jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Betül Aldemir Dikici
Source: The European Research Journal (article, 2024-08-27)
Field: Materials Science — Materials Chemistry

Impact of internal phase volume on the physical, morphological and mechanical characteristics of emulsion templated scaffolds Objectives: The high porosity of tissue engineering scaffolds is advantageous as they provide a high degree of infiltration of nutrients, enable cell penetration, and support vascularisation. However, the mechanical strength is also critical for providing structural support to the defect site throughout the regeneration process. In this study, we aimed to establish a relationship between internal phase volume and emulsion-templated scaffolds' physical, morphological and mechanical characteristics. Methods: In this work, tetra methacrylate functionalised polycaprolactone (4PCLMA) polymers were synthesised via ring-opening polymerisation followed by methacrylation. 4PCLMA-based emulsion templated matrices with 60%, 75% and 82% internal phase volumes were fabricated (P60, P75, and P82). These scaffolds' densities, porosities, average pore and window sizes, degree of interconnectivity values, and mechanical properties were investigated. Results: Increasing internal phase volume reduced the density of the foams by almost two-fold. No direct correlation was observed between average pore size and internal phase volume. Both the average window sizes and the degree of interconnectivity values increase with increasing internal phase volume. Compression modulus values are calculated as 0.46±0.04 MPa, 0.23±0.02 MPa and 0.14±0.01 MPa for P60, P75, and P82, respectively. Increasing internal phase volume from 60% to 82% caused a more than 2-fold reduction in the stiffness of the emulsion-templated matrices. Conclusions: Accordingly, by reporting on this experimental framework, we established a relationship between internal phase volume and the physical, morphological and mechanical characteristics of 4PCMA-based scaffolds to precisely engineer these characteristics for specific tissue engineering applications. Interconnectivity ; Materials science ; Composite material ; Emulsion ; Porosity ; Polymer ; Polycaprolactone ; Phase (matter) ; Volume (thermodynamics) ; Stiffness ; Rheology ; Chemical engineering ; Biomedical engineering ; Chemistry Pickering emulsions and particle stabilization ; Bone Tissue Engineering Materials ; Electrospun Nanofibers in Biomedical Applications
Authors: Adewale Ajao | Mousa Almousa
Source: Preprints.org (preprint, 2024-09-26)
Field: Chemistry — Analytical Chemistry

Application of Machine Learning to Understand PFAS Occurrence, Distribution, Transport and Removal in Water Per- and polyfluoroalkyl substances (PFAS) are arguably the most common water contaminants in the world today. While several research experiments have been done to understand and remove PFAS from the environment, there is still a lot of unknown. Little has been known about the use of Machine learning (ML) to understand PFAS. This work hence reviews some leading ML approaches and applications in PFAS studies in the distribution, transport, removal, and occurrence predictions of PFAS. Several evaluation matrices were examined and used to perform this function. There are still a lot of areas whereby ML can be used to improve our PFAS knowledge base, some of these were briefly stated in this review. Distribution (mathematics) ; Computer science ; Environmental science ; Mathematics Analytical chemistry methods development ; Water Quality Monitoring and Analysis ; Environmental Chemistry and Analysis
Authors: Hao‐Wei Pang | Xiaorui Dong | Matthew S. Johnson | William H. Green
Source: ChemRxiv (preprint, 2024-01-30)
Field: Materials Science — Materials Chemistry

A Subgraph Isomorphic Decision Tree to Predict Radical Thermochemistry with Bounded Uncertainty Estimation Detailed chemical kinetic models offer valuable mechanistic insights into industrial applications. Automatic generation of a reliable kinetic model requires fast and accurate radical thermochemistry estimation. Kineticists often prefer hydrogen bond increment (HBI) corrections from a closed shell molecule to the corresponding radical for their interpretability, physical meaning, and facilitation of error cancellation as a relative quantity. Tree estimators, used due to limited data, rely on expert knowledge and manual construction currently, posing challenges in maintenance and improvement. In this work, we extend the subgraph isomorphic decision tree (SIDT) algorithm originally developed for rate estimation, to estimate HBI corrections. We introduce a physics-aware splitting criterion, explore a bounded weighted uncertainty estimation method, and evaluate aleatoric uncertainty-based and model variance reduction-based pre-pruning methods. Moreover, we compile a dataset of thermochemical parameters for 2,210 radicals involving C, O, and H based on quantum chemical calculations from recently published works. We leverage the collected dataset to train the SIDT model. Compared to existing empirical tree estimators, the SIDT model (1) offers an automatic approach to generating and extending tree estimator for thermochemistry, (2) has better accuracy and R2, (3) provides significantly more realistic uncertainty estimates, and (4) has a tree structure much more advantageous in descent speed. Overall, the SIDT estimator marks a great leap in kinetic modeling, offering more precise, reliable, and scalable predictions for radical thermochemistry. Thermochemistry ; Estimator ; Interpretability ; Computer science ; Heuristics ; Uncertainty quantification ; Decision tree ; Tree (set theory) ; Algorithm ; Machine learning ; Mathematics ; Chemistry ; Statistics ; Physical chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Process Optimization and Integration
Authors: Haotian Xu | Moritz Flaschel | Laura De Lorenzis
Source: Research Square (preprint, 2024-03-08)
Field: Materials Science — Polymers and Plastics

