jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yarkın A. Çetin | Benjamí Martorell | Francesc Serratosa | Mónica Calatayud
Source: ACS Omega (article, 2024-09-05)
Field: Materials Science — Materials Chemistry

Adsorption of Guanine on Oxygen-Deficient TiO<sub>2</sub> Surface: A Combined MD-DFTB/DFT Strategy Metal oxides (MOs) are key materials in many fields, including technological, industrial, and biomedical applications. In most of these implementations, surface reactivity and reducibility properties are critical considerations. In their nanosized form, MOs exhibit enhanced reactivity that is connected to toxicity. Besides the fact that the biological molecule and the surface of the corresponding material interact chemically, little is known about the toxicological mechanisms involved on the atomic scale. The goal of this study is to investigate the role of TiO<sub>2</sub> surfaces in interaction with one genetic base, namely guanine. Using a combination of the quasi-electronic density functional-tight binding molecular dynamics simulations and density functional theory calculations, we explored the adsorption modes of guanine with a stoichiometric and oxygen-deficient anatase TiO<sub>2</sub> (101) surface. With such an approach, we have characterized new adsorption modes not previously found, and we have highlighted the relevance of defective surfaces in the adsorption of genetic basis, as a model for explaining possible toxicology mechanisms induced by the adsorption process. Reactivity (psychology) ; Adsorption ; Metal ; Molecule ; Nanotechnology ; Materials science ; Guanine ; Chemistry ; Combinatorial chemistry ; Computational chemistry ; Chemical engineering ; Physical chemistry ; Organic chemistry ; Metallurgy Catalytic Processes in Materials Science ; Nanomaterials for catalytic reactions ; Graphene research and applications
Authors: V Alekhya | B Rajalakshmi | Sonika Kochar | Irfan Khan | Surovi Paul | Zaid Alsalami | Vanya Arun
Source: E3S Web of Conferences (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

RETRACTED: Nanofertilizers: Transforming Agriculture for Sustainable Food Production Following the publication of the Retraction notice , this article was formally retracted on 3 April 2026. Agriculture ; Production (economics) ; Sustainable agriculture ; Food processing ; Business ; Agricultural economics ; Agricultural science ; Natural resource economics ; Agricultural engineering ; Environmental science ; Economics ; Geography ; Food science ; Engineering ; Biology Nanoparticles: synthesis and applications ; Graphene and Nanomaterials Applications
Authors: Wenhui Liu | Zhijian Wu | Di Wu | Dingjiang Huang | Ziyuan Zhao
Source:  (article, 2024-07-26)
Field: Materials Science — Materials Chemistry

Imagination-Limited Q-Learning for Offline Reinforcement Learning Offline reinforcement learning seeks to derive improved policies entirely from historical data but often struggles with over-optimistic value estimates for out-of-distribution (OOD) actions. This issue is typically mitigated via policy constraint or conservative value regularization methods. However, these approaches may impose overly constraints or biased value estimates, potentially limiting performance improvements. To balance exploitation and restriction, we propose an Imagination-Limited Q-learning (ILQ) method, which aims to maintain the optimism that OOD actions deserve within appropriate limits. Specifically, we utilize the dynamics model to imagine OOD action-values, and then clip the imagined values with the maximum behavior values. Such design maintains reasonable evaluation of OOD actions to the furthest extent, while avoiding its over-optimism. Theoretically, we prove the convergence of the proposed ILQ under tabular Markov decision processes. Particularly, we demonstrate that the error bound between estimated values and optimality values of OOD state-actions possesses the same magnitude as that of in-distribution ones, thereby indicating that the bias in value estimates is effectively mitigated. Empirically, our method achieves state-of-the-art performance on a wide range of tasks in the D4RL benchmark. Solvation ; Computer science ; Artificial neural network ; Artificial intelligence ; Graph ; Machine learning ; Theoretical computer science ; Chemistry ; Molecule Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Topic Modeling
Authors: Sujeeth Swamy | Usha Philipose | Ankit Meheta | Mohammed Al-Fatlawi | H Pal Thethi | Bhishm Pratap | Din Bandhu
Source: E3S Web of Conferences (review, 2024-01-01)
Field: Materials Science — Materials Chemistry

RETRACTED: A Review of Numerical Simulation and Modeling in High Strain Rate Deformation Processes This proceeding volume has been retracted from the publication because we found some solid reasons to believe that it has infringed our integrity criteria and now presents a risk for our journal and scholarly science in general. Different types of malpractice are involved, in particular citation manipulation and inappropriate references. We are extremely concerned by such malpractice which considerably impacts the image of our title and our Publisher’s reputation. For further details, please refer to our publishing ethics policies . If you have any questions, please contact us at contact@webofconferences.org See the retraction notice E3S Web of Conferences 505 , 00001 (2024), https://doi.org/10.1051/e3sconf/202450500001 Strain rate ; Deformation (meteorology) ; Strain (injury) ; Computer science ; Materials science ; Composite material ; Biology High-Velocity Impact and Material Behavior ; Mechanical Behavior of Composites ; Structural Response to Dynamic Loads
Authors: Hans‐Christian Weissker | F. Calvo
Source: Alloys (article, 2024-02-20)
Field: Materials Science — Materials Chemistry

Optical Properties of AgAu Alloy Clusters: Effect of Chemical Configuration along a Rearrangement Pathway Gold and silver are, for all their chemical similarities, optically very different. Small Ag clusters show a localized surface-plasmon resonance (LSPR), whereas in Au clusters smaller than about 300 atoms, the resonance is absent due to the coupling with the interband transitions from the d electrons. This opens the possibility of tuning the cluster properties depending on their composition and chemical configuration. Earlier work on AgAu alloy clusters has shown that the outermost shell of atoms is crucial to their overall optical properties. In the present contribution, we consider the optical spectroscopic properties associated with the structural rearrangement in 55-atom AgAu alloy clusters in which the core transforms from pure silver to pure gold. Calculations using time-dependent density-functional theory are complemented by an in-depth study of the subtle effects that the chemical configuration has on the details of the materials’ d bands. Although the cluster surface remains alloyed, the geometrical changes translate into strong variations in the optical properties. Cluster (spacecraft) ; Alloy ; Atom (system on chip) ; Coupling (piping) ; Materials science ; Density functional theory ; Chemical physics ; Electron ; Resonance (particle physics) ; Surface plasmon resonance ; Work (physics) ; Core (optical fiber) ; Molecular physics ; Atomic physics ; Chemistry ; Nanotechnology ; Computational chemistry ; Physics ; Nanoparticle ; Metallurgy Nanocluster Synthesis and Applications ; nanoparticles nucleation surface interactions ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Fan Zhang | Wenxiu Wang | Qianyun Ma | Shi Qi | Kexiang Chen | Xinyue Pan | Shiyuan Xie | Jianfeng Sun
Source: Food Chemistry X (article, 2024-06-17)
Field: Chemistry — Analytical Chemistry

A two-dimensional correlation spectroscopy analysis-based approach for asymptomatic rot detection in stored potatoes using hyperspectral imaging Fusarium dry rot (FDR), which is caused by several Fusarium species, is a major disease affecting potatoes during storage. The study aimed to identify the gleyic stage and monitor rot progression in stored potatoes using a hyperspectral imaging (HSI) system. We evaluated the susceptibility parameters and quality attributes during the infection process and monitored starch, soluble protein, malondialdehyde, and aerobic bacterial contents in all samples. To further characterize the infection process, we collected spectral data on different storage days and then mapped these data using two-dimensional correlation spectroscopy. The results revealed 20 peaks related to these component contents. Then, the quantitative analysis models of these indicators were established based on the 2D correlation synchronization spectrum. The optimal correlation coefficients of the validation set were 0.9273, 0.9634, 0.9470, and 0.9487 for these indicators. Visual analysis was implemented to these indicators, and the content distribution can be effectively observed on hyperspectral images. Hyperspectral imaging ; Correlation ; Fusarium ; Mathematics ; Biological system ; Food science ; Chemistry ; Environmental science ; Horticulture ; Biology ; Artificial intelligence ; Computer science Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Essential Oils and Antimicrobial Activity
Authors: Jiraporn Sripinyowanich Jongyingcharoen | Suppakit Howimanporn | Agustami Sitorus | Thitima Phanomsophon | Jetsada Posom | Thanapol Salubsi | Adisak Kongwaree | Chin Hock Lim | Kittisak Phetpan | Panmanas Sirisomboon | Satoru Tsuchikawa
Source: Polymers (article, 2024-01-08)
Field: Chemistry — Analytical Chemistry

Classification of the Crosslink Density Level of Para Rubber Thick Film of Medical Glove by Using Near-Infrared Spectral Data Classification of the crosslink density level of para rubber medical gloves by using near-infrared spectral data combined with machine learning is the first time reported in this paper. The spectra of medical glove samples with different crosslink densities acquired by an ultra-compact portable MicroNIR spectrometer were correlated with their crosslink density levels, which were referencely evaluated by the toluene swell index (TSI). The machine learning protocols used to classify the 3 groups of TSI were specified as less than 80% TSI, 80-88% TSI, and more than 88% TSI. The 80-88% TSI group was the group in which the compounded latex was suitable for medical glove production, which made the glove specification comply with the requirements of customers as indicated by the tensile test. The results show that when comparing the algorithms used for modeling, the linear discriminant analysis (LDA) developed by 2nd derivative spectra with 15 k-best selected wavelengths fairly accurately predicted the class but was most reliable among other algorithms, i.e., artificial neural networks (ANN), support vector machines (SVM), and k-nearest neighbors (kNN), due to higher prediction accuracy, precision, recall, and F1-score of the same value of 0.76 and no overfitting or underfitting prediction. This developed model can be implemented in the glove factory for screening purposes in the production line. However, deep learning modeling should be explored with a larger sample number required for better model performance. Overfitting ; Artificial intelligence ; Neoprene ; Computer science ; Support vector machine ; Linear discriminant analysis ; Near-infrared spectroscopy ; Natural rubber ; Pattern recognition (psychology) ; Materials science ; Machine learning ; Artificial neural network ; Physics ; Composite material ; Optics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Statistical Methods and Models
Authors: Jessica Juweriah Ibrahim | Hiba H. Ibraheem | Heba A. Hussein | B Mahdi | M Abbass | M Mohsin | W Al-Azzawi | M Hanoon | M Alkaabi | L Shaker | A Al-Amiery | W Isahak | A Kadhum | M Takriff | Q Jawad | D Zinad | R Salim | T Gaaz | S Al-Baghdadi | F Noori | W Ahmed | A Resen | S Junaedi | A Kadihum | A Mohamad | A Alamiery | H Aljibori | H Al-Asadi | F Hashim | A Salam | T Abed | K Reda | A Al Adily | F Sayyid | R Al-Azzawi | M Kzar | H Jawoosh | A Mustafa | N Betti | Y Abdulsahib | A Eltmimi | S Alhabeeb | T Allami | A Khudhair | T Gazz | W Alani | A Kadhim
Source: International Journal of Corrosion and Scale Inhibition (article, 2024-03-30)
Field: Materials Science — Materials Chemistry

Experimental and theoretical studies on the corrosion inhibition potentials of N′-((1H-indole-3-yl)methylene)benzohydrazide for mild steel in HCl In this study, we introduce a novel corrosion inhibitor, N-((1H-indole-3-yl)methylene)benzohydrazide (IMBH) synthesized from indole-3-carboxyaldehyde. The inhibitory properties of IMBH and the starting material were investigated for mild steel in hydrochloric acid (HCl) solutions of varying concentrations (0.2 M, 0.4 M, and 0.6 M). The evaluation of corrosion inhibition was performed using a combination of scanning electron microscopy (SEM), energy dispersion X-ray analysis (EDX) techniques, and electrochemical measurements. Our findings demonstrate a clear correlation between the concentration of the inhibitor and its inhibition effectiveness. As the concentration of IMBH rises, the corrosion of mild steel is more effectively controlled. The inhibition mechanism mainly operates through a charge transfer process, where the formation of protective adsorption layers on the steel surface plays a significant role in mitigating corrosion. Furthermore, we employed quantum chemical calculations to gain insights into the inhibitory process, offering valuable theoretical support for our experimental observations. This investigation sheds light on the promising corrosion inhibition potential of IMBH and its feasibility as a protective agent for mild steel in acidic environments. The understanding of its inhibitory mechanism, facilitated by quantum chemical analysis, provides essential knowledge for the development of effective corrosion prevention strategies in various industrial applications. Methylene ; Corrosion ; Indole test ; Methylene blue ; Chemistry ; Medicinal chemistry ; Nuclear chemistry ; Computational chemistry ; Inorganic chemistry ; Materials science ; Organic chemistry ; Catalysis Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Diego Sibilia | Martina Amendolea | Roberta Sangiovanni | Mario Bragaglia | Fabrizio Nicoletti | Pierfrancesco Filetici | Antonio D’Addona | Francesca Nanni | Leonardo Dassatti | Giuseppina Nocca
Source: BioMed Research International (article, 2024-01-01)
Field: Materials Science — Biomaterials

Biodegradation Study of Biomaterials Composed of Acrylated Epoxidized Soybean Oil: An In Vitro Study In the field of regenerative medicine, acrylated epoxidized vegetable oils are emerging as a promising avenue of exploration. The aim of this study is to evaluate the degradability of two formulations of acrylated epoxidized soybean oil (AESO): pure AESO and AESO diluted with soybean oil (SO) for potential bioprintability applications. The comprehensive investigation of these two polymeric formulations included optimization of polymerization conditions, confirmation of cytocompatibility, and, most importantly, the study of their degradability. The results reveal that AESO, used as a biomaterial for biomedical applications, undergoes a distinctive degradation process, combining both enzymatic and oxidative degradation (AESO/SO samples lose 29.45% of their weight after 60 days). This phenomenon is the result of a complex interplay of factors, including the chemical composition and physical characteristics of the polymer, the unique tissue environment in which it is implanted, and the duration of implantation. Epoxidized soybean oil ; Biomaterial ; Soybean oil ; Polymerization ; Biodegradation ; Polymer ; Chemistry ; Materials science ; Organic chemistry ; Food science biodegradable polymer synthesis and properties ; Electrospun Nanofibers in Biomedical Applications ; Polymer composites and self-healing
Authors: Oshin Kawduji Thool | Abhilash Sasidharan | Bindu M. Krishna | Sarasan Sabu | Muhammed Navaf | Kappat Valiyapeediyekkal Sunooj
Source: Sustainable Food Technology (article, 2024-11-18)
Field: Materials Science — Biomaterials

Nano-hydroxyapatite (n-HAP) from <i>Pangasius</i> bone side streams and its application as a reinforcing agent in biodegradable food packaging films The bone side streams from catfish ( Pangasianodon hypophthalmus ) were used to produce nano-hydroxyapatite (n-HAP) by a calcination method. Food packaging ; Nano- ; Materials science ; Pangasius ; Nanotechnology ; Composite material ; Food science ; Chemistry ; Fish <Actinopterygii> ; Fishery biodegradable polymer synthesis and properties ; Bone Tissue Engineering Materials ; Nanocomposite Films for Food Packaging
Authors: L. Susanne Huth | Terry L. Threlfall | David S. Hughes
Source: Crystal Growth & Design (article, 2024-03-04)
Field: Chemistry — Physical and Theoretical Chemistry

Aspects of Isostructurality and Polymorphism in a Diverse Group of Monosubstituted Acetanilides The dependence of crystal structure assembly on molecular changes is studied for a series of monosubstituted acetanilide compounds. A library of 81 crystal structures is obtained from the Cambridge Structural Database, structural similarities are determined, and energetic preferences are assessed. Two dominant amide–amide hydrogen bond geometries and a molecular stack are frequently observed especially in the para-acetanilide subgroup. In all cases, the amide functional group has a greater effect on the chain geometry and resultant crystal packing than the phenyl substituent. The study concludes with a short review of the polymorphism of paracetamol and suggests an alternative concept for overcoming the compaction issues currently encountered in the manufacture of the oral dosage form. Acetanilide ; Group (periodic table) ; Polymorphism (computer science) ; Chemistry ; Stereochemistry ; Organic chemistry ; Genotype ; Biochemistry ; Gene Crystallography and molecular interactions ; Crystal structures of chemical compounds ; Organometallic Compounds Synthesis and Characterization
Authors: A. Lebreton | Jérémy Barbé | Christophe Lethien | Jonathan N. Coleman | Thierry Brousse
Source: Journal of The Electrochemical Society (article, 2024-08-30)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Tuning Deposition Conditions for VN Thin Films Electrodes for Microsupercapacitors: Influence of the Thickness Vanadium nitride is a highly promising material for micro-pseudocapacitors when used as a bifunctional thin film, i.e. an electrode material and a current collector, owing to its remarkable electrical and electrochemical properties. However, the specific limitations associated with high-rate cycling remain unclear. In this study, we evaluate how the characteristic time associated with charge/discharge of vanadium nitride films is modified with the film thicknesses using electrochemical impedance spectroscopy and cyclic voltammetry measurements coupled to a semi-empirical model commonly utilized to assess the high-rate behaviour of battery electrodes. Both methodologies are in good agreement and revealed that rate capability of this bi-functional material is limited by the VN electrical conductivity. To confirm this finding, VN thin films were sputtered on platinum current collectors, leading to a six-fold reduction in the characteristic time associated with charge/discharge of the current collectors/electrode material. This underscores the importance of using current collectors even for highly conductive electrode materials. Materials science ; Electrode ; Deposition (geology) ; Thin film ; Composite material ; Optoelectronics ; Nanotechnology ; Chemistry ; Geology Supercapacitor Materials and Fabrication ; Electrodeposition and Electroless Coatings ; Electrocatalysts for Energy Conversion
Authors: Omer A. Alawi | Haslinda Mohamed Kamar | Mustafa Mudhafar Shawkat | Mohammed M. Al Ani | Hussein A. Mohammed | Raad Z. Homod | Mazlan Abdul Wahid
Source: International Journal of Hydromechatronics (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Artificial intelligence-based viscosity prediction of polyalphaolefin-boron nitride nanofluids Predicting viscosity's nanofluids can benefit all domains, including energy, thermofluids, power systems, energy storage, materials, cooling, heating, and lubrication.The objective of this study to predict the dynamic viscosity of polyalphaolefin-hexagonal boron nitride (PAO/hBN) nanofluids using four main parameters: shear rate, shear stress, nanomaterials mass fraction, and temperature.Moreover, three hybrid ensemble learning models (Bayesian ridge-random forest, Bayesian ridge-MLP regressor and Bayesian ridge-AdaBoost regressor) were developed for the current task.The forward sequential feature selector (FSFS) created four input combinations (models).Model 4 showed the best prediction accuracy, followed by models 2, 3 and 1.The computational findings showed that ensemble learner 1 was slightly outperformed by ensemble learner 3.Meanwhile, among the predictive models, ensemble learner 2 consistently placed third.Besides, the research results demonstrated that creating predictive models based on all input parameters can produce a precise prediction matrix.Overall, the study recommended exciting conclusions on predicting a nanolubricant's viscosity for use in heat transfer applicants. Nanofluid ; Boron nitride ; Viscosity ; Materials science ; Boron ; Metallurgy ; Nanotechnology ; Composite material ; Chemistry ; Nanoparticle ; Organic chemistry Diamond and Carbon-based Materials Research ; Fuel Cells and Related Materials ; Drilling and Well Engineering
Authors: Elena R. Remesal | Víctor Posligua | Miguel Mahillo-Paniagua | Konstantin Glazyrin | Javier Fdez. Sanz | Antonio M. Márquez | José J. Plata
Source: Journal of Materials Chemistry A (article, 2024-11-11)
Field: Materials Science — Materials Chemistry

Enhancing the thermoelectric figure of merit of BiN <i>via</i> polymorphism, pressure, and nanostructuring BiN is a promising candidate as thermoelectric material, but many variables need to be optimized. In this work, we maximize its zT based on pressure, temperature, carrier concentration, grain size and polymorphism. Bin ; Figure of merit ; Thermoelectric effect ; Polymorphism (computer science) ; Materials science ; Nanotechnology ; Physics ; Optoelectronics ; Engineering ; Mechanical engineering ; Thermodynamics ; Genetics ; Genotype ; Biology Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; MXene and MAX Phase Materials