jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Olalekan John Okesanya | Tolutope Adebimpe Oso | Uthman Okikiola Adebayo | Oluwatobi Babajide Ayelaagbe | Khalifat Boluwatife Obadeyi | Moyosore Esther Ogunmuyiwa-James | Abdulrahman Kayode Yahaya | Clement Ngele Chukwu | Kabiru Olalekan Tajudeen | Olaoluwa Joseph Oso | Mohamed Mustaf Ahmed | Ifrah Ali | Don Eliseo Lucero-Prisno III
Source: Exploration of Drug Science (article, 2026-01-28)
Field: Materials Science — Materials Chemistry

Harnessing nanomaterials to overcome antimicrobial resistance in Gram-positive bacteria: a systematic review Background: Antimicrobial resistance (AMR) among Gram-positive bacteria has emerged as a significant global health threat, with pathogens such as methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant Enterococcus faecium (VRE) exhibiting increasing resistance to conventional antibiotics. This systematic review evaluates new advances in nanomaterial-based antimicrobial agents as innovative solutions to combat AMR in Gram-positive bacteria. Methods: Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, studies published between 2014 and 2024 were systematically screened and analysed from databases including PubMed, Scopus, Google Scholar, and HINARI. From an initial 1,405 articles, 131 experimental studies that met the inclusion criteria were systematically analysed to harness the advances in nanomaterial-based antimicrobial agents in combating AMR in Gram-positive bacteria. Results: The included studies demonstrated that various nanomaterials, including silver nanoparticles (AgNPs), gold nanoparticles (AuNPs), zinc oxide nanoparticles (ZnO NPs), copper and copper oxide nanoparticles (Cu/CuO NPs), as well as polymeric and hybrid systems, exhibited potent antibacterial and antibiofilm activities. Key mechanisms of action included bacterial membrane disruption, reactive oxygen species (ROS) generation, intracellular interference, and targeted drug delivery. Many nanomaterials showed enhanced efficacy and synergistic effects when combined with conventional antibiotics, effectively reducing bacterial load and inhibiting biofilm formation in resistant strains like MRSA. Discussion: Nanomaterials offer a multifaceted approach to overcome the evolving resistance mechanisms in Gram-positive pathogens, showing significant preclinical and clinical success. Despite these substantial preclinical results, challenges such as cytotoxicity, environmental impact, scalability, and the potential for resistance adaptation remain unaddressed. Furthermore, important translational barriers persist, most notably insufficient pharmacokinetic data and unclear regulatory pathways. Future efforts must focus on standardized manufacturing, comprehensive toxicity studies, and robust clinical trials to bridge the gap between laboratory innovation and practical therapeutic application. Antimicrobial ; Antibiotic resistance ; Enterococcus faecium ; Biofilm ; Drug resistance ; Nanomaterials ; Multiple drug resistance ; Antimicrobial drug Nanoparticles: synthesis and applications ; Antimicrobial agents and applications ; Nanoplatforms for cancer theranostics
Authors: Madihan Yusof | Sofiyah Mohd Razali | Ros Syazmini Mohd Ghani | Mohamad Saiful Sulaiman
Source: Journal of tropical resources and sustainable science/Journal of Tropical Resources and Sustainable Science (article, 2026-03-31)
Field: Materials Science — Polymers and Plastics

