jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Krysma Irene Munar | Maritess Lacquio | Bong Carlo Remillon | Mariam C. Recuenco
Source: KIMIKA (article, 2024-12-11)
Field: Materials Science — Materials Chemistry

Synthesis of BSA-stabilized copper nanoclusters and evaluation of their potential use as fluorescent sensors for folic acid This study describes the synthesis and characterization of BSA-stabilized copper nanoclusters (BSA-CuNCs) for use as sensors for the B vitamin folic acid. Ascorbate served as a reducing agent while bovine serum albumin (BSA) served as a capping and stabilizing agent. The BSA-CuNCs had a golden-yellow appearance and perceivably have good dispersibility. Transmission electron microscopy indicated average diameters of the BSA-CuNCs as 2.47 ± 0.50 nm. With atomic force microscopy and dynamic light scattering, the diameters of the BSA-CuNCs were varied and larger than anticipated (64 ± 13 nm AFM; 11-608 nm DLS), since nanocluster sizes should be less than 2 nm. The BSA-CuNCs' UV–visible spectra did not show peaks in the 500–600 nm indicating the absence of the surface plasmon resonance (SPR) of larger nanoparticles. The BSA-CuNCs were excited at 320 nm and showed a fluorescence emission at 644 nm. The BSA-CuNCs were then evaluated as potential folic acid sensors. Fluorescence quenching was observed as folic acid was added incrementally. The quenching fitted well with the Stern-Volmer equation, with estimated limits for detection (LOD) and quantitation (LOQ) at 0.77 µg/mL and 2.35 µg/mL, respectively. Next, the folic acid content of some over-the-counter supplements was assessed. There was a range of 33.31% to 40.21% in the percent error between the claimed and experimental folic acid values, which were beyond the acceptable accuracy and precision errors of 5% and 10%, respectively. Nevertheless, current results may suggest potential that the BSA-CuNCs may be used to quantify folic acid. However, further research is required to improve sample preparations and analytical procedures. Folic acid ; Fluorescence ; Nanoclusters ; Copper ; Chemistry ; Combinatorial chemistry ; Biochemistry ; Organic chemistry ; Medicine ; Internal medicine ; Physics Nanocluster Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Aslı Çakan | Chanaprom Cholsuk | Angus Gale | Mehran Kianinia | Serkan Paçal | Serkan Ateş | Igor Aharonovich | Milos Toth | Tobias Vogl
Source: arXiv (Cornell University) (preprint, 2024-10-10)
Field: Materials Science — Materials Chemistry

Quantum Optics Applications of Hexagonal Boron Nitride Defects Hexagonal boron nitride (hBN) has emerged as a compelling platform for both classical and quantum technologies. In particular, the past decade has witnessed a surge of novel ideas and developments, which may be overwhelming for newcomers to the field. This review provides an overview of the fundamental concepts and key applications of hBN, including quantum sensing, quantum key distribution, quantum computing, and quantum memory. Additionally, we highlight critical experimental and theoretical advances that have expanded the capabilities of hBN, in a cohesive and accessible manner. The objective is to equip readers with a comprehensive understanding of the diverse applications of hBN, and provide insights into ongoing research efforts. Hexagonal boron nitride ; Boron nitride ; Hexagonal crystal system ; Materials science ; Quantum ; Boron ; Nitride ; Nanotechnology ; Crystallography ; Physics ; Quantum mechanics ; Chemistry ; Graphene ; Organic chemistry Boron and Carbon Nanomaterials Research ; Graphene research and applications ; Diamond and Carbon-based Materials Research
Authors: Jomy Jose Philip | Arismar Cerqueira S. | Nagaraju Kottam | Chandan Hunsur Ravikumar | R. Geetha Balakrishna
Source: Journal of Fluorescence (review, 2024-11-26)
Field: Materials Science — Materials Chemistry

Reliability of Multi-Emissive Carbon Quantum Dots for Multiplexing; Assessing the Figures of Merit Chemistry ; Figure of merit ; Reliability (semiconductor) ; Quantum dot ; Multiplexing ; Carbon quantum dots ; Carbon fibers ; Nanotechnology ; Reliability engineering ; Optoelectronics ; Telecommunications ; Quantum mechanics ; Algorithm ; Composite number ; Computer science Carbon and Quantum Dots Applications ; Quantum Dots Synthesis And Properties ; Graphene and Nanomaterials Applications
Authors: Dian Agung Pangaribowo | Fathunnisa Fathunnisa | Ari Satia Nugraha | Ayik Rosita Puspaningtyas | Indah Purnama Sary
Source: Borneo Journal of Pharmacy (article, 2024-08-30)
Field: Chemistry — Organic Chemistry

