jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Clare R. Rees-Zimmerman | José Martín‐Roca | David Evans | Mark A. Miller | Dirk G. A. L. Aarts | Chantal Valeriani
Source: The Journal of Chemical Physics (article, 2025-02-18)
Field: Materials Science — Materials Chemistry

Numerical methods for unraveling inter-particle potentials in colloidal suspensions: A comparative study for two-dimensional suspensions We compare three model-free numerical methods for inverting structural data to obtain interaction potentials, namely, iterative Boltzmann inversion (IBI), test-particle insertion (TPI), and a machine-learning (ML) approach called ActiveNet. Three archetypal models of two-dimensional colloidal systems are used as test cases: Weeks-Chandler-Anderson short-ranged repulsion, the Lennard-Jones potential, and a repulsive shoulder interaction with two length scales. Additionally, data on an experimental suspension of colloidal spheres are acquired by optical microscopy and used to test the inversion methods. The methods have different merits. IBI is the only choice when the radial distribution function is known but particle coordinates are unavailable. TPI requires snapshots with particle positions and can extract both pair- and higher-body potentials without the need for simulation. The ML approach can only be used when particles can be tracked in time and it returns the force rather than the potential. However, it can unravel pair interactions from any one-body forces (such as drag or propulsion) and does not rely on equilibrium distributions for its derivation. Our results may serve as a guide when a numerical method is needed for application to experimental data and as a reference for further development of the methodology itself. Test particle ; SPHERES ; Particle (ecology) ; Drag ; Inversion (geology) ; Statistical physics ; Colloidal particle ; Classical mechanics ; Experimental data ; Physics ; Colloid ; Computer science ; Mechanics ; Mathematics ; Chemistry ; Geology ; Physical chemistry ; Statistics Material Dynamics and Properties ; Electrostatics and Colloid Interactions ; Advanced Thermodynamics and Statistical Mechanics
Authors: Christian Gebhardt | Pascal Bawidamann | Anna-Katharina Spring | Robin Schenk | Konstantin Schütze | Gabriel G. Moya Muñoz | Nicolas Wendler | Douglas A. Griffith | Jan Lipfert | Thorben Cordes
Source: Nature Communications (article, 2025-05-03)
Field: Chemistry — Spectroscopy

Labelizer: systematic selection of protein residues for covalent fluorophore labeling Attaching fluorescent dyes to biomolecules is essential for assays in biology, biochemistry, biophysics, biomedicine and imaging. A systematic approach for the selection of suitable labeling sites in macromolecules, particularly proteins, is missing. We present a quantitative strategy to identify such protein residues using a naïve Bayes classifier. Analysis of >100 proteins with ~400 successfully labeled residues allows to identify four parameters, which can rank residues via a single metric (the label score). The approach is tested and benchmarked by inspection of literature data and experiments on the expression level, degree of labelling, and success in FRET assays of different bacterial substrate binding proteins. With the paper, we provide a python package and webserver ( https://labelizer.bio.lmu.de/ ), that performs an analysis of a pdb-structure (or model), label score calculation, and FRET assay scoring. The approach can facilitate to build up a central open-access database to continuously refine the label-site selection in proteins. Fluorophore ; Computational biology ; Förster resonance energy transfer ; Python (programming language) ; Protein Data Bank (RCSB PDB) ; Computer science ; Chemistry ; Biochemistry ; Fluorescence ; Biology ; Physics Advanced Proteomics Techniques and Applications ; Advanced Biosensing Techniques and Applications ; Biotin and Related Studies
Authors: Seyed Hossein Hashemi | Farshid Torabi
Source: Processes (article, 2025-06-21)
Field: Materials Science — Biomaterials

Machine Learning-Based Prediction of Scale Inhibitor Efficiency in Oilfield Operations Water injection is widely recognized as one of the most important operational approaches for enhanced oil recovery in oilfields. However, this process faces significant challenges due to the formation of sulfate and carbonate mineral scales caused by high salinity in both injected water and formation water. To address this issue, the use of mineral scale inhibitors has emerged as a valuable solution. In this study, we evaluated the performance of seven machine learning algorithms (Gradient Boosting Machine; k-Nearest Neighbors; Decision Tree; Random Forest; Linear Regression; Neural Network; and Gaussian Process Regression) to predict inhibitor efficiency. The models were trained on a comprehensive dataset of 661 samples (432 for training; 229 for testing) with 66 features including temperature; concentrations of various ions (sodium; calcium, magnesium; barium; strontium; chloride; sulfate; bicarbonate; carbonate, etc.), and inhibitor dosage levels (DTPMP, PPCA, PBTC, EDTMP, BTCA, etc.). The results showed that GPR achieved the highest prediction accuracy with R2 = 0.9608, followed by Neural Network (R2 = 0.9230) and Random Forest (R2 = 0.8822). These findings demonstrate the potential of machine learning approaches for optimizing scale inhibitor performance in oilfield operations Scale (ratio) ; Computer science ; Machine learning ; Artificial intelligence ; Petroleum engineering ; Engineering Calcium Carbonate Crystallization and Inhibition ; Drilling and Well Engineering ; Hydraulic Fracturing and Reservoir Analysis
Authors: Hanadi Sawalha | Simon E. Moulton | Andreas Winkel | Meike Stiesch | Bita Zaferanloo
Source: Journal of Functional Biomaterials (review, 2025-04-03)
Field: Materials Science — Materials Chemistry

Role of Endophytic Fungi in the Biosynthesis of Metal Nanoparticles and Their Potential as Nanomedicines Metal nanoparticles (MNPs) produced through biosynthesis approaches have shown favourable physical, chemical, and antimicrobial characteristics. The significance of biological agents in the synthesis of MNPs has been acknowledged as a promising alternative to conventional approaches such as physical and chemical methods, which are confronted with certain challenges. To meet these challenges, the use of endophytic fungi as nano-factories for the synthesis of MNPs has become increasingly popular worldwide in recent times. This review provides an overview of the synthesis of MNPs using endophytic fungi, the mechanisms involved, and their important biomedical applications. A special focus on different biomedical applications of MNPs mediated endophytic fungi involved their antibacterial, antifungal, antiviral, and anticancer applications and their potential as drug delivery agents. Furthermore, this review highlights the significance of the use of endophytic fungi for the green synthesis of MNPs and discusses the benefits, challenges, and prospects in this field. Plant use of endophytic fungi in defense ; Nanotechnology ; Antifungal ; Drug delivery ; Materials science ; Biochemical engineering ; Antimicrobial ; Biotechnology ; Combinatorial chemistry ; Biology ; Chemistry ; Microbiology ; Engineering ; Botany Nanoparticles: synthesis and applications ; Medicinal Plants and Neuroprotection
Authors: Merlin R. Stühler | Alex J. Plajer
Source: Polymer Chemistry (article, 2025-01-01)
Field: Chemistry — Organic Chemistry

Supramolecular polymerization and self-assembly of transition metal complexes Metallo-supramolecular polymers (MSPs) combine metal–ligand coordination and non-covalent interactions. This review highlights how ligand geometry and metal choice influence MSP design for emerging applications. Supramolecular chemistry ; Polymerization ; Self-assembly ; Transition metal ; Polymer chemistry ; Chemistry ; Crystallography ; Organic chemistry ; Catalysis ; Polymer ; Crystal structure Advanced Polymer Synthesis and Characterization ; Mesoporous Materials and Catalysis ; Silicone and Siloxane Chemistry
Authors: Khalil Hadi Hakami | Abdul Rauf Khan | Saad Amin Bhatti
Source: Journal of Mathematics (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Computation of Differential, Integral Operators and Quantitative Structure–Property Analysis of Boron <i>α</i> ‐Icosahedral Nanosheet In its crystalline state, the α ‐icosahedral nanosheet of boron demonstrates superconductivity and thermal electronic properties. Mathematical research on a graph’s structure yields a graph descriptor, a numerical measure. Chemical graph theory employs connectivity descriptors to analyze molecular structures, providing crucial insights into many chemical compounds’ chemical and biological characteristics. These characteristics benefit physicists, chemists, and medical and pharmaceutical specialists. In this paper, the idea of reverse degree–based ‐polynomial is initiated, and differential and integral operators are computed. We formulate reverse degree–based topological descriptors based on this concept. In this paper, we examine the boron α ‐icosahedral nanosheet for this technique. We looked at the physicochemical properties of boron α ‐icosahedral nanosheets using reverse degree–based topological descriptors and best‐fit linear regression models to get an idea of what they are. Researchers are hoping that this strategy will lead them into new areas where they can investigate related studies. Mathematics ; Icosahedral symmetry ; Nanosheet ; Computation ; Property (philosophy) ; Differential (mechanical device) ; Boron ; Pure mathematics ; Algorithm ; Chemistry ; Nanotechnology ; Geometry ; Materials science ; Physics Boron and Carbon Nanomaterials Research ; Graphene research and applications ; Dendrimers and Hyperbranched Polymers
Authors: Wenjun Liang | Limin Yang | Jiaqi Zhu | Yiling Lian | Yang Lü
Source: APL Materials (article, 2025-06-01)
Field: Materials Science — Materials Chemistry

Strain engineering of microfabricated diamond and its applications Diamond is considered the ultimate semiconductor material due to its excellent physical and electrical properties, such as ultrahigh thermal conductivity, ultrawide bandgap, and superhigh carrier mobility. After ultralarge deformation experimentally conducted on diamond in 2018 and 2021, strain engineering is a highly appealing candidate for tuning these properties of diamond, opening up potential applications in microelectronics and quantum technologies. In this review, we briefly review the implementation of strain engineering on diamond, including introducing dynamic strain by nanomechanical tests and maintaining static strain by various methods. We also provide a brief overview of the strain-induced property changes and the specific applications of the strained diamond. Materials science ; Diamond ; Strain (injury) ; Strain engineering ; Nanotechnology ; Engineering physics ; Metallurgy ; Silicon ; Engineering Diamond and Carbon-based Materials Research ; Force Microscopy Techniques and Applications ; Advanced Surface Polishing Techniques
Authors: Boopathy Krishnasamy | Priya Thirugapillai | Amuthakkannan Rajakannu | Mayakannan Selvaraju
Source: Matéria (Rio de Janeiro) (article, 2025-01-01)
Field: Materials Science — Polymers and Plastics

Optimization and multi-objective analysis of tensile, flexural and impact strength in nano-hybrid bio-composites reinforced with Helicteres isora, Holoptelea integrifolia fibers, and nanographene ABSTRACT Industries worldwide seek sustainable, high-strength bio-composites to reduce carbon footprint and replace synthetic materials. This research enhances natural fiber-based composites, ensuring lightweight, cost-effective, and eco-friendly alternatives. It supports green manufacturing and sustainable engineering, promoting a shift away from fossil-based materials. This study aims to optimize the mechanical properties of nano-hybrid bio-composites reinforced with Holoptelea integrifolia fibers, Helicteres isora fibers, and graphene nanosheets within a polypropylene matrix. Using the Box-Behnken design and Response Surface Methodology (RSM), the effects of fiber and graphene composition on tensile, flexural and impact strength were analyzed. The Multi-Objective Particle Swarm Optimization (MOPSO) approach was employed to maximize strength while minimizing composite weight. The optimized composition (15.6721 wt% Holoptelea integrifolia, 15.7198 wt% Helicteres isora, and 0.9307 wt% graphene) achieved a tensile strength of 45.407, flexural strength of 62.0344 MPa and impact strength of 147.119 J/m, demonstrating a significant enhancement. FESEM analysis revealed improved fiber-matrix adhesion, reduced voids, crack path deviation, and fiber bridging mechanisms, which enhanced fracture resistance. These findings support the development of lightweight, high-performance bio-composites, making them ideal for automotive, aerospace, and structural applications where improved strength-to-weight ratios are crucial. Flexural strength ; Ultimate tensile strength ; Composite material ; Materials science ; Nano- ; Composite number Natural Fiber Reinforced Composites ; Additive Manufacturing and 3D Printing Technologies ; biodegradable polymer synthesis and properties
Authors: Lina Guo | Haiqing Tian | Daqian Wan | Yang Yu | Kai Zhao | Xuebo Zheng | Haijun Li | Jianying Sun
Source: Agriculture (article, 2025-05-09)
Field: Chemistry — Analytical Chemistry

Detection of Aflatoxin B1 in Maize Silage Based on Hyperspectral Imaging Technology Aflatoxin B1 (AFB1) is widely present in maize silage feed and poses strong toxicity, seriously threatening livestock production and food safety. To achieve efficient and accurate non-destructive detection of AFB1, this study proposes a quantitative prediction method based on hyperspectral imaging technology. Using the full-spectrum bands after SG, SNV, MSC, FD, SD, and SNV + FD, MSC + FD, SNV + SD, MSC + SD preprocessing, the characteristic wavelengths selected by CARS, BOSS, and RF feature selection methods, and the augmented bands generated by Mixup data augmentation as input features, three models were developed for AFB1 content prediction: a linear WPLSR_SD_Mixup_QPE model, a nonlinear SVR_SD_Mixup_PCA model, and a deep learning CNN_SD_Mixup_WMSE_SA model. The results demonstrated that SD preprocessing was the most suitable for AFB1 detection in maize silage, and the Mixup data augmentation method effectively improved model performance. Among the models, SVR_SD_Mixup_PCA achieved the best performance, with an Rp2 of 0.9458, RMSEP of 3.1259 μg/kg, and RPD of 4.2969, indicating high prediction accuracy and generalization capability. This study fills the gap of hyperspectral image technology fused with artificial intelligence algorithm in the application of quantitative detection of AFB1 content in maize silage and provides a new technical method and theoretical basis for nondestructive testing of corn silage feed. Hyperspectral imaging ; Aflatoxin ; Silage ; Agronomy ; Mycotoxin ; Biology ; Environmental science ; Biotechnology ; Remote sensing ; Geography Spectroscopy and Chemometric Analyses ; Mycotoxins in Agriculture and Food ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Eric Brand | Pol Sallés | Alessandro Palliotto | Haiyuan Wang | Wei Chen | Edwin Dollekamp | M. K. Ramis | Quan Li | Jakub Drnec | J. M. García‐Lastra | Mariona Coll | Nini Pryds | Daesung Park
Source: Advanced Physics Research (article, 2025-08-07)
Field: Materials Science — Materials Chemistry

Strain Engineering of Complex Oxide Membranes on Flexible Metallic Support Abstract Controlling material functionalities via external stimuli is a cornerstone of modern science and technology. One effective strategy involves tuning mechanical strain, which can be induced through lattice mismatch, electric fields, or applied mechanical force. Recent advances in fabricating freestanding single‐crystalline complex oxide membranes have opened new opportunities for integrating these materials onto previously incompatible platforms such as metals and flexible polymers for next‐generation device applications. A key step in strain engineering is understanding and controlling the integration of such materials with flexible substrates. In this study, the integration and adhesion of freestanding single‐crystalline La 0.7 Sr 0.3 MnO 3 (LSMO(001)) membranes onto metallic surfaces (Au, Pt, and TiN) coated on flexible polymer substrates is demonstrated. It is found that the choice of metal underlayer significantly influences the ability to strain the membrane. Using TiN‐coated polymer support, a uniform strain of ≈1% in LSMO membranes, along with strong adhesion between the membrane and substrate, is achieved. Theoretical calculations reveal that strong Ti─O bonding and compact in‐plane lattice matching at the LSMO(001)/TiN(111) interface lower the interface formation energy compared to noble metals. These findings offer valuable insights for selecting suitable platforms to apply external mechanical stress to freestanding oxide membranes, facilitating their integration into flexible electronic systems. Strain engineering ; Membrane ; Strain (injury) ; Oxide ; Materials science ; Metal ; Composite material ; Chemistry ; Metallurgy ; Biology Electronic and Structural Properties of Oxides ; Ferroelectric and Piezoelectric Materials ; Semiconductor materials and devices
Authors: Nima Rezazadeh | Alessandro De Luca | Donato Perfetto | Giuseppe Lamanna | Fawaz Annaz | Mario De Oliveira
Source: Preprints.org (preprint, 2025-10-24)
Field: Materials Science — Materials Chemistry

GAT-CAMDA: A Graph Attention Network Framework for Temperature-Resilient SHM of Composite Plates This study introduces GAT-CAMDA, a novel framework for structural health monitoring (SHM) of composite materials under temperature-induced variability, leveraging the powerful feature extraction capabilities of Graph Attention Networks (GATs) and advanced domain adaptation techniques. By combining Maximum Mean Discrepancy (MMD) and Correlation Alignment (CORAL) losses with domain-discriminative adversarial layer, the framework achieves precise alignment of feature distributions across temperature domains, ensuring robust damage detection without relying on labelled target data. A bespoke data augmentation process extrapolates damage behaviour across unmeasured temperature conditions, addressing the scarcity of damaged-state observations. Hyperparameter optimisation via Optuna not only identifies optimal settings to enhance model performance, achieving a classification accuracy of 95.83% on a benchmark dataset but also illustrates hyperparameter significance for explainability. Additionally, the GAT architecture’s attention demonstrates the importance of various sensors, enhancing transparency and reliability in damage detection. The dual use of Optuna serves to refine model accuracy and elucidate parameter impacts, while GAT-CAMDA represents a significant advancement in SHM, enabling precise, interpretable, and scalable diagnostics across complex operational environments. Hyperparameter ; Structural health monitoring ; Scalability ; Reliability (semiconductor) ; Graph ; Bespoke ; Process (computing) ; Interpretability ; Benchmark (surveying) Machine Learning in Materials Science ; Ultrasonics and Acoustic Wave Propagation ; Model Reduction and Neural Networks
Authors: Harrison Specht | Maddy Yeh | Sarah N. Sipe | David Barnes‐Seeman | Mark E. Adamo | Kevin Mcdonnell | Michael P. Agius | Corinna Friedrich | Weiqiang Pang | Yanchang Huang | Kalyani Raju | Wayne Vuong | Michael A. Lee | Ahmet Yeşilçimen | Bradley L. Pentelute | Nikolai Slavov
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-05-27)
Field: Chemistry — Spectroscopy

PSMtags improve peptide sequencing and throughput in sensitive proteomics Mass spectrometry-based proteomics enables comprehensive characterization of protein abundance, function, and interactions. Label-free approaches are simple to implement but challenging to scale to thousands of samples per day. Multiplexed techniques, such as plexDIA, can address these limitations but remain restricted by the lack of mass tags optimized for data-independent acquisition (DIA) workflows. Here, we present a systematic approach screening a library of 576 compounds that identifies several small molecules that, when conjugated to peptides, improve their detection and sequence identification by mass spectrometry. The lead molecule, PSMtag, substantially increases the detection of fragment b-ions, which increases the confidence of sequence identification and enhances de novo sequencing. PSMtags allow 9-plexDIA, using only stable isotopes of carbon, oxygen and nitrogen. As a result, it allows simultaneously increasing proteome coverage and sample throughput for plexDIA workflows without compromising quantitative accuracy. We demonstrate 240 samples-per-day with 9-plexDIA, while acquiring 28,359 protein data points in the same time label-free methods acquire 4,340. Our approach constitutes an expandable framework for designing mass tags to overcome existing limitations in multiplexed proteomics and provides plexDIA reagents capable of analyzing over 1,000 samples per day when using 10 minute runs. By facilitating higher throughput and improved identification, this innovation holds significant potential for accelerating proteomic studies across diverse biological and clinical applications. Throughput ; Proteomics ; Computational biology ; Peptide ; DNA sequencing ; Computer science ; Chemistry ; Biology ; Biochemistry ; Gene ; Telecommunications Advanced Proteomics Techniques and Applications ; Peptidase Inhibition and Analysis ; Ubiquitin and proteasome pathways
Authors: Hüsnügül Yılmaz Atay | Jacek Łukasz Wilk-Jakubowski | Valentyna Loboichenko
Source: Materials (article, 2025-06-05)
Field: Materials Science — Polymers and Plastics

Novel Flame-Retardant Wood-Polymer Composites by Using Inorganic Mineral Huntite and Hydromagnesite: An Aspect of Application in Electrical Engineering In this study, a flame-retardant wood-polymer composite was produced using huntite-hydromagnesite mineral, recognized for its non- flammability properties. In this context, wood-polymer composites were produced with the co-rotating twin-screw extrusion technique, while polypropylene was applied as the composite matrix, medium density fiberboard waste and inorganic huntite-hydromagnesite mineral were used as the reinforcement material. The proportion of wood powder additives was changed to 10% and 20%, and the huntite and hydromagnesite ratio was changed to 30%, 40%, 50% and 60%. Maleic anhydride grafted polypropylene, i.e., MAPP, was applied as a binder at a rate of 3%. Polypropylene, wood fibers, mineral powders, and MAPP blended in the mixer were processed in the extruder and turned into granules. Structural, morphological, thermal, mechanical, and flame-retardant properties of the composites were analyzed using XRD, SEM, FTIR, TGA, tensile testing, and the UL-94 vertical flammability test. Test samples were prepared to evaluate the physical and mechanical properties with a compression molding machine. It was concluded that the composites gained significant flame retardancy with the addition of huntite hydromagnesite. The potential for using this material in various fields and its compliance with the principles of circular economy and the Sustainable Development Goals (SDG 12) were noted. Fire retardant ; Materials science ; Polypropylene ; Flammability ; Composite material ; Wood flour ; Composite number ; Compression molding ; Extrusion ; Maleic anhydride ; Polymer ; Copolymer ; Mold Flame retardant materials and properties ; Natural Fiber Reinforced Composites ; Polymer Nanocomposites and Properties