jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Dede Rustandi | Sony Hartono Wijaya | Mushthofa Mushthofa | Ratih Damayanti
Source: Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) (article, 2024-02-07)
Field: Chemistry — Organic Chemistry

Anatomy Identification of Bamboo Stems with The Convolutional Neural Networks (CNN) Method It is important to note that some species of bamboo are protected and considered endangered. However, distinguishing between traded and protected bamboo species or differentiating between bamboo species for various purposes remains a challenge. This requires specialized skills to identify the type of bamboo, and currently, the process can only be carried out in the forest for bamboo that is still in clump form by experienced researchers or officers. However, a study has been conducted to develop an easier and faster method of identifying bamboo species. The study aims to create an automatic identification system for bamboo stems based on their anatomical structure (ASINABU). The bamboo identification algorithm was developed using macroscopic images of cross-sectioned bamboo stems and the research method used was the convolutional neural network (CNN). CNN was designed to identify bamboo species with images taken using a cellphone camera equipped with a lens. The final product is an Android automatic identification application that can detect bamboo species with an accuracy of 99.9%. Bamboo ; Convolutional neural network ; Identification (biology) ; Computer science ; Anatomy ; Artificial intelligence ; Biology ; Botany Wood and Agarwood Research ; Bamboo properties and applications ; Computer Science and Engineering
Authors: Lukas Zell | Thomas S. Hofer | Mario Schubert | Alexander Popoff | Anna Höll | Moritz Marschhofer | Petra Huber‐Cantonati | Veronika Temml | Daniela Schuster
Source: Biochemical Pharmacology (article, 2024-06-05)
Field: Chemistry — Spectroscopy

Impact of 2-hydroxypropyl-β-cyclodextrin inclusion complex formation on dopamine receptor-ligand interaction – A case study The octanol-water distribution coefficient (logP), used as a measure of lipophilicity, plays a major role in the drug design and discovery processes. While average logP values remain unchanged in approved oral drugs since 1983, current medicinal chemistry trends towards increasingly lipophilic compounds that require adapted analytical workflows and drug delivery systems. Solubility enhancers like cyclodextrins (CDs), especially 2-hydroxypropyl-β-CD (2-HP-β-CD), have been studied in vitro and in vivo investigating their ADMET (adsorption, distribution, metabolism, excretion and toxicity)-related properties. However, data is scarce regarding the applicability of CD inclusion complexes (ICs) in vitro compared to pure compounds. In this study, dopamine receptor (DR) ligands were used as a case study, utilizing a combined in silico/in vitro workflow. Media-dependent solubility and IC stoichiometry were investigated using HPLC. NMR was used to observe IC formation-caused chemical shift deviations while in silico approaches utilizing basin hopping global minimization were used to propose putative IC binding modes. A cell-based in vitro homogeneous time-resolved fluorescence (HTRF) assay was used to quantify ligand binding affinity at the DR subtype 2 (D<sub>2</sub>R). While all ligands showed increased solubility using 2-HP-β-CD, they differed regarding IC stoichiometry and receptor binding affinity. This case study shows that IC-formation was ligand-dependent and sometimes altering in vitro binding. Therefore, IC complex formation can't be recommended as a general means of improving compound solubility for in vitro studies as they may alter ligand binding. Chemistry ; Ligand (biochemistry) ; Lipophilicity ; Solubility ; In silico ; In vitro ; Combinatorial chemistry ; IVIVC ; In vivo ; Ligand efficiency ; Stereochemistry ; Receptor ; Biochemistry ; Organic chemistry Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods ; Drug Solubulity and Delivery Systems
Source: Biointerface Research in Applied Chemistry (article, 2024-10-15)
Field: Chemistry — Organic Chemistry

Colorimetric Screen-Printed Label Using Low Molecular Weight Chitosan Grafted with Rosolic Acid for pH and Ammonia Gas Sensing Colorimetric sensing technologies are valued for their simplicity and adaptability, yet their large-scale production remains economically challenging. This study presents a cost-effective colorimetric pH sensor developed from low molecular weight chitosan (LC) grafted with rosolic acid (LCRA), engineered as a pH-sensitive colorant for screen-printing inks. LCRA was synthesized via a Mannich reaction and characterized using 1H NMR, FT-IR, and UV-Vis spectroscopy. LCRA showed reduced crystallinity and thermal stability alongside notable improvements in water solubility compared to its LC precursor. The LCRA ink displayed compatibility with various substrates, including polypropylene spun bond, filter paper, and cotton, applying easily via screen printing without any dye leaching. Notably, it exhibited a responsive color change from orange-yellow to pink-red in response to pH adjustments between 4.0 and 12.0 and upon exposure to ammonia gas. These findings position the LCRA label as a versatile and efficient solution for visual pH detection across various applications. Chitosan ; Ammonia ; Chemistry ; Ammonia gas ; Combinatorial chemistry ; Chromatography ; Organic chemistry Polydiacetylene-based materials and applications ; Advanced Chemical Sensor Technologies ; Analytical Chemistry and Sensors
Authors: Alexander Sikorski | Robert Julian Rabben | Surahit Chewle | Marcus Weber
Source: arXiv (Cornell University) (preprint, 2024-04-16)
Field: Chemistry — Physical and Theoretical Chemistry

Capturing the Macroscopic Behaviour of Molecular Dynamics with Membership Functions Markov processes serve as foundational models in many scientific disciplines, such as molecular dynamics, and their simulation forms a common basis for analysis. While simulations produce useful trajectories, obtaining macroscopic information directly from microstate data presents significant challenges. This paper addresses this gap by introducing the concept of membership functions being the macrostates themselves. We derive equations for the holding times of these macrostates and demonstrate their consistency with the classical definition. Furthermore, we discuss the application of the ISOKANN method for learning these quantities from simulation data. In addition, we present a novel method for extracting transition paths from simulations based on the ISOKANN results and demonstrate its efficacy by applying it to simulations of the μ-opioid receptor. With this approach we provide a new perspective on the analysis of macroscopic behaviour of Markov systems. Dynamics (music) ; Statistical physics ; Molecular dynamics ; Economic geography ; Computer science ; Physics ; Economics ; Quantum mechanics Various Chemistry Research Topics ; History and advancements in chemistry ; Molecular spectroscopy and chirality
Authors: B.C. do Amaral | Andrew R. M. Michael | Nicholas I. Brodie | D. Alex Crowder | Kristen H. Eiriksson | David C. Schriemer
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-12-23)
Field: Chemistry — Organic Chemistry

Click-linking: a cell-compatible protein crosslinking method based on click chemistry Abstract Crosslinking mass spectrometry (XL-MS) has the potential to map the human interactome at high resolution and with high fidelity, replacing indirect, error prone sampling methods such as affinity pulldown MS. However, the sampling depth of XL-MS remains stubbornly low. We present a novel crosslinking strategy that splits the crosslinking reaction into two sequential and orthogonal coupling events. The method involves pre-stabilizing the spatial proteome with a fixation protocol inspired by immunofluorescence imaging, followed by a stepwise process that begins with extensively labeling surface-accessible lysines in the cell with N-hydroxysuccinimide (NHS)-modified click reagents. We show that a subsequent copper-catalyzed azide-alkyne cycloaddition (CuAAC) reaction of the installed precursors generates crosslinks at levels approaching 30% of the total signal, as demonstrated by a subtractive approach. The method generates no detectable side reactions or obvious distortions of the spatial proteome. Protein-protein interactions (PPIs) are detected at levels approximately 20 times higher than a conventional DSS-based method, outperforming even enrichable crosslinkers. Click chemistry ; Proteome ; Chemistry ; Interactome ; Cycloaddition ; Combinatorial chemistry ; Mass spectrometry ; Biophysics ; Biochemistry ; Chromatography ; Catalysis ; Biology Click Chemistry and Applications ; Biotin and Related Studies ; Advanced Biosensing Techniques and Applications
Authors: Gunji Bala Murali | Ramakrishna Doodi | Mathews Koshy
Source:  (article, 2024-07-12)
Field: Materials Science — Biomaterials

Performance Evaluation of Bio-Inspired Pattern-Based Tensile Specimens for Lightweight Electric Vehicle Battery Applications Currently, researchers are seeking to pay more attention to lightweight designs to minimize the materials used and greatly improve the strength of designs to increase their loading capacity in engineering applications. Bio-inspired patterns are given more attention in the design of these lightweight structures. In this current study, tensile specimens are fabricated as per the ASTM D638-IV standard and through the VAT photopolymerization technique with different bio-inspired patterns like honeycomb, re-entrant, and spider web structures, with unit cell sizes of 4 mm and 6 mm, respectively. Tensile tests are conducted on all the specimens, and all the test results are compared and evaluated to determine the one with the best performance. It was concluded that the 4 mm unit cell size specimens performed well, and spider web structures were shown to have especially satisfactory performance. Battery (electricity) ; Materials science ; Electric vehicle ; Ultimate tensile strength ; Automotive engineering ; Computer science ; Composite material ; Engineering ; Power (physics) Electrospun Nanofibers in Biomedical Applications ; Advanced Materials and Mechanics ; Modular Robots and Swarm Intelligence
Authors: Natalija Ž. Nedić | Tamara Tadić | Bojana Marković | Aleksandra B. Nastasović | Aleksandar Popović | Sandra Bulatović
Source: Separations (article, 2024-10-28)
Field: Chemistry — Analytical Chemistry

Eco-Friendly Green Approach to the Biosorption of Hazardous Dyes from Aqueous Solution on Ragweed (Ambrosia artemisiifolia) Biomass The presented research includes the preparation, characterization, and implementation of magnetic biosorbent (Fe3O4/RWB), obtained from ragweed (Ambrosia artemisiifolia) biomass. Fe3O4/RWB was examined for the removal of a hazardous dye, malachite green (MG), from an aqueous solution in a batch system. The effects of the experimental parameters—initial dye concentration (10–300 mg/L), contact time (0–120 min), biosorbent dose (1–5 g/L), initial pH (2–10), ionic strength (0–1 mol/L), and temperature (298–318 K) on dye biosorption—were studied. The results showed that increases in biosorbent dose, contact time, and initial pH led to an increase in biosorption efficiency, while the increase in initial dye concentration, the ionic strength, and temperature had the opposite effect. The biosorption kinetics for MG on Fe3O4/RWB were analyzed with pseudo-first-order, pseudo-second-order, and Elovich kinetic models, while the Langmuir, Freundlich and Temkin isotherm models were used for equilibrium data analysis. It was observed that the MG biosorption followed the pseudo-second-order kinetic model, whereas the Langmuir model was the best fit for the equilibrium biosorption data of MG, with a Qmax of 34.1 mg/g. the desorption of MG from Fe3O4/RWB indicated reusability in five adsorption/desorption cycles, good performance, and potential in practical applications. Ragweed ; Ambrosia artemisiifolia ; Biosorption ; Biomass (ecology) ; Ambrosia ; Hazardous waste ; Biochar ; Environmentally friendly ; Environmental science ; Aqueous solution ; Chemistry ; Agronomy ; Waste management ; Botany ; Ecology ; Biology ; Adsorption ; Engineering ; Pyrolysis ; Sorption Dye analysis and toxicity ; Water Quality Monitoring and Analysis ; Adsorption and biosorption for pollutant removal
Authors: Wellington Bruno Silva de Jesus | Vinícius Lima da Silva | Héricles Ruiliman Oliveira de Souza | Thiago A. Costa | Otávio L. Rocha | Luiz Gabriel da Silva Nascimento Nascimento | Maria Adrina Paixão de Souza da Silva
Source: Trends in Sciences (article, 2024-07-19)
Field: Materials Science — General Materials Science

Thermal and Microstructural Characterization of the Multicomponent Alloy Al33wt%Cu1wt%Ni-1.2wt%Ta Solidified with Transient Heat Flow The demand for materials with specific properties continues to grow in modern industry, necessitating a deeper understanding of metal solidification processes. This study investigates the thermal and microstructural characteristics of a novel multicomponent alloy, Al33wt%Cu1wt%Ni-1.2wt%Ta, which has not been previously reported in the literature. This alloy, comprising aluminum, copper, nickel and tantalum, exhibits superior mechanical strength, thermal stability and corrosion resistance compared to conventional alloys, making it suitable for various applications. Utilizing transient heat flow techniques, thermal characterization and microstructural analysis were performed on the alloy after solidification. Thermal mapping revealed variable growth and cooling rates along the ingot, influencing macrostructural transitions from columnar to equiaxed grains. Microstructural examination uncovered a complex evolution, with refined dendritic spacings initially, followed by the formation of intermetallic phases such as Al3Ta, α-Al and Al2Cu. The study also proposed a hypothesis on the formation of diamond-shaped intermetallics like Ni3Ta and Ta(Cu,Al)2, which were consumed to form Al7Cu4Ni at the edges. Secondary dendritic spacing analysis supported this hypothesis, showing correlation with growth laws. The findings provide valuable insights into solidification behavior and microstructural evolution, aiding in parameter optimization and enhancing the alloy’s properties for specific applications. However, limitations include the need for further research to explore mechanical and thermal properties and validate industrial potential. Transient (computer programming) ; Materials science ; Alloy ; Characterization (materials science) ; Heat flow ; Metallurgy ; Thermal ; Flow (mathematics) ; Mechanics ; Thermodynamics ; Computer science ; Physics ; Nanotechnology Metallurgical and Alloy Processes ; Material Properties and Applications ; Solidification and crystal growth phenomena
Authors: Parth U. Kelkar | Kendra A. Erk | Seth Lindberg
Source: Review of Scientific Instruments (article, 2024-03-01)
Field: Chemistry — Organic Chemistry

Dynamic diffusive interfacial transport (D-DIT): A novel quantitative swelling technique for developing binary phase diagrams of aqueous surfactant systems Current methods to develop surfactant phase diagrams are time-intensive and fail to capture the kinetics of phase evolution. Here, the design and performance of a quantitative swelling technique to study the dynamic phase behavior of surfactants are described. The instrument combines cross-polarized optical and short-wave infrared imaging to enable high-resolution, high-throughput, and in situ identification of phases and water compositions. Data across the entire composition spectrum for the dynamics and phase evolution of a binary aqueous non-ionic surfactant solution at two isotherms are presented. This instrument provides pathways to develop non-equilibrium phase diagrams of surfactant systems-critical to predicting the outcomes of formulation and processing. It can be applied to study time-dependent material relationships across a diverse range of materials and processes, including the dissolution of surfactant droplets and the drying of aqueous polymer films. Pulmonary surfactant ; Phase diagram ; Dissolution ; Aqueous solution ; Materials science ; Swelling ; Phase (matter) ; Ionic bonding ; Surface tension ; Kinetics ; Chemical engineering ; Thermodynamics ; Chemistry ; Physical chemistry ; Ion ; Organic chemistry ; Composite material ; Physics Surfactants and Colloidal Systems ; Pickering emulsions and particle stabilization ; Advanced Polymer Synthesis and Characterization
Authors: Xiaowei Qi | YuLong Xie | Jingyang Niu | Junwei Zhao | Ya‐Min Li | Wei‐Hui Fang | Jian Zhang
Source: Angewandte Chemie (article, 2024-10-24)
Field: Materials Science — Materials Chemistry

Application of Hard and Soft Acid‐base Theory to Construct Heterometallic Materials with Metal‐oxo Clusters Abstract Heterometallic cluster‐based materials offer the potential to incorporate multiple functionalities, leveraging the aggregation effects of clusters and translating this heterogeneity and complexity into unexpected properties that are more than just the sum of their components. However, the rational construction of heterometallic cluster‐based materials remains challenging due to the complexity of metal cation coordination and structural unpredictability. This minireview provides insights into a general synthetic strategy based on Hard and Soft Acids and Bases (HSAB) theory, summarizing its advantages in the designed synthesis of discrete heterometallic clusters (intracluster assembly) and infinite heterometallic cluster‐based materials (intercluster assembly). Furthermore, it emphasizes the potential to exploit the intrinsic properties of mixed components to achieve breakthroughs across a broad range of applications. The insights from this review are expected to drive the progress of heterometallic cluster‐based materials in a controllable and predictable manner. HSAB theory ; Cluster (spacecraft) ; Nanotechnology ; Construct (python library) ; Computer science ; Materials science ; Chemistry ; Computational chemistry Nanocluster Synthesis and Applications ; Polyoxometalates: Synthesis and Applications ; Inorganic Chemistry and Materials
Authors: Hilal Erdoğan
Source: Türk doğa ve fen dergisi :/Türk doğa ve fen dergisi (article, 2024-09-25)
Field: Chemistry — Analytical Chemistry

Detection of Bean Rust (Uromyces appendiculatus) Disease Under Field Conditions Using Thermal Imaging Among the factors causing yield losses in agricultural fields, plant diseases are known to be one of the most significant. For many years, pesticides have been used to combat these diseases. However, due to the unintended toxic effects of pesticides on non-target organisms in recent years, there have been restrictions on their usage. Therefore, there has been an increased interest in alternative methods to chemical control in combating plant diseases. Among these alternative methods, thermal imaging, widely used within the scope of precision agriculture practices, holds a significant position. This study aims to detect bean rust disease (Agent: Uromyces appendiculatus) at an early stage using thermal imaging methods. According to the obtained results, it has been determined that leaves infected with the pathogen have a temperature approximately 2 ºC lower than healthy leaves. Surface temperatures of healthy and infected leaves were measured at 60-minute intervals for three weeks. Throughout this three-week period, it was observed that the average daily temperatures of infected leaves and healthy leaves were below ambient temperatures. Thermal imaging is considered to play a crucial role in the potential early detection of plant diseases. Rust (programming language) ; Pesticide ; Biology ; Horticulture ; Agriculture ; Toxicology ; Agronomy ; Ecology Spectroscopy and Chemometric Analyses ; Seed and Plant Biochemistry ; Genetics and Plant Breeding
Authors: Adam M. Krajewski | Arindam Debnath | Wesley F. Reinhart | Allison M. Beese | Zi‐Kui Liu
Source: The Journal of Open Source Software (preprint, 2024-11-14)
Field: Materials Science — Materials Chemistry

nimCSO: A Nim package for Compositional SpaceOptimization nimCSO is a high-performance tool implementing several methods for selecting components (data dimensions) in compositional datasets, which optimize the data availability and density for applications such as machine learning.Making said choice is a combinatorically hard problem for complex compositions existing in high-dimensional spaces due to the interdependency of components being present.Such spaces are encountered across many scientific disciplines (see Statement of Need), including materials science, where datasets on Compositionally Complex Materials (CCMs) often span 20-45 chemical elements, 5-10 processing types, and several temperature regimes, for up to 60 total data dimensions.This challenge also exists in everyday contexts, such as study of cooking ingredients (Ahn et al., 2011), which interact in various recipes, giving rise to questions like "Given 100 spices at the supermarket, which 20, 30, or 40 should I stock in my pantry to maximize the number of unique dishes I can spice according to recipe?".Critically, this is not as simple as frequency-based selection because, e.g., removing less common nutmeg and cinnamon from your shopping list will prevent many recipes with the frequent vanilla, but won't affect those using black pepper (Edmisten, 2022). Computer science ; Python (programming language) ; Heuristics ; Data structure ; Cache ; Programming language ; Parallel computing ; Operating system Machine Learning in Materials Science ; Geochemistry and Geologic Mapping ; Scientific Computing and Data Management
Authors: Zoiţa Berinde | Claudia Butean
Source: Carpathian Journal of Mathematics (article, 2024-04-17)
Field: Chemistry — Organic Chemistry

Robust mathematical models of structure and quantitative structure-property relationship studiesof alkyl halides A QSPR (Q uantitative S tructure- Property R elationship) model links mathematically various physicochemical properties with the structure of a molecule. Establishing such quantitative relationships is of great technological importance as in this way one can predict the properties of new untested molecules by means of a linear or nonlinear equation that expresses a certain property as an explicit function of one or more independent variables. These functional relationships (usually called QSPR models) are obtained by performing specific QSPR studies on a class of similar compounds whose properties have been already determined and were appropriately correlated to their molecular structures. In this study we are interested to determine the best QSPR models for the normal boiling point ($bp$) and molar refraction ($mr$) of 64 alkyl halides by using the topological index $ZEP$, the structural parameter ($H_d$) and the number of carbon atoms ($N$) as predictor variables. In order to validate the developed models with respect to the goodness-of-fit, robustness and predictive ability for the two properties considered in the class of alkyl halides, external validation, cross-validation (leave-one-out) and randomization ($y$-randomization) were performed. The obtained results have shown that the three descriptors above could be efficiently used for modelling and predicting the normal boiling points and molar refractions of the considered class of compounds. Halide ; Alkyl ; Property (philosophy) ; Chemistry ; Organic chemistry ; Philosophy ; Epistemology Chemical Thermodynamics and Molecular Structure ; Synthesis and properties of polymers ; Inorganic and Organometallic Chemistry