&lt;b&gt;A bibliometric analysis of fiber cement board research: Trends, development and future direction&lt;/b&gt; Over the past decade, research on fiber cement board (FCB) materials has experienced significant growth, driven by the demand for sustainable and high-performance construction materials. This bibliometric analysis examines global research trends in FCB studies from 2021 to 2025 by systematically retrieving publications from Scopus using predefined search strings related to fiber cement boards. The study applied filters for peer-reviewed articles published in English and utilized the bibliometric tools VOSviewer to analyze citation networks, co-authorship patterns, keyword co-occurrence, and thematic evolution. This approach enabled the identification of influential publications and authors, as well as the detection of emerging areas of interest. A comprehensive database search revealed a growing focus on incorporating various fiber reinforcements, including synthetic and natural fibers, to enhance mechanical, thermal, and durable properties. Notably, research on hybrid fiber reinforcement and alternative binders has gained prominence due to their potential to improve sustainability and structural performance. The study revealed a rising publication trend, peaking at nearly 650 articles in 2024, with China leading in output (854 publications) and citations (10,373), while Australia demonstrated the highest impact (19.9 citations per paper). Highly cited authors such as Zaid, O. (691 citations) and Kaplan, G. (471 citations) have made significant contributions, with Construction and Building Materials emerging as the dominant journal. These quantified insights highlight key contributors, research influence, and thematic shifts toward AI integration, sustainability, and fiber innovation in FCB studies. The results highlight the importance of material innovation, emphasizing the role of fiber-modified cement composites in advancing environmentally friendly and resilient construction practices. This study serves as a crucial reference for shaping future research and industrial applications in sustainable building materials. Sustainability ; Scopus ; Bibliometrics ; Citation ; China ; Keyword search ; Fiber Natural Fiber Reinforced Composites ; Innovative concrete reinforcement materials ; Concrete and Cement Materials Research
Authors: Ian Wolde | Allan R. Starke | Alexandre Da Silva | José M. Cardemil
Source: SolarPACES Conference Proceedings (article, 2026-01-21)
Field: Materials Science — Materials Chemistry

Estimation of Industrial Heat Balance Based on Actual Plant Data: A Case Study for Cement Plant The industrial sector plays a significant role in anthropogenic effect on the environment due to greenhouse gases emissions associated with energy-intensive processes. To advance in the sustainability goals, it is essential to propose new modeling methodologies to assess the yield performance of clean technologies on industrial processes. However, due to the complexity and variety of such processes it is particularly challenging to assess the non-linear performance of industrial process in terms of heat loads, temperature, and others. The present study introduces a novel methodology to configure an estimation model of the heat released by the clinkering process in the cement industry, based only on operational data. Furthermore, a methodology is proposed to evaluate the correlation between the heat of the process and the operational parameters. The result of this methodology provides a function for the heat consumed or released by the chemical reactions of the process, based on the relevant operational parameters. The approach proposes a global specific enthalpy, parameter that represents the response of the process, only through the heat consumed or released by the chemical reactions. A methodology is proposed for evaluating the correlation of operational parameters with the proposed specific enthalpy. The methodology is validated by assessing the pyro-processes in the cement industry, renowned for its high energy intensity and pollution emissions. The proposed methodology could facilitate the evaluation of clean technologies into different industrial processes. This approach can guide industries in identifying opportunities for reducing pollution emissions while optimizing energy efficiency. Process (computing) ; Estimation ; Greenhouse gas ; Sustainability ; Cement ; Function (biology) ; Energy consumption ; Variety (cybernetics) ; Waste heat Thermal and Kinetic Analysis ; Thermochemical Biomass Conversion Processes ; Process Optimization and Integration
Authors: G. Manavendra | Vinay Atgur | S. Basavarajappa | B. Nageswara Rao | N. R. Banapurmath | S. Dhanalakshmi | Ashok M. Sajjan | Irfan Anjum Badruddin | M. A. Umarfarooq | S. M. Abdul Khader | Essam R. I. Mahmoud
Source: Scientific Reports (article, 2026-01-04)
Field: Materials Science — Polymers and Plastics

An investigation into the physico-chemical characteristics and performance of surface-treated areca fibers This research explores chemical and mechanical treatments to enhance the properties of areca husk fibers, an abundant resource in Karnataka. A primary focus of this work is the comprehensive characterization of these fibers. Fibers, averaging 35 mm in length and 300 [Formula: see text] in diameter, were extracted from the husk. Chemical analysis revealed a predominantly cellulose composition with low lignin content. Following extraction, the fibers underwent alkaline treatment, and their physical, mechanical, and chemical characteristics were thoroughly assessed. Significant improvements in mechanical properties, including tensile strength, Young's modulus, and elongation at break, were observed after alkaline treatment. The microfibrillar angle was theoretically estimated, and the calculated theoretical strength was compared with experimental results. Morphological analysis further elucidated the fiber's structural characteristics. Overall, areca fiber properties were found to be comparable to other natural fibers, highlighting their potential for developing eco-friendly and cost-effective composites. Areca ; Ultimate tensile strength ; Cellulose ; Lignin ; Chemical composition ; Husk ; Elongation Natural Fiber Reinforced Composites ; Advanced Cellulose Research Studies ; Bamboo properties and applications
Authors: Andikan J. Nwosu | Liang Chen | Rashmi Kumar | Yumi Kwon | Shaun M. Goodyear | Adel Kardosh | James M Fulcher | Ljiljana Pasa-Tolic
Source: bioRxiv (Cold Spring Harbor Laboratory) (article, 2026-02-09)
Field: Chemistry — Spectroscopy

Evaluation and application of chemical decrosslinking in the context of histopathological spatial proteomics Laser capture microdissection (LCM) - based spatial mass spectrometry proteomics is a rapidly emerging technique with strong potential for use in formalin-fixed, paraffin-embedded (FFPE) tissues. Several sample-preparation methods have been developed to decrosslink FFPE proteins for spatial proteomics; however, residual crosslinks often remain, and depth can remain impaired relative to fresh frozen tissue samples. To increase proteome coverage in spatially resolved LCM-FFPE samples, we investigated a panel of chemical compounds with the potential to catalyze the decrosslinking of nucleophilic functional groups on proteins. Systematic screening and optimization of temperature, incubation time, and reagent concentration led to the identification of 3,4-diaminobenzoic acid as an effective agent for improving proteome coverage in FFPE pancreatic tissue. This compound could boost precursor identifications by more than 10% at both reduced (70 °C) and high (90 °C) temperatures. Application of this chemical-decrosslinking strategy to a pancreatic ductal adenocarcinoma tissue section enabled the identification of numerous cell-type-enriched proteins with clinical and therapeutic relevance. Taken together, our findings show that chemical decrosslinking can increase proteome coverage in FFPE tissues, thereby advancing our understanding of tissue microenvironments in physiological and pathological contexts. Proteome ; Laser capture microdissection ; Proteomics ; Context (archaeology) ; Identification (biology) ; Microdissection Advanced Proteomics Techniques and Applications ; Molecular Biology Techniques and Applications ; Mass Spectrometry Techniques and Applications
Authors: Mythili Senthil
Source: Aeronautical and Aerospace Engineering (article, 2026-03-28)
Field: Materials Science — Polymers and Plastics

Advanced Fire-Resistant Materials for Future Construction Advanced fire-resistant materials are at the forefront of mitigating fire hazards in modern buildings. These materials improve the structural stability of buildings during a fire and improve the safety and well-being of occupants. Key innovations include high-performance composites, intumescent coatings and advanced ceramics. Each of these materials offers unique advantages that address different aspects of fire resistance, from delaying the spread of flames to maintaining structural stability. Research significance: The primary goal of developing advanced fire-resistant materials is to improve safety for occupants and protect property from fire damage. By studying and using materials with improved fire-resistance properties, researchers can help reduce the frequency of fire-related injuries and deaths. This is especially important in densely populated urban areas and skyscrapers, where fire safety is a significant issue. Methology: Alternative: Magnesium oxide board, calcium silicate board, gypsum board with additives, cement-bonded particle board and fiber-reinforced polymer (FRP) composites. Evaluation Preference: Thermal Stability (°C), Mechanical Strength (MPa), Weight (kg/m²), Emissions (kg CO/m²). Result: The results indicate that Fibre-Reinforced Polymer (FRP) Composites achieved the highest rank, while Gypsum Board with Additives had the lowest rank being attained. Conclusion: “The value of the dataset for Advanced Fire-Resistant Materials for Future Construction, according to the weighted product method, Fibre-Reinforced Polymer (FRP) Composites achieves the highest ranking.” Gypsum ; Intumescent ; Fire safety ; Thermal stability ; Fire performance ; Smoke ; Filler (materials) Flame retardant materials and properties ; Magnesium Oxide Properties and Applications ; Fire effects on concrete materials
Authors: K. Zhou | Taiping Zhang | Suyalatu Zhang | Dexin Wang | Shujie Hao | Ruonan Wei
Source: Beverages (article, 2026-03-05)
Field: Chemistry — Analytical Chemistry

Research on the Method of Tea Variety Traceability Based on Near-Infrared Spectroscopy To establish a rapid traceability method for tea varieties and address the limitations of traditional identification techniques, this study focused on four types of tea—Longjing, Maofeng, Zhuyeqing, and Biluochun—using near-infrared (NIR) spectroscopy. A total of 84 sets of NIR spectra were collected and preprocessed using Savitzky–Golay smoothing (S-G), multiplicative scatter correction (MSC), standard normal variate transformation (SNV), and first derivative (1stDer) methods. Dimensionality reduction and feature selection were then performed using principal component analysis (PCA), linear discriminant analysis (LDA), their combination (PCA-LDA), and the successive projections algorithm (SPA). Classification models based on multiple linear regression (MLR) and support vector machine (SVM) were constructed and evaluated via five-fold cross-validation to assess generalization ability and stability. The results indicated that the SVM model significantly outperformed the MLR model in overall classification and generalization. The PCA-LDA combined approach proved to be the most effective feature selection method. The optimal classification model for tea variety traceability was achieved using MSC or SNV preprocessing combined with PCA-LDA-SVM, yielding a mean five-fold cross-validation accuracy of 96.67%. The confusion matrix revealed that misclassifications mainly occurred between Longjing and Biluochun and between Maofeng and Zhuyeqing, which can be attributed to similarities in processing techniques and chemical composition among these tea varieties. This study provides a rapid, non-destructive, and accurate spectroscopic detection method for tea quality control and traceability, offering a valuable reference for the rapid identification of agricultural products. Feature selection ; Principal component analysis ; Support vector machine ; Pattern recognition (psychology) ; Traceability ; Linear discriminant analysis ; Preprocessor ; Feature (linguistics) ; Smoothing Spectroscopy and Chemometric Analyses ; Tea Polyphenols and Effects ; Advanced Chemical Sensor Technologies
Authors: Ahmed A. Abdou Elabbasy | Abdulrazak H. Almaliki | M. Anwar H. Khan | Paul O. Awoyera | Olaolu George Fadugba
Source: Scientific Reports (article, 2026-02-17)
Field: Materials Science — Polymers and Plastics

Response surface modeling and correlation analyses of mechanical and non-destructive properties in graphene–date palm fiber reinforced concrete Achieving fracture toughness and sustainability in concrete remains a major challenge in modern construction. This study explores the synergistic effects of graphene nano-platelets (GNPs) and date palm fiber (DPF) on the mechanical and eco-efficiency performance of concrete. Eleven mix proportions with varying GNP (0-0.2%) and DPF (0-2.5%) contents were tested. Response surface methodology was employed to model five key properties-compressive strength, flexural strength, modulus of elasticity, split tensile strength, and ultrasonic pulse velocity. Pearson and canonical correlation analyses were used to examine property interrelations. Multi-objective optimization indicated that incorporating 0.199% GNPs and 0.99% DPF achieved optimal performance, enhancing compressive strength by 44%. The developed models exhibited high accuracy (R² > 0.96, desirability = 0.971). While GNPs increased embodied carbon, the overall approach balances performance, modeling precision, and sustainability, offering valuable insights for next-generation green infrastructure materials. Ultimate tensile strength ; Flexural strength ; Response surface methodology ; Compressive strength ; Toughness ; Fiber ; Fracture toughness ; Modulus Natural Fiber Reinforced Composites ; Innovative concrete reinforcement materials ; Concrete and Cement Materials Research
Authors: Mark A. Boyer | Daniel Tabor
Source: ChemRxiv (article, 2026-01-12)
Field: Materials Science — Materials Chemistry

A Construction of Arbitrary Order Internal Coordinate Transformations to Improve Studies of Large Amplitude Motions Internal coordinates and their derivatives underpin the efficient treatment of geometry optimizations, high-resolution spectroscopic simulation, and the fitting of potential surfaces in quantum chemistry. Existing descriptions of the construction of internal coordinate derivatives generally either lack simplicity or generality. In this paper, we provide a simple framework for evaluating any internal coordinate derivative to any order and an automatic approach to obtain the corresponding inverse transformation. Through further extension to transformations between internal coordinate systems, this approach provides a complete, generic method for handling a wide variety of molecular problems. The utility of this construction is demonstrated by investigations into the behavior of internal coordinate interpolations for studying isomerizations, quantifying the coupling between carbonyl stretches and a complex stretch coordinate in an organometallic system, and analysis of the performance of a machine learned interatomic potential in computing anharmonic frequencies as a function of low-frequency coordinate distortions. This approach is shown to be numerically efficient as well as general, and a reference implementation is provided. Coordinate system ; Coordinate space ; Simple (philosophy) ; Function (biology) ; Anharmonicity ; Ellipsoidal coordinates ; Inverse ; Log-polar coordinates ; Generalized coordinates ; Elliptic coordinate system Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Crystallography and molecular interactions
Authors: N.K. Tanasheva | D.A. Afanasyev | G.A. Ranova | L.L. Minkov | A.N. Dyusembayeva | E.V. Sheinmaiyer | T.A. Rakhimgaliyev | M.K. Kaliaskarova
Source: Bulletin of the Karaganda University Physics Series (article, 2026-03-29)
Field: Materials Science — Materials Chemistry

Designing and Prototyping a Microcontroller Device for Measuring the Main Parameters of Solar Panels This article presents the results of prototyping and designing a device for measuring the basic characteristics of solar panels (photocurrent and voltage) and climatic conditions (temperature and illumination). This device is being developed to identify potential faults in the solar panel-microinverter system, as well as the causes of reduced efficiency in converting light energy into electrical energy. As a result of prototyping, a prototype of the measuring device was assembled using an Arduino Mega 2560 R3 with a W5100 Ethernet module. Cur rent is measured by an ACS712 sensor. Voltage is measured via a voltage divider connected to the microcon troller's analog-to-digital converter input. OPT4003 light and DS18B20 temperature sensors are used. The IoT device is assembled using a RAK3172 sensor module and INA228, OPT4003, and DS18B20 microcir cuits. A power module based on two IRFP460 field-effect transistors was developed to measure the solar panel's current-voltage characteristics. Software has been written for the developed prototype and the IoT de vice. Microcontroller ; Ethernet ; Rapid prototyping ; Software ; Voltage ; Arduino ; Solar energy ; Temperature measurement ; Power (physics) Advanced Materials and Semiconductor Technologies ; Solar Radiation and Photovoltaics ; Photovoltaic System Optimization Techniques
Authors: Ngan Le Viet | Quynh Do Truc | Nhi Trinh Yen | Le Vu Thi Nhat | Thuy Do Thi Hong | Hieu Tran Huy | Trang Vũ Thị | Hao Le Thi Hong | Thao Ta Thi | Thanh Nguyen Duc
Source: Heavy metals and arsenic concentrations in water agricultural soil and rice in Ngan Son district Bac Kan province Vietnam (article, 2026-02-28)
Field: Chemistry — Analytical Chemistry

Preliminary study on distinguishing commercial orange juices based on chemical composition analysis at the laboratory scale Orange juice is among the most consumed fruit juices worldwide, valued for its nutritional and sensory qualities. However, due to its high economic value, orange juice is also one of the most vulnerable commodities to adulteration, particularly through dilution or substitution with cheaper ingredients. This study aimed to differentiate between natural and commercial orange juice and to evaluate the ability to detect adulteration by dilution with sucrose and citric acid solutions using multivariate statistical approaches. A total of 50 samples, including fresh-squeezed orange juice, commercial juices, and artificially adulterated juices at different levels (10%, 20%, 30%, 50%, and 90%), were analyzed for organic acid composition (oxalic acid, malic acid, ascorbic acid, citric acid, and furmaric acid), flavonoids (hesperidin, narirutin) analyzed by HPLC-PDA and sugars (fructose, glucose, and sucrose) analyzed by HPLC-RID. Principal Component Analysis (PCA) was first applied to reduce data dimensionality and to identify major compounds contributing to the variation among samples. Subsequently, Discriminant Analysis based on PCA scores (PCA-DA) was used for classification and model validation. The results showed that PCA successfully separated natural juices from commercial ones, with citric acid, glucose, fructose, hesperidin, and narirutin being the main discriminant markers. For the adulterated juice experiment, PCA-DA achieved clear separation of groups, with 100% correct classification when three principal components were included in the model. These findings confirm that combining chemical profiling with PCA and PCA-DA is a reliable and efficient approach for authenticity testing of orange juice. The method provides a promising analytical tool for quality control laboratories and regulatory agencies to combat economically motivated adulteration in fruit juices. Principal component analysis ; Orange juice ; Orange (colour) ; Ascorbic acid ; Citric acid ; Malic acid ; Linear discriminant analysis ; Sensory analysis Spectroscopy and Chemometric Analyses ; Fermentation and Sensory Analysis ; Edible Oils Quality and Analysis