Structure Modification of Cinnamic Acid to (E)-1-(3,4-dihydroisoquinoline-2(1H)-yl)-3-phenylprop-2-en-1-one and Antioxidant Activity Test by DPPH Method Antioxidants can protect cells from free radical damage by stabilizing them. One of the compounds that has antioxidant activity is cinnamic acid. Cinnamic acid and its derivatives have several activities: antibacterial, anticancer, and antioxidant. However, the ability of cinnamic acid to capture free radicals is still relatively low. One of the efforts that can be made to increase the antioxidant activity of cinnamic acid is to modify its structure. Structure modification is an effort to improve the pharmacological activity of a compound through chemical synthesis reactions. The cinnamic acid structure can be modified by changing the carboxylic -OH group into an amine group through an N-atom acylation reaction. This study was conducted by reacting cinnamoyl chloride (1a), which is a cinnamic acid derivative with 1,2,3,4-tetrahydroisoquinoline (2b) which is a compound of isoquinoline group to produce (E)-1-(3,4-dihydroisoquinoline-2(1H)-yl)-3-phenylprop-2-en-1-one (3b) and then tested for antioxidant activity using DPPH method. The resulting product compound was yellow crystals with a yield of 81.56%. The antioxidant activity produced by the product is more significant than that of cinnamic acid compounds at the same concentration. DPPH ; Cinnamic acid ; Antioxidant ; Chemistry ; Organic chemistry ; Stereochemistry Synthesis and biological activity ; Metal complexes synthesis and properties ; Free Radicals and Antioxidants
Authors: Khodeja Afrin | Kaniz Fatema | Fariha Afrose | Md. Abdus Samad Azad | Md. Shamim Akter | Md. Saiful Alam | Papia Haque | Yeasmin Akter | Newaz Mohammed Bahadur
Source: Open Journal of Applied Sciences (article, 2024-01-01)
Field: Materials Science — Biomaterials

ZnO Incorporated Acrylamide Grafted Chitosan Based Composite Film for Advanced Wound Healing Applications This study was carried out to prepare ZnO nanoparticles incorporated acrylamide grafted chitosan composite film for possible biomedical application especially drug loading in wound healing. ZnO nanoparticles were prepared by co-precipitation method from zinc acetate di-hydrate and incorporated in acrylamide grafted chitosan. FT-IR and TGA of the prepared composite film confirmed the successful incorporation of ZnO nanoparticles in the acrylamide-grafted polymer matrix. SEM images showed that the ZnO nanoparticles were homogeneously distributed on the porous matrix of the composite film. Water uptake and buffer uptake analysis revealed that the composite film could hold water and buffer sufficiently, which facilitated the absorption of exudate from the wound site. Amoxicillin was loaded in the prepared composite film and the maximum loading efficiency was found to be 67.33% with drug concentration of 300 ppm. In vitro studies showed greater antimicrobial activity of drug-loaded composite film compared to both pure film and standard antibiotic disc. Finally, the In vivo mouse model showed maximum healing efficiency compared to conventional gauge bandages because the loading of antibiotic in the film produced a synergistic effect and healing time was reduced. Chitosan ; Acrylamide ; Composite number ; Wound healing ; Materials science ; Composite film ; Composite material ; Biomedical engineering ; Chemical engineering ; Polymer ; Engineering ; Surgery ; Medicine ; Copolymer Nanocomposite Films for Food Packaging ; Electrospun Nanofibers in Biomedical Applications ; Hydrogels: synthesis, properties, applications
Authors: He Chen | Yifan Cheng | Jianqiang Dong | Jie Mao | Xin Wang | Yuan Gao | Yuchao Li | Chengzhi Wang | Qiong Wu
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-02-08)
Field: Materials Science — Materials Chemistry

Protein property prediction based on local environment by 3D equivariant convolutional neural networks Abstract Predicting the properties of proteins is an important procedure in protein engineering. It determines the subspace of mutations for protein modifications, which is critical to the success of the project, but heavily relies on the knowledge and experience of scientists. In this study, we propose a novel deep 3D-CNN model, Eq3DCNN, specifically designed for local environment-related tasks in protein engineering. Eq3DCNN uses basic atom descriptors and their coordinates as inputs, utilizing customized data augmentations to enhance its training efficiency. To make the Eq3DCNN extracted features with more generalization capability, we incorporated a rotation equivariant module to get rotation invariant features. Using cross-validations with different data splitting strategies and under the scenarios of zero-shot predictions, we demonstrate that Eq3DCNN outperformed other 3D-CNN models in stability predictions, and also well-preformed on other prediction tasks, such as the binding pocket and the secondary structure predictions. Our results also identified the key factors that contribute to the model’s accuracy and the scope of its applications. These findings may help scientists in designing better mutation experiments and increasing the success rate in protein engineering. Computer science ; Convolutional neural network ; Scope (computer science) ; Generalization ; Property (philosophy) ; Invariant (physics) ; Artificial intelligence ; Equivariant map ; Key (lock) ; Rotation (mathematics) ; Subspace topology ; Stability (learning theory) ; Mutation ; Machine learning ; Algorithm ; Mathematics Machine Learning in Materials Science ; Protein Structure and Dynamics ; RNA and protein synthesis mechanisms
Authors: S. N. G. Chu | Yuanping Chen | Zhelin Li | Rami Mrad | Runxian Jiao | Guan Huang | Jun Shan
Source: Research Square (preprint, 2024-02-02)
Field: Materials Science — Materials Chemistry

Generative Design of Crystal Structures by Point Cloud Representations and Diffusion Model Point cloud ; Point (geometry) ; Generative model ; Generative grammar ; Diffusion ; Computer science ; Mathematics ; Geometry ; Artificial intelligence ; Physics Machine Learning in Materials Science ; Manufacturing Process and Optimization
Authors: Vassiliki Belessi | Apostolos Koutsioukis | Dimitra Giasafaki | Theodora Philippakopoulou | Vassiliki Panagiotopoulou | C. Mitzithra | Sotiria Kripotou | Georgios Manolis | Theodore Steriotis | Georgia Charalambopoulou | Vasilios Georgakilas
Source: Nanomaterials (article, 2024-05-15)
Field: Chemistry — Organic Chemistry

One-Pot Synthesis of Functionalised rGO/AgNPs Hybrids as Pigments for Highly Conductive Printing Inks This work provides a method for the development of conductive water-based printing inks for gravure, flexography and screen-printing incorporating commercial resins that are already used in the printing industry. The development of the respective conductive materials/pigments is based on the simultaneous (in one step) reduction of silver salts and graphene oxide in the presence of 2,5-diaminobenzenesulfonic acid that is used for the first time as the common in-situ reducing agent for these two reactions. The presence of aminophenylsulfonic derivatives is essential for the reduction procedure and in parallel leads to the enrichment of the graphene surface with aminophenylsulfonic groups that provide a high hydrophilicity to the final materials/pigments. Graphene ; Materials science ; Oxide ; Nanotechnology ; Electrical conductor ; Inkwell ; Environmentally friendly ; Conductive ink ; Reducing agent ; Chemical engineering ; Screen printing ; Sheet resistance ; Composite material ; Metallurgy ; Layer (electronics) Nanomaterials for catalytic reactions ; Conducting polymers and applications ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Adewale Ajao | Mousa Almousa
Source: Preprints.org (preprint, 2024-04-23)
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 ; Psychology ; Mathematics Analytical chemistry methods development ; Water Quality Monitoring and Analysis ; Environmental Chemistry and Analysis
Authors: Ian T. Beck | Erica C. Mitchell | Annabelle R. Webb | Justin M. Turney | Brandon Rotavera | Henry F. Schaefer
Source: The Journal of Physical Chemistry A (article, 2024-12-06)
Field: Chemistry — Organic Chemistry

Evaluating the Importance of Conformers for Understanding the Vacuum-Ultraviolet Spectra of Oxiranes: Experiment and Theory Vacuum-ultraviolet (VUV) absorption spectroscopy enables electronic transitions that offer the unambiguous identification of molecules. As target molecules become more complex, multifunctional species present a great challenge to both experimental and computational spectroscopy. This research reports both experimental and theoretical studies of oxiranes. Computationally, the nuclear ensemble approach has been used to accurately predict experimental spectra for a variety of molecules. However, this approach incurs great computational cost, as ensembles generally consist of thousands of geometries. The present study aims to drastically reduce the ensemble by evaluating the significance of the conformers to the predicted spectra. This approach was applied to 11 substituted oxiranes using the Conformer Rotamer Ensemble Sampling Tool (CREST) of Grimme to generate an ensemble of unique conformers determined by their Boltzmann populations. Five TD-DFT functionals (BMK, CAM-B3LYP, M06-2X, MN15, ωB97X-D) and EOM-CCSD were used to simulate the spectrum of each substituted oxirane ensemble. Computed spectra were then compared to the experiment using both qualitative and quantitative metrics. Based on these metrics, it was observed that certain conformers may not be necessary to characterize this set of oxiranes despite the temperature (323 K) of the experiment. A single conformer can then be used with TD-DFT and EOM-CCSD to replicate the experimental spectra of these medium-sized combustion species. Conformational isomerism ; Spectral line ; Spectroscopy ; Molecule ; Chemistry ; Computational chemistry ; Statistical physics ; Chemical physics ; Physics ; Quantum mechanics Free Radicals and Antioxidants ; Chemical Thermodynamics and Molecular Structure ; Computational Drug Discovery Methods
Authors: Howard Mark | Jerome Workman
Source: Spectroscopy (article, 2024-02-01)
Field: Chemistry — Analytical Chemistry

Data Transforms in Chemometric Calibrations, Part 4A: Continuous-Wavelength Spectra and Discrete-Wavelength Models In this column and its successor, we describe and explain some algorithms and data transforms beyond those commonly used. We present and discuss algorithms that are rarely, if ever, used in practice, despite having been described in the literature. These comprise algorithms used in conjunction with continuous spectra, as well as those used with discrete spectra. Wavelength ; Spectral line ; Calibration ; Chemometrics ; Analytical Chemistry (journal) ; Materials science ; Physics ; Optics ; Mathematics ; Chemistry ; Statistics ; Chromatography ; Quantum mechanics Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Metabolomics and Mass Spectrometry Studies
Authors: Jakkapat Seeyangnok | Udomsilp Pinsook | Graeme J. Ackland
Source: arXiv (Cornell University) (preprint, 2024-12-11)
Field: Materials Science — Materials Chemistry

Two-gap superconductivity in a Janus MoSeLi monolayer Two-dimensional (2D) lithium-decorated materials have emerged as a significant area of study since the prediction of superconductivity in lithium-decorated graphene at temperatures around 8.1 K, with experimental evidence observed at Tc = 5.9 K. Following earlier studies, this paper focuses on the hexagonal Janus MoSeLi monolayer as a promising candidate for Li-decorated 2D materials. Our research reveals that lithium substitution on a selenium layer of MoSe2 can produce a hexagonal Janus MoSeLi monolayer, which exhibits metallic behavior with potential phonon-mediated superconductivity with a critical temperature Tc of 4.5 K. Additionally, by solving the anisotropic gap equations derived from Migdal-Eliashberg theory, we found that the Janus MoSeLi monolayer exhibits two-gap superconductivity. This finding underscores the potential of hexagonal Janus MoSeLi as a significant Li-decorated 2D material for exploring two-dimensional superconductivity and sets the stage for further investigations into new families of Janus transition-metal chalcogenides. Janus ; Superconductivity ; Monolayer ; Condensed matter physics ; Materials science ; Physics ; Nanotechnology 2D Materials and Applications ; Iron-based superconductors research ; Advanced Thermoelectric Materials and Devices
Authors: Noor Ageng Setiyanto | Harun Al Azies | Usman Sudibyo | Ayu Pertiwi | Setyo Budi | Muhamad Akrom
Source: Journal of Multiscale Materials Informatics (article, 2024-04-29)
Field: Chemistry — Organic Chemistry

A Machine Learning Model for Evaluation of the Corrosion Inhibition Capacity of Quinoxaline Compounds Investigating potential corrosion inhibitors via empirical research is a labor- and resource-intensive process. In this work, we evaluated various linear and non-linear algorithms as predictive models for corrosion inhibition efficiency (CIE) values using a machine learning (ML) paradigm based on the quantitative structure-property relationship (QSPR) model. In the quinoxaline compound dataset, our analysis showed that the XGBoost model performed the best predictor of other ensemble-based models. The coefficient of determination (R2), mean absolute percentage error (MAPE), and root mean squared error (RMSE) metrics were used to objectively assess this superiority. To sum up, our study offers a fresh viewpoint on the effectiveness of machine learning algorithms in determining the ability of organic compounds like quinoxaline to suppress corrosion on iron surfaces. Quinoxaline ; Corrosion ; Chemistry ; Computer science ; Artificial intelligence ; Combinatorial chemistry ; Organic chemistry Synthesis and Biological Evaluation ; Corrosion Behavior and Inhibition
Authors: Mohamed Zbiri | Anne A. Y. Guilbert
Source: The Journal of Physical Chemistry B (preprint, 2024-06-17)
Field: Chemistry — Physical and Theoretical Chemistry

Dynamics of Polyalkylfluorene Conjugated Polymers: Insights from Neutron Spectroscopy and Molecular Dynamics Simulations The dynamics of the conjugated polymers poly(9,9-dioctylfluorene) (PF8) and poly(9,9-didodecylfluorene) (PF12), differing by the length of their side chains, is investigated in the amorphous phase using the temperature-dependent quasielastic neutron scattering (QENS) technique. The neutron spectroscopy measurements are synergistically underpinned by molecular dynamics (MD) simulations. The probe is focused on the picosecond time scale, where the structural dynamics of both PF8 and PF12 would mainly be dominated by the motions of their side chains. The measurements highlighted temperature-induced dynamics, reflected in the broadening of the QENS spectra upon heating. The MD simulations reproduced well the observations; hence, the neutron measurements validate the MD force fields, the adopted amorphous model structures, and the numerical procedure. As the QENS spectra are dominated by the signal from the hydrogens on the backbones and side chains of PF8 and PF12, extensive analysis of the MD simulations allowed the following: (i) tagging these hydrogens, (ii) estimating their contributions to the self-part of the van Hove functions and hence to the QENS spectra, and (iii) determining the activation energies of the different motions involving the tagged hydrogens. PF12 is found to exhibit QENS spectra broader than those of PF8, indicating a more pronounced motion of the didodecyl chains of PF12 as compared to dioctyl chains of PF8. This is in agreement with the outcome of our MD analysis: (i) confirming a lower glass transition temperature of PF12 compared to PF8, (ii) showing PF12 having a lower density than PF8, and (iii) highlighting lower activation energies of the motions of PF12 in comparison with PF8. This study helped to gain insights into the temperature-induced side-chain dynamics of the PF8 and PF12 conjugated polymers, influencing their stability, which could potentially impact, on the practical side, the performance of the associated optoelectronic active layer. Dynamics (music) ; Polymer ; Molecular dynamics ; Conjugated system ; Neutron spectroscopy ; Chemical physics ; Spectroscopy ; Materials science ; Neutron ; Statistical physics ; Nanotechnology ; Neutron scattering ; Physics ; Chemistry ; Nuclear magnetic resonance ; Computational chemistry ; Nuclear physics ; Quantum mechanics Advanced Physical and Chemical Molecular Interactions
Authors: Zhaoning Yu | Xiangyang Xu | Hongyang Gao
Source: arXiv (Cornell University) (preprint, 2024-10-03)
Field: Materials Science — Materials Chemistry

G2T-LLM: Graph-to-Tree Text Encoding for Molecule Generation with Fine-Tuned Large Language Models We introduce G2T-LLM, a novel approach for molecule generation that uses graph-to-tree text encoding to transform graph-based molecular structures into a hierarchical text format optimized for large language models (LLMs). This encoding converts complex molecular graphs into tree-structured formats, such as JSON and XML, which LLMs are particularly adept at processing due to their extensive pre-training on these types of data. By leveraging the flexibility of LLMs, our approach allows for intuitive interaction using natural language prompts, providing a more accessible interface for molecular design. Through supervised fine-tuning, G2T-LLM generates valid and coherent chemical structures, addressing common challenges like invalid outputs seen in traditional graph-based methods. While LLMs are computationally intensive, they offer superior generalization and adaptability, enabling the generation of diverse molecular structures with minimal task-specific customization. The proposed approach achieved comparable performances with state-of-the-art methods on various benchmark molecular generation datasets, demonstrating its potential as a flexible and innovative tool for AI-driven molecular design. Encoding (memory) ; Computer science ; Tree (set theory) ; Graph ; Theoretical computer science ; Artificial intelligence ; Mathematics ; Combinatorics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis