jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mirza Farrukh Waheed Baig | S. F. Hasany | Muhammad Faizan Shirazi
Source:  (article, 2023-10-18)
Field: Materials Science — Materials Chemistry

Green Synthesis of Nano Graphite Materials from Lemon and Orange Peel: A Sustainable Approach for Carbon-Based Materials This research paper presents a study on the preparation and characterization of nano graphite materials (NGMs) from lemon and orange peel powder using the pyrolysis method. The NGMs were analyzed using X-ray Diffractometer (XRD) and Fourier transform infrared spectroscopy (FTIR) for structural and compositional properties. The XRD analysis confirmed the crystalline nature of the NGMs, while the FTIR analysis provided information about the functional groups present in the materials. Our results suggest that NGMs fabricated from lemon and orange peel powder have potential for applications in various fields, including energy storage and catalysis. Diffractometer ; Fourier transform infrared spectroscopy ; Materials science ; Graphite ; Pyrolysis ; Orange (colour) ; Nano- ; Carbon fibers ; Chemical engineering ; Composite material ; Scanning electron microscope ; Chemistry ; Composite number Carbon and Quantum Dots Applications ; Quantum Dots Synthesis And Properties ; Graphene research and applications
Authors: Zi‐Kui Liu
Source: arXiv (Cornell University) (preprint, 2023-10-06)
Field: Materials Science — Materials Chemistry

Zentropy Theory for Quantitative Prediction of Emergent Behaviors through Symmetry-Breaking Configurations Density functional theory (DFT) is the de facto approach for predicting self-consistent-field electronic structures of ground-state configurations of complex atoms, molecules, and solids and providing their property data for materials discovery and design. This capability is greatly enabled by the generalized gradient approximation for exchange-correlation interactions with an important set of exchange-correlation functionals developed by John Perdew and his collaborators in last several decades. The scientific community and the present author's group have greatly benefited from this capability. Over the years, the present author's group has integrated the energetics from DFT-based calculations both at zero K and finite temperature into thermodynamic modeling and developed methods to predict tracer diffusivity, elastic coefficients, interfacial energy, and a number of other properties related to the derivatives of free energy. One key outcome is the accurate prediction of free energy of a system through the consideration of both ground-state and stable symmetry-breaking non-ground-state configurations. It is articulated that phonon properties of all individual configurations can be accurately calculated by quasiharmonic approximations in the temperature and volume ranges of interest, and the emergent behaviors and anharmonicity of a system originate primarily from the statistical competition among all the configurations. Anharmonicity ; Statistical physics ; Ground state ; Thermal diffusivity ; Symmetry breaking ; Field (mathematics) ; Density functional theory ; Physics ; Theoretical physics ; Thermodynamics ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Superconductivity in MgB2 and Alloys
Authors: J. Irshad Ahamed | S. I. Davis Presley | Venkatadri Babu | Jayachitra .s
Source: Research Square (preprint, 2023-11-11)
Field: Chemistry — Organic Chemistry

Research on Chemical Intermediates Spectral, Density functional theory calculations, molecular docking studies, antimicrobial, antioxidant, antidiabetic activities of the novel (E)-2-(3-(5-(3-nitrophenyl)furan-2- yl)acryloyl)-3,4-dihydronaphthalen-1(2H)-one Abstract The title molecule (E)-2-(3-(5-(3-nitrophenyl)furan-2-yl)acryloyl)-3,4-dihydronaphthalen-1(2H)-one (ENFADH) was completed by combining aromatic aldehydes with methyl ketones using Claisen-Schmidt condensation. The NMR, FTIR, and UV-visible investigations were used to characterize the novel chalcones. To determine the geometