jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Le Yu | Mark A. B. Kreutzberger | Maria C. Hancu | Thi H. Bui | Adam C. Farsheed | Edward H. Egelman | Jeffrey D. Hartgerink
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-05-15)
Field: Materials Science — Biomaterials

Beyond the Triple Helix: Exploration of the Hierarchical Assembly Space of Collagen-like Peptides The <i>de novo</i> design of self-assembling peptides has garnered significant attention in scientific research. While alpha-helical assemblies have been extensively studied, exploration of polyproline type II (PPII) helices, such as those found in collagen, remains relatively limited. In this study, we focused on understanding the sequence-structure relationship in hierarchical assemblies of collagen-like peptides, using defense collagen SP-A as a model. By dissecting the sequence derived from SP-A and synthesizing short collagen-like peptides, we successfully constructed a discrete bundle of hollow triple helices. Mutation studies pinpointed amino acid sequences, including hydrophobic and charged residues that are critical for oligomer formation. These insights guided the <i>de novo</i> design of collagen-like peptides, resulting in the formation of diverse quaternary structures, including discrete and heterogenous bundled oligomers, 2D nanosheets, and pH-responsive nanoribbons. Our study represents a significant advancement in the understanding and harnessing of collagen higher-order assemblies beyond the triple helix. Triple helix ; Collagen helix ; Helix (gastropod) ; Space (punctuation) ; Chemistry ; Computational biology ; Stereochemistry ; Biology ; Computer science ; Paleontology Collagen: Extraction and Characterization ; Chemical Synthesis and Analysis ; Advanced Proteomics Techniques and Applications
Authors: Qiaohong Wang | Valay Agarawal | Matthew R. Hermes | Mário Motta | Julia E. Rice | Gavin O. Jones | Laura Gagliardi
Source: The Journal of Chemical Physics (article, 2024-07-01)
Field: Chemistry — Organic Chemistry

Distinguishing homolytic vs heterolytic bond dissociation of phenylsulfonium cations with localized active space methods Modeling chemical reactions with quantum chemical methods is challenging when the electronic structure varies significantly throughout the reaction and when electronic excited states are involved. Multireference methods, such as complete active space self-consistent field (CASSCF), can handle these multiconfigurational situations. However, even if the size of the needed active space is affordable, in many cases, the active space does not change consistently from reactant to product, causing discontinuities in the potential energy surface. The localized active space SCF (LASSCF) is a cheaper alternative to CASSCF for strongly correlated systems with weakly correlated fragments. The method is used for the first time to study a chemical reaction, namely the bond dissociation of a mono-, di-, and triphenylsulfonium cation. LASSCF calculations generate smooth potential energy scans more easily than the corresponding, more computationally expensive CASSCF calculations while predicting similar bond dissociation energies. Our calculations suggest a homolytic bond cleavage for di- and triphenylsulfonium and a heterolytic pathway for monophenylsulfonium. Heterolysis ; Homolysis ; Complete active space ; Chemistry ; Dissociation (chemistry) ; Bond-dissociation energy ; Bond cleavage ; Excited state ; Computational chemistry ; Quantum chemical ; Electronic structure ; Potential energy ; Chemical bond ; Atomic physics ; Chemical space ; Chemical physics ; Physical chemistry ; Physics ; Density functional theory ; Radical ; Molecule ; Basis set Radical Photochemical Reactions ; Advanced Chemical Physics Studies ; Ammonia Synthesis and Nitrogen Reduction
Authors: Hayder R. Fadhil | Ayad M.R. Raauf | Monther Faisal Mahdi
Source: Pharmacia (article, 2024-09-11)
Field: Chemistry — Organic Chemistry

Synthesis, characterization, preliminary molecular docking, pharmacological activity, and ADME studies of some new pyrazoline derivatives as anti-breast cancer agents This work studied the anti-breast cancer activities of pyrazoline-containing benzenesulfonamides (6–10) in vitro and silico. The GOLD suite performed molecular docking on the target human estrogen receptor and the PARPA1 antagonist crystal structure, yielding comparable results using tamoxifen as a reference drug. Researchers tested these compounds (6–10) on two types of breast cancer cells (MCF-7 and MDA-MB-468) in the lab and found an antiproliferative activity that depended on the dose. Compounds 9 demonstrated a high docking score on PARP1 antagonist crystal structure in triple-negative breast cancer with a PLP fitness score of 93.24 and potential antiproliferative activity with an IC50 of 2.79 µM on the MDA-MB-468 cell line. In contrast, compounds 8 and 10 showed promising activity on the MCF-7 cancer cell line with IC50s of 7.4 and 17.96, respectively. ADME ; Docking (animal) ; Breast cancer ; Chemistry ; Pharmacology ; Pyrazoline ; Computational biology ; Cancer ; Combinatorial chemistry ; Medicine ; Biology ; Drug ; Internal medicine ; Organic chemistry Synthesis and biological activity ; Computational Drug Discovery Methods ; Multicomponent Synthesis of Heterocycles
Authors: Yuxi Wang | Xingxing Zhang | Wujuan Yan | Nianjie Liang | Haiyu He | Xinwei Tao | Ang Li | Fuwei Yang | Buxuan Li | Te‐Huan Liu | Jia Zhu | Wu Zhou | Wei Wang | Lin Zhou | Bai Song
Source: arXiv (Cornell University) (preprint, 2024-02-21)
Field: Materials Science — Materials Chemistry

Thermal transport in a 2D amorphous material Two-dimensional (2D) crystals proved revolutionary soon after graphene was discovered in 2004. However, 2D amorphous materials only became accessible in 2020 and remain largely unexplored. In particular, the thermophysical properties of amorphous materials are of great interest upon transition from 3D to 2D. Here, we probe thermal transport in 2D amorphous carbon. A cross-plane thermal conductivity ($κ$) down to 0.079 $\rm{Wm}^{-1}K^{-1}$ is measured for van der Waals stacked multilayers at room temperature, which is among the lowest reported to date. Meanwhile, an unexpectedly high in-plane $κ$ is obtained for freestanding monolayers which is a few times larger than what is predicted by conventional wisdom for 3D amorphous carbon with similar $\rm{sp}^{2}$ fraction. Our molecular dynamics simulations reveal the role of disorder and highlight the impact of dimensionality. Amorphous materials at the 2D limit open up new avenues for understanding and manipulating heat at the atomic scale. Amorphous solid ; Thermal ; Materials science ; Physics ; Chemistry ; Thermodynamics ; Crystallography Thermal properties of materials
Authors: R. Malairaja | Vijayan Venugopal
Source: Journal of New Materials for Electrochemical Systems (article, 2024-09-10)
Field: Materials Science — Polymers and Plastics

Optimization and Characterization of Borassus Fiber-Reinforced Epoxy Composites with Caesalpinia Bonducella Seed Shell Powder Using Response Surface Methodology Utilizing reinforced lignocellulosic fibers in polymer matrix composites (PMCs) is a highly effective approach, since it reduces the necessity for more often used synthetic fibers.To investigate the uses of particulate matrix composites (PMCs), we concentrated on studying the fibers from Borassus, which are underutilized and have not been well researched.In this study, we used particles of Caesalpinia bonducella seed shell powder (CBSSP) and Borassus fibers (BF) as a reinforcing agent.The research aimed to assess the effectiveness of different CBSSP and Borassus fibers (BF) through treatment with 5% NaOH.The use of alkali treatment to CBSSP and BF samples significantly improved the compatibility between the biomaterial's characteristics and the natural fillers in the epoxy-BF composites.This enhancement was noted in the findings of physicochemical, XRD, FTIR, thermal and morphological analysis.In response surface analysis, a first-degree polynomial model was employed to maximize tensile strength (TS), tensile modulus (TM), flexural strength (FS), impact energy (IE), and moisture absorption of reinforced fiber.The optimization was done by considering the composition and length of the fiber.The Response Surface methodology (RSM) numerical model was used to analyze the mentioned characteristics and develop an ideal Epoxy-BF composite with minimum moisture absorption, maximum tensile modulus, flexural strength, and impact energy.After analyzing the data, it was concluded that the most effective setup for the Epoxy-BF composite is to use a 4 mm fiber length reinforcement combined with a loading reinforcement of 25 wt % of biomaterials. Composite material ; Caesalpinia ; Epoxy ; Materials science ; Shell (structure) ; Fiber ; Characterization (materials science) ; Botany ; Biology ; Nanotechnology Natural Fiber Reinforced Composites
Authors: Moritz Quincke | Manuel Mundszinger | Johannes Biskupek | Ute Kaiser
Source: Nano Letters (article, 2024-07-01)
Field: Materials Science — Materials Chemistry

Defect Density and Atomic Defect Recognition in the Middle Layer of a Trilayer MoS<sub>2</sub> Stack Molybdenum disulfide (MoS<sub>2</sub>) is one of the most intriguing two-dimensional materials, and moreover, its single atomic defects can significantly alter the properties. These defects can be both imaged and engineered using spherical and chromatic aberration-corrected high-resolution transmission electron microscopy (C<sub>C</sub>/C<sub>S</sub>-corrected HRTEM). In a few-layer stack, several atoms are vertically aligned in one atomic column. Therefore, it is challenging to determine the positions of missing atoms and the damage cross-section, particularly in the not directly accessible middle layers. In this study, we introduce a technique for extracting subtle intensity differences in C<sub>C</sub>/C<sub>S</sub>-corrected HRTEM images. By exploiting the crystal structure of the material, our method discerns chalcogen vacancies even in the middle layer of trilayer MoS<sub>2</sub>. We found that in trilayer MoS<sub>2</sub> the middle layer's damage cross-section is about ten times lower than that in the monolayer. Our findings could be essential for the application of few-layer MoS<sub>2</sub> in nanodevices. Stack (abstract data type) ; Layer (electronics) ; Materials science ; Atomic layer deposition ; Nanotechnology ; Optoelectronics ; Engineering physics ; Physics ; Computer science 2D Materials and Applications ; Advanced Memory and Neural Computing ; Semiconductor materials and interfaces
Authors: Shubham Mondal | Md Mehedi Hasan Tanim | Garrett Baucom | Shaurya Dabas | Jinghan Gao | V. Gaddam | Jiangnan Liu | Aiden Ross | Longqing Chen | Honggyu Kim | Roozbeh Tabrizian | Zetian Mi
Source: arXiv (Cornell University) (preprint, 2024-08-28)
Field: Materials Science — Materials Chemistry

Unprecedented Enhancement of Piezoelectricity in Wurtzite Nitride Semiconductors via Thermal Annealing The incorporation of rare-earth elements in wurtzite nitride semiconductors, e.g., scandium alloyed aluminum nitride (ScAlN), promises dramatically enhanced piezoelectric responses, critical to a broad range of acoustic, electronic, photonic, and quantum devices and applications. Experimentally, however, the measured piezoelectric responses of nitride semiconductors are far below what theory has predicted. Here, we show that the use of a simple, scalable, post-growth thermal annealing process can dramatically boost the piezoelectric response of ScAlN thin films. We achieve a remarkable 3.5-fold increase in the piezoelectric modulus, d33 for 30% Sc content ScAlN, from 12.3 pC/N in the as-grown state to 45.5 pC/N, which is eight times larger than that of AlN. The enhancement in piezoelectricity has been unambiguously confirmed by three separate measurement techniques. Such a dramatic enhancement of d33 has been shown to impact the effective electromechanical coupling coefficient kt2 : increasing it from 13.8% to 76.2%, which matches the highest reported values in millimeter thick lithium niobate films but is achieved in a 100 nm ScAlN with a 10,000 fold reduction in thickness, thus promising extreme frequency scaling opportunities for bulk acoustic wave resonators for beyond 5G applications. By utilizing a range of material characterization techniques, we have elucidated the underlying mechanisms for the dramatically enhanced piezoelectric responses, including improved structural quality at the macroscopic scale, more homogeneous and ordered distribution of domain structures at the mesoscopic scale, and the reduction of lattice parameter ratio (c/a) for the wurtzite crystal structure at the atomic scale. Overall, the findings present a simple yet highly effective pathway that can be extended to other material families to further enhance their piezo responses. Wurtzite crystal structure ; Piezoelectricity ; Materials science ; Nitride ; Annealing (glass) ; Semiconductor ; Optoelectronics ; Engineering physics ; Condensed matter physics ; Nanotechnology ; Composite material ; Metallurgy ; Zinc ; Physics Thermal properties of materials ; Ferroelectric and Piezoelectric Materials ; Semiconductor materials and devices
Authors: Li-Dunn Chen | Michael A. Caprio | Devin M. Chen | Andrew J. Kouba | Carrie K. Kouba
Source: PLoS Computational Biology (article, 2024-02-14)
Field: Chemistry — Analytical Chemistry

Enhancing predictive performance for spectroscopic studies in wildlife science through a multi-model approach: A case study for species classification of live amphibians Near infrared spectroscopy coupled with predictive modeling is a growing field of study for addressing questions in wildlife science aimed at improving management strategies and conservation outcomes for managed and threatened fauna. To date, the majority of spectroscopic studies in wildlife and fisheries applied chemometrics and predictive modeling with a single-algorithm approach. By contrast, multi-model approaches are used routinely for analyzing spectroscopic datasets across many major industries (e.g., medicine, agriculture) to maximize predictive outcomes for real-world applications. In this study, we conducted a benchmark modeling exercise to compare the performance of several machine learning algorithms in a multi-class problem utilizing a multivariate spectroscopic dataset obtained from live animals. Spectra obtained from live individuals representing eleven amphibian species were classified according to taxonomic designation. Seven modeling techniques were applied to generate prediction models, which varied significantly (p < 0.05) with regard to mean classification accuracy (e.g., support vector machine: 95.8 ± 0.8% vs. K-nearest neighbors: 89.3 ± 1.0%). Through the use of a multi-algorithm approach, candidate algorithms can be identified and applied to more effectively model complex spectroscopic data collected for wildlife sciences. Other key considerations in the predictive modeling workflow that serve to optimize spectroscopic model performance (e.g., variable selection and cross-validation procedures) are also discussed. Benchmark (surveying) ; Wildlife ; Computer science ; Machine learning ; Support vector machine ; Artificial intelligence ; Random forest ; Predictive modelling ; Field (mathematics) ; Workflow ; Multivariate statistics ; Feature selection ; Data mining ; Ecology ; Mathematics ; Geography ; Biology ; Cartography Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Identification and Quantification in Food
Authors: Eric D. Boittier | Kai Töpfer | M. Devereux | Markus Meuwly
Source: arXiv (Cornell University) (preprint, 2024-06-01)
Field: Materials Science — Materials Chemistry

Kernel-based Minimal Distributed Charges: A Conformationally Dependent ESP-Model for Molecular Simulations A kernel-based method (kernelized minimal distributed charge model - kMDCM) to represent the molecular electrostatic potential (ESP) in terms of off-center point charges whose positions adapts to the molecular geometry. Using Gaussian kernels and atom-atom distances as the features, the ESP for water and methanol is shown to improve by at least a factor of two compared with point charge models fit to an ensemble of structures. Combining kMDCM for the electrostatics and reproducing kernels for the bonded terms allows energy-conserving simulation of 2000 water molecules with periodic boundary conditions on the nanosecond time scale. Kernel (algebra) ; Computer science ; Mathematics ; Combinatorics Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Advanced Chemical Physics Studies
Authors: Manuel Díaz de los Ríos | Evelyn Faife Pérez
Source: Universal Journal of Green Chemistry (article, 2024-12-12)
Field: Chemistry — Spectroscopy

Use of Group Contribution Methods, Hansen’s Solubility Theory and Microsoft Excel in Solvents Selection for Extraction of Natural Products Usually, Hansen’s theory and the COSMO continuous solvation models have been used for the selectionof solvents related to a solute, using the HSPiP and COSMO-RS computational tools, respectively. Those tools are notalways available to many researchers; for this reason, Microsoft Excel (ME) has been used for the estimation of goodsolvents in the extraction of natural products of high value and interesting for the food, pharmaceutical and cosmeticindustries. This method is applicable to cases where there is little, or practically no information about the compound ofinterest, for which functional group contribution methods are used, among them, the Joback method for the estimation ofproperties necessary for the determination of solubility models, and Van Krevelen method for the initial estimation of theHansen solubility parameters for solubility estimates, the Flory-Hugging model is used, from which and its classification,according to the North American Pharmacopeia, the good and bad solvents are defined in the selected database forthe final estimation of Hansen’s parameters and the solubility region. The methodology was validated with ten studycases reported in the literature. The results of this study showed that other green solvents, not reported in the literatureconsulted, are suggested as good solvents for polyphenols, such as diacetone alcohol, diethylene glycol monobutyl etherand tetrahydrofurfuryl alcohol. In the consulted literature there are no references on the use of a structured methodologyin Microsoft Excel (ME) for the estimation of good solvents for the extraction of natural products from the estimationof solubility with existing models. All the consulted papers refer the use of various professional computer programs forthis purpose, specifically HSPiP, and COSMO-RS, which are not accessible to all researchers. With these estimates andan initial classification based on pharmacopoeia criteria, a first classification of good and bad solvents is made, which isrefined by means of optimization methods in ME. Solubility ; Microsoft excel ; Extraction (chemistry) ; Hildebrand solubility parameter ; Computer science ; Diethylene glycol ; Alcohol ; Chemistry ; Solvation ; Process engineering ; Chromatography ; Organic chemistry ; Engineering ; Solvent Analytical Chemistry and Chromatography
Authors: Arseny Skryagin | Felix Divo | Mudasir Ali | Devendra Singh Dhami | Kristian Kersting
Source: arXiv (Cornell University) (preprint, 2024-12-05)
Field: Materials Science — Materials Chemistry

Graph Neural Networks Need Cluster-Normalize-Activate Modules Graph Neural Networks (GNNs) are non-Euclidean deep learning models for graph-structured data. Despite their successful and diverse applications, oversmoothing prohibits deep architectures due to node features converging to a single fixed point. This severely limits their potential to solve complex tasks. To counteract this tendency, we propose a plug-and-play module consisting of three steps: Cluster-Normalize-Activate (CNA). By applying CNA modules, GNNs search and form super nodes in each layer, which are normalized and activated individually. We demonstrate in node classification and property prediction tasks that CNA significantly improves the accuracy over the state-of-the-art. Particularly, CNA reaches 94.18% and 95.75% accuracy on Cora and CiteSeer, respectively. It further benefits GNNs in regression tasks as well, reducing the mean squared error compared to all baselines. At the same time, GNNs with CNA require substantially fewer learnable parameters than competing architectures. Computer science ; Cluster (spacecraft) ; Graph ; Artificial neural network ; Theoretical computer science ; Artificial intelligence ; Computer network Machine Learning in Materials Science ; Brain Tumor Detection and Classification ; Advanced Graph Neural Networks
Authors: Lingfeng Gui | Alan Armstrong | Amparo Galindo | Fareed Bhasha Sayyed | Stanley P. Kolis | Claire S. Adjiman
Source: Molecular Systems Design & Engineering (article, 2024-01-01)
Field: Chemistry — Spectroscopy

On the design of optimal computer experiments to model solvent effects on reaction kinetics Model-based design of experiments using the D-optimality criterion can help select computer experiments to generate more information-rich training sets and leads to more reliable surrogate models that can be used for efficient molecular design. Kinetics ; Solvent ; Chemical kinetics ; Chemistry ; Computer science ; Thermodynamics ; Organic chemistry ; Physics Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods ; Process Optimization and Integration
Authors: Sundas Noureen | Safia Safia | Fatima Javed | Faheem Ullah | Amir Sada Khan
Source: Journal of Health and Rehabilitation Research (article, 2024-03-21)
Field: Chemistry — Organic Chemistry

Removal of Cephalexin-Antibiotic from Drinking Water by Designing CaCl2 Incorporated Chitosan Co- Tragacanth Gum Composite Hydrogel Background: The prevalence of antibiotic contaminants in aquatic environments presents a critical challenge to public health, necessitating the development of efficient removal methods. Emerging pollutants, especially antibiotics, have been implicated in the rise of antibiotic-resistant bacteria, threatening ecological balance and human well-being. Objective: This study aims to synthesize and evaluate the efficacy of a CaCl2-based composite hydrogel using chitosan as a biopolymer for the adsorption of antibiotic pollutants from water, targeting the improvement of water purification technologies in healthcare settings. Methods: Chitosan-based composite hydrogels were synthesized via free radical polymerization, incorporating CaCl2 and utilizing techniques such as Field Emission Scanning Electron Microscopy (FESEM) for morphology analysis, Energy-dispersive X-ray (EDX) analysis for elemental verification, Fourier-transform infrared spectroscopy (FTIR) for functional group identification, and X-ray diffraction (XRD) to determine crystallinity. Physicochemical parameters, including contact time, pH, temperature, reusability, and swelling behavior in various media, were systematically assessed to ascertain the hydrogel's adsorptive performance. Results: The synthesized hydrogels demonstrated a porous and rough surface ideal for adsorption, with an initial antibiotic removal rate of 80% within 30 minutes of contact time. FTIR analysis confirmed the presence of functional groups corresponding to absorption bands at 1053, 1415, 1601, 2600, 2900, 3300, and 3500 cm-1. EDX and XRD analyses affirmed the incorporation of CaCl2, with a crystalline phase characterized by sharp peaks. The hydrogel's maximum adsorption efficiency reached 95% at an optimized adsorbent dose of 0.05 g. Conclusion: The CaCl2-based composite hydrogel exhibits significant potential for the removal of antibiotic pollutants from water, suggesting a viable approach to mitigate the environmental impact of pharmaceutical contaminants and enhance human health by reducing the spread of antibiotic resistance. Tragacanth ; Chitosan ; Composite number ; Antibiotics ; Chemistry ; Materials science ; Food science ; Composite material ; Organic chemistry Nanomaterials for catalytic reactions ; Synthesis of Tetrazole Derivatives