Discovering non-associated pressure-sensitive plasticity models with EUCLID Plasticity ; Computer science ; Econometrics ; Mathematics ; Physics ; Thermodynamics Polymer crystallization and properties ; Fuel Cells and Related Materials ; Fiber-reinforced polymer composites
Authors: André Paschold | Moritz Schäffler | Xincheng Miao | Luis Gardon | Stephanie Krüger | Henrike Heise | Merle I. S. Röhr | Maria Ott | Birgit Strodel | Wolfgang H. Binder
Source: Bioconjugate Chemistry (article, 2024-06-12)
Field: Materials Science — Biomaterials

Photocontrolled Reversible Amyloid Fibril Formation of Parathyroid Hormone-Derived Peptides Peptide fibrillization is crucial in biological processes such as amyloid-related diseases and hormone storage, involving complex transitions between folded, unfolded, and aggregated states. We here employ light to induce reversible transitions between aggregated and nonaggregated states of a peptide, linked to the parathyroid hormone (PTH). The artificial light-switch 3-{[(4-aminomethyl)phenyl]diazenyl}benzoic acid (AMPB) is embedded into a segment of PTH, the peptide PTH<sub>25-37</sub>, to control aggregation, revealing position-dependent effects. Through <i>in silico</i> design, synthesis, and experimental validation of 11 novel PTH<sub>25-37</sub>-derived peptides, we predict and confirm the amyloid-forming capabilities of the AMPB-containing peptides. Quantum-chemical studies shed light on the photoswitching mechanism. Solid-state NMR studies suggest that β-strands are aligned parallel in fibrils of PTH<sub>25-37</sub>, while in one of the AMPB-containing peptides, β-strands are antiparallel. Simulations further highlight the significance of π-π interactions in the latter. This multifaceted approach enabled the identification of a peptide that can undergo repeated phototriggered transitions between fibrillated and defibrillated states, as demonstrated by different spectroscopic techniques. With this strategy, we unlock the potential to manipulate PTH to reversibly switch between active and inactive aggregated states, representing the first observation of a photostimulus-responsive hormone. Chemistry ; Parathyroid hormone ; Amyloid fibril ; Fibril ; Amyloid (mycology) ; Peptide ; Biophysics ; Hormone ; Biochemistry ; Amyloid β ; Internal medicine ; Calcium ; Organic chemistry ; Inorganic chemistry Supramolecular Self-Assembly in Materials ; Photochromic and Fluorescence Chemistry ; Chemical Synthesis and Analysis
Authors: Amaresh Prusty | Sasmita Padhi
Source: Fabad Journal of Pharmaceutical Sciences (article, 2024-10-04)
Field: Materials Science — Biomaterials

Nanomaterials Marvels: Transformative Advancements in Biomedicine, Drug Delivery, and Pharmaceutical Analysis Nanoparticles are solid colloidal particles ranging in size from 10 to 1000 nm having high surface area-to-volume ratio which allows them for efficient interaction with biological systems. Nanoparticles offer many benefits in comparison to larger particles such as increased surface-to-volume ratio and increased magnetic properties. Nanomaterials hold the potential to revolutionize in critical domains like Biomedicine, Drug Delivery, and Pharmaceutical Analysis. These particles can be functionalized with specific molecules to target diseased cells or tissues, enhancing the efficacy of drugs while minimizing side effects. For instance, gold nanoparticles conjugated with antibodies can be used for targeted cancer therapy, delivering therapeutic agents directly to tumor cells. Similarly drugs encapsulated within nanoparticles can be protected from premature degradation and released in a controlled manner at the target site improving their drug solubility, and enhance cellular uptake, leading to better therapeutic effect in treatment strategies. Polymeric nanoparticles, liposomes, and micelles are some examples of commonly used nanocarriers for drug delivery. Nanomaterials are finding increasing applications in pharmaceutical analysis and can be employed as highly sensitive detection probes for drugs, metabolites, and biomarkers. Additionally, nanomaterials can be used for the separation and purification of bio molecules, facilitating accurate and efficient analysis. This review explores different types of nano materials used exploring their new advances and applications in biomedicine, drug delivery, and pharmaceutical analysis. As research continues to overcome current challenges, nanomaterials unique properties hold immense promise for revolutionizing healthcare and improving patient outcomes. Biomedicine ; Nanotechnology ; Nanocarriers ; Drug delivery ; Nanomaterials ; Nanomedicine ; Nanoparticle ; Drug ; Materials science ; Targeted drug delivery ; Pharmacology ; Medicine ; Bioinformatics Nanoparticle-Based Drug Delivery ; Cancer Research and Treatment ; Computational Drug Discovery Methods
Authors: Ruiqiong Wang | Tiantian Du | Jinchen Cao | Guoqiang Wang
Source: Frontiers in Heat and Mass Transfer (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

The Paraffin Crystallization in Emulsified Waxy Crude Oil by Dissipative Particle Dynamics With the advancement of oilfield extraction technology, since oil-water emulsions in waxy crude oil are prone to be deposited on the pipe wall, increasing the difficulty of crude oil extraction. In this paper, the mesoscopic ... | Find, read and cite all the research you need on Tech Science Press Dissipative particle dynamics ; Crystallization ; Materials science ; Dissipative system ; Particle (ecology) ; Thermal fluids ; Chemical engineering ; Crude oil ; Combustion ; Thermodynamics ; Petroleum engineering ; Chemistry ; Heat transfer ; Composite material ; Organic chemistry ; Physics ; Polymer ; Engineering Petroleum Processing and Analysis ; Enhanced Oil Recovery Techniques ; Crystallization and Solubility Studies
Authors: Shaghayegh Sadeghi | Alan Bui | Ali Forooghi | Jianguo Lü | Alioune Ngom
Source: arXiv (Cornell University) (preprint, 2024-01-05)
Field: Materials Science — Materials Chemistry

Can Large Language Models Understand Molecules? Purpose: Large Language Models (LLMs) like GPT (Generative Pre-trained Transformer) from OpenAI and LLaMA (Large Language Model Meta AI) from Meta AI are increasingly recognized for their potential in the field of cheminformatics, particularly in understanding Simplified Molecular Input Line Entry System (SMILES), a standard method for representing chemical structures. These LLMs also have the ability to decode SMILES strings into vector representations. Method: We investigate the performance of GPT and LLaMA compared to pre-trained models on SMILES in embedding SMILES strings on downstream tasks, focusing on two key applications: molecular property prediction and drug-drug interaction prediction. Results: We find that SMILES embeddings generated using LLaMA outperform those from GPT in both molecular property and DDI prediction tasks. Notably, LLaMA-based SMILES embeddings show results comparable to pre-trained models on SMILES in molecular prediction tasks and outperform the pre-trained models for the DDI prediction tasks. Conclusion: The performance of LLMs in generating SMILES embeddings shows great potential for further investigation of these models for molecular embedding. We hope our study bridges the gap between LLMs and molecular embedding, motivating additional research into the potential of LLMs in the molecular representation field. GitHub: https://github.com/sshaghayeghs/LLaMA-VS-GPT Embedding ; Molecule ; Mathematics ; Computer science ; Chemistry ; Artificial intelligence ; Organic chemistry Machine Learning in Materials Science ; Advanced Biosensing Techniques and Applications
Authors: Wanfu Gao | Hanlin Pan | Qingqi Han | Kunpeng Liu
Source:  (article, 2024-07-26)
Field: Materials Science — Materials Chemistry

Noise-Resistant Label Reconstruction Feature Selection for Partial Multi-Label Learning The "Curse of dimensionality" is prevalent across various data patterns, which increases the risk of model overfitting and leads to a decline in model classification performance. However, few studies have focused on this issue in Partial Multi-label Learning (PML), where each sample is associated with a set of candidate labels, at least one of which is correct. Existing PML methods addressing this problem are mainly based on the low-rank assumption. However, low-rank assumption is difficult to be satisfied in practical situations and may lead to loss of high-dimensional information. Furthermore, we find that existing methods have poor ability to identify positive labels, which is important in real-world scenarios. In this paper, a PML feature selection method is proposed considering two important characteristics of dataset: label relationship's noise-resistance and label connectivity. Our proposed method utilizes label relationship's noise-resistance to disambiguate labels. Then the learning process is designed through the reformed low-rank assumption. Finally, representative labels are found through label connectivity, and the weight matrix is reconstructed to select features with strong identification ability to these labels. The experimental results on benchmark datasets demonstrate the superiority of the proposed method. Adapter (computing) ; Computer science ; Property (philosophy) ; Shot (pellet) ; Artificial intelligence ; Computer hardware ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: L. Bian | Chen Liu | Zhen Zhang | Y. P. Huang | Xinyu Pan | Yi Zhang | Jiaou Wang | Pavel Dudin | J. Ávila | Zhesheng Chen | Yuhui Dong
Source: Communications Physics (article, 2024-12-06)
Field: Materials Science — Materials Chemistry

Automatic extraction of fine structural information in angle-resolved photoemission spectroscopy by multi-stage clustering algorithm Abstract Unsupervised clustering method has shown strong capabilities in automatically categorizing the ARPES (ARPES: angle-resolved photoemission spectroscopy) spatial mapping dataset. However, there is still room for improvement in distinguishing subtle differences caused by different layers and substrates. Here, we propose a method called Multi-Stage Clustering Algorithm (MSCA). Using the K-means clustering results/metrics for real space in different energy-momentum windows as the input of the second round K-means clustering for momentum space, the energy-momentum windows that exhibit subtle inhomogeneity in real space will be highlighted. It recognizes different types of electronic structures both in real space and momentum space in spatially resolved ARPES dataset. This method can be used to capture the areas of interest, and is especially suitable for samples with complex band dispersions, and can be a practical tool to any high dimensional scientific data analysis. Angle-resolved photoemission spectroscopy ; Cluster analysis ; Position and momentum space ; Space (punctuation) ; Momentum (technical analysis) ; Computer science ; Stage (stratigraphy) ; Photoemission spectroscopy ; Physics ; Algorithm ; Computational physics ; Artificial intelligence ; Electronic structure ; X-ray photoelectron spectroscopy ; Condensed matter physics ; Geology ; Quantum mechanics ; Nuclear magnetic resonance Machine Learning in Materials Science ; Physics of Superconductivity and Magnetism ; Transition Metal Oxide Nanomaterials
Authors: Bojana Anić Marković | Аleksandar Marinković | Jelena Antić‐Stanković | Stefan Mijatović | Ilija Cvijetić | Milena Simić | Irena Arandjelović
Source: Pharmaceutics (article, 2024-08-18)
Field: Chemistry — Organic Chemistry

Synthesis and Antimicrobial Activity of Newly Synthesized Nicotinamides Antioxidants are promising compounds with antimicrobial activity against drug-resistant pathogens, especially when combined with conventional antimicrobials. Our study aimed to characterize the structure of nicotinamides synthesized from nicotinic acid and thiocarbohydrazones and to evaluate their antibacterial and antifungal activity. Seven nicotinic acid hydrazides (NC <b>1</b>-<b>7</b>) were synthesized using mono-thiocarbohydrazones with hydroxyl group substituents, along with quinolone, phenolic, and pyridine rings known for their antimicrobial activity. The in vitro antimicrobial activity of NC <b>1</b>-<b>7</b>, at concentrations ranging from 0.001 to 1 mM, was tested against <i>Staphylococcus aureus</i> (ATCC 6538), <i>Enterococcus faecalis</i> (ATCC 29212), <i>Pseudomonas aeruginosa</i> (ATCC 27853), <i>Klebsiella pneumoniae</i> (NCIMB 9111), and <i>Candida albicans</i> (ATCC 24433) using the broth microdilution method per EUCAST 2024 guidelines. Microorganism survival percentages were calculated based on optical density, and target fishing using the PharmMapper database identified potential molecular targets. The results showed that <i>P. aeruginosa</i> was most susceptible to the compounds, while <i>C. albicans</i> was the least susceptible. NC <b>3</b> significantly inhibited <i>P. aeruginosa</i> and <i>K. pneumoniae</i> growth at 0.016 mM, while higher concentrations were required for <i>S. aureus</i>, <i>E. faecalis</i>, and <i>C. albicans</i>. NC <b>5</b> was most effective against gram-positive bacteria at 0.03 mM. Only NC <b>4</b> completely inhibited <i>C. albicans</i> below 1 mM. NC <b>3</b>, with the lowest concentration for 50% growth inhibition (0.016-0.064 mM), showed promising antibacterial potential against specific AMR-related proteins (bleomycin resistance protein, HTH-type transcriptional regulator QacR, and streptogramin A acetyltransferase), suggesting that this class of compounds could enhance or restore the activity of established antibiotics. Antimicrobial ; Chemistry ; Microbiology ; Biology Synthesis and biological activity ; Computational Drug Discovery Methods ; Bioactive Compounds and Antitumor Agents
Authors: Piotr Cysewski | Maciej Przybyłek | Tomasz Jeliński
Source: Polymers in Medicine (article, 2024-02-02)
Field: Materials Science — Materials Chemistry

Predicting sulfanilamide solubility in the binary mixtures using a reference solvent approach Our study addresses the crucial issue of coherence of solubility data used for many theoretical inquiries, including parameter fitting of semi-empirical models, in-depth thermodynamic interpretations and application of machine learning protocols. The effectiveness of the proposed methodology for dataset curation was demonstrated for sulfanilamide solubility in binary mixtures. This approach enabled not only the formulation of a consistent dataset of sulfanilamide solubility binary solvent mixtures, but also its implementation as a qualitative tool guiding rationale solvent selection for experimental solubility screening. Solubility ; Dissolution ; Chemistry ; Solvent ; Acetone ; Sulfisoxazole ; Thermodynamics ; Organic chemistry ; Physics Crystallization and Solubility Studies ; Analytical Chemistry and Chromatography ; Analytical Methods in Pharmaceuticals
Authors: Keyu Bao | Yanfang Zhao | Wei Ding | Yuanbin Xiao | Bing Yang
Source: Nanomaterials (article, 2024-11-21)
Field: Materials Science — Materials Chemistry

First Principles Study of p-Type Transition and Enhanced Optoelectronic Properties of g-ZnO Based on Diverse Doping Strategies By utilizing first principles calculations, p-type transition in graphene-like zinc oxide (g-ZnO) through elemental doping was achieved, and the influence of different doping strategies on the electronic structure, energy band structure, and optoelectronic properties of g-ZnO was investigated. This research study delves into the effects of strategies such as single-acceptor doping, double-acceptor co-doping, and donor-acceptor co-doping on the properties of g-ZnO. This study found that single-acceptor doping with Li and Ag elements can form shallow acceptor levels, thereby facilitating p-type conductivity. Furthermore, the introduction of the donor element F can compensate for the deep acceptor levels formed by double-acceptor co-doping, transforming them into shallow acceptor levels and modulating the energy band structure. The co-doping strategy involving double-acceptor elements and a donor element further optimizes the properties of g-ZnO, such as reducing the bandgap and enhancing carrier mobility. Additionally, in terms of optical properties, g-Zn<sub>14</sub>Li<sub>2</sub>FO<sub>15</sub> demonstrates outstanding performance in the visible-light region compared with other doping systems, especially generating a higher absorption peak around the wavelength of 520 nm. These findings provide a theoretical foundation for the application of g-ZnO in optoelectronic devices. Doping ; Materials science ; Optoelectronics ; Nanotechnology ZnO doping and properties ; Ga2O3 and related materials ; Copper-based nanomaterials and applications
Authors: Ander Gracia Moisés | Ignacio Vitoria | Amaia Avedillo de la Casa | María Moreno Pérez | J. J. Imas | Carlos R. Zamarreño
Source: Food Control (article, 2024-08-23)
Field: Chemistry — Analytical Chemistry

Optimization of optical spectroscopy classification algorithms for limited data scenarios in the food industry: Tomato sauce samples case Spectroscopy ; Algorithm ; Computer science ; Food industry ; Environmental science ; Chemistry ; Food science ; Physics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis