite materials composed of randomly dispersed metallic ferromagnetic particles within a nonmagnetic (polymer) matrix must respond to several challenges. For these reasons, it is both timely and compelling to consider the primary constraints identified in various modeling approaches in the literature and show that they encounter several critical issues. If not properly addressed, these various systematic constraints in modeling the effective magnetic permeability can introduce limitations in the modeling process. Materials science ; Composite material ; Permeability (electromagnetism) ; Polymer Magnetic Properties of Alloys ; Characterization and Applications of Magnetic Nanoparticles ; Geomagnetism and Paleomagnetism Studies
Authors: Tudorache Iuliana | Cornel Samoilă | Doru Ursuțiu
Source: Preprints.org (preprint, 2025-04-17)
Field: Materials Science — Materials Chemistry

Fatigue Resistance of C60 Steel by Thermochemical Oxidation Treatment The research context of this study focuses on optimizing the use of C60 steel in industrial applications where durability and fatigue resistance are critical. C60 steel is known for its combination of strength and hardness, but it is essential to evaluate how thermal and thermo-chemical treatments affect its performance under fatigue condi-tions. The objectives of the study include: Investigating the crack formation process and the early stages of fatigue in C60 steel, comparing the effects of thermal treatments (hardening and tempering) with those of thermo-chemical treatments (oxidation) on the steel&amp;#039;s micro-structure, evaluating the performance of C60 steel under fatigue conditions based on the applied treatments, providing insights that can help optimize the use of this steel in in-dustries requiring high resistance and durability. The methodology of the study included: fatigue tests performed on a four-point bending machine to determine the exact time of microcracking. The C60 steel samples were pro-cessed according to SR ISO 1099:2017 &amp;quot;Fatigue testing. Axial load method&amp;quot;. To ensure consistency and comparability of results, the samples were made from the same material charge and machined under the same conditions. A total of 26 specimens were used, 13 for each type of treatment: thermal treatment by hardening and tempering and thermo-chemical treatment by oxidation. Due to the different mechanical properties obtained from the thermal and thermo-chemical treatment processes, the sets of specimens were tested at varying forces. In these tests, frequency changes were monitored to evaluate the behaviour of the materials under repeated stresses. Finally, the frequency changes were correlated with the number of cycles to identify when microcracks appeared and their evolution. The main results from this study show: significant differences between the lifetimes of thermally and thermochemically treated samples and the time of microcracks appear-ance in the material. Resistance (ecology) ; Materials science ; Metallurgy ; Biology Metal Alloys Wear and Properties ; High Temperature Alloys and Creep ; Fatigue and fracture mechanics
Authors: P K DINSHA | Dinesh Singh | THEJUS PRASOON | ADARSH PS | ABHINAV SABU
Source: INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT (article, 2025-07-14)
Field: Chemistry — Analytical Chemistry

Fruit Quality Detection Using CNN In todays world where our daily diet mainly consist of junk food, consumption of good quality fruits have become a necessity. Rather than consuming fruits , our main focus must be towards the quality of the fruits. So here comes the importance of a sytem ,which can sort fruits based on its quality , as sorting fruits by human labour is time consuming and also chance of causing error are more comparing to a machine. Therefore we have devised a model to classify fruits based on their appearances to good/bad. The model mainly make use of Machine Vision Techniques[1] to identify the images of fruits. Processing the outer appearance is the primary step. Then the captured image is preprocessed where the important features like the colour or any black spots or any formation of mould or funguses are detected. The captured images are normalized and the data is also augmented in this step. The image preprocessing[2] helps in classification and comparison of image during both training and testing. The model that we selected is Convolutional Neural Network(CNN)[3] Model. This model comes under Deep Learning[4]. KEYWORDS: Machine Vision [1], Deep Learning [4] , Image Processing [2], Machine Learning [7], Normalization [6], Convolutional Neural Network (CNN) [11]. Quality (philosophy) ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) ; Environmental science ; Physics Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Chemical Sensor Technologies
Authors: Alina Ruxandra Caramitu | Magdalena Valentina Lungu | Romeo Cristian Ciobanu | Ioana Ion | Delia Pătroi | Gabriela Sbarcea | Virgil Marinescu | Doina Constantinescu
Source: Processes (article, 2025-04-03)
Field: Materials Science — Polymers and Plastics

Biodegradable Polymer Composites Based on Polypropylene and Hybrid Fillers for Applications in the Automotive Industry This study focuses on the development and characterization of biodegradable polymer composites consisting of a polypropylene (PP) matrix, carbon black pigment, and hybrid fillers. The fillers incorporated into these composites consisted of a blend of fibers and particles derived from natural, biodegradable materials, such as flax fibers (FFs) and wood flour (WF) particles. The compositions of polymer material were expressed as PP/FF/WF weight ratios of 100/0/0, 70/5/25, and 70/10/20. The polymer materials were prepared using conventional plastic processing methods like extrusion to produce composite mixtures, followed by melt injection to manufacture the samples needed for characterization. The structural characterization of the polymer materials was conducted using optical microscopy and X-ray diffraction (XRD) analyses, while thermal, mechanical, and dielectric properties were also evaluated. Additionally, their biodegradation behavior under mold exposure was assessed over six months. The results were analyzed comparatively, and the optimal composition was identified as the polymer composite containing the highest flax fiber content, namely PP + 10 wt.% flax fiber + 20 wt.% wood flour. Polypropylene ; Automotive industry ; Composite material ; Materials science ; Polymer ; Engineering Natural Fiber Reinforced Composites ; biodegradable polymer synthesis and properties ; Additive Manufacturing and 3D Printing Technologies
Authors: Md. Habibur Rahman Aslam | Foyzul Karim | Anisul Islam Suva
Source:  (article, 2025-08-19)
Field: Materials Science — Materials Chemistry

Design and Modeling of a High-Efficiency Unit Concentrated Solar Thermoelectric Generator A concentrated solar thermoelectric generator (CSTEG) is a low-maintenance, silent system that converts focused solar heat into electricity. However, its applications are limited by low efficiency, high material costs, and the need for complex thermal management. In this study, an efficient CSTEG unit was designed and simulated using COMSOL simulation software to operate within 473 K to 353 K. Each leg of CSTEG was segmented into two parts, and materials with a high dimensionless figure of merit (ZT) value were used to enhance efficiency. Melt-spun with excess Te (Te-MS) compound (Bi0.5Sb1.5Te3) and zone-melted Bi0.4Sb1.6Te3 after hot deformation (HD-A-Sb1.6) were used as P-type materials. Bi2Te3 -10 wt% nanocomposites and polycrystalline Bi2Te2.3Se0.7 alloy were used as n-type materials. The simulation results indicate that the CSTEG unit produces an open-circuit voltage of 46.2 mV and a maximum electrical power output of 28.32 mW, with a conversion efficiency of 5.69%. These results demonstrate the potential of material segmentation and optimization in CSTEG design for enhancing thermoelectric performance in concentrated solar applications. Thermoelectric generator ; Materials science ; Energy conversion efficiency ; Thermoelectric effect ; Thermoelectric materials ; Photovoltaic system ; Figure of merit ; Electricity generation ; Optoelectronics ; Solar cell ; Thermal efficiency ; Nuclear engineering ; Composite material ; Mechanical engineering ; Power (physics) ; Electrical engineering ; Thermal conductivity ; Thermodynamics ; Engineering ; Physics ; Chemistry Advanced Thermoelectric Materials and Devices ; Thermal Radiation and Cooling Technologies ; Thermal properties of materials
Authors: Miit Daga | Dhriti Parikh | Swarna Priya Ramu
Source: IEEE Access (article, 2025-01-01)
Field: Chemistry — Inorganic Chemistry

DeepSeqCoco: A Robust Mobile Friendly Deep Learning Model for Detection of Diseases in Cocos Nucifera Coconut tree diseases are a serious risk to agricultural yield, particularly in developing countries where conventional farming practices restrict early diagnosis and intervention. Current disease identification methods are manual, labor-intensive, and non-scalable. In response to these limitations, we come up with DeepSeqCoco, a deep learning based model for accurate and automatic disease identification from coconut tree images. The model was tested under various optimizer settings, such as SGD, Adam, and hybrid configurations, to identify the optimal balance between accuracy, minimization of loss, and computational cost. Results from experiments indicate that DeepSeqCoco can achieve as much as 99.5% accuracy (achieving up to 5% higher accuracy than existing models) with the hybrid SGD-Adam showing the lowest validation loss of 2.81%. It also shows a drop of up to 18% in training time and up to 85% in prediction time for input images. The results point out the promise of the model to improve precision agriculture through an AI-based, scalable, and efficient disease monitoring system. Computer science ; Cocos nucifera ; Artificial intelligence ; Deep learning Coconut Research and Applications ; Advanced Chemical Sensor Technologies ; Peanut Plant Research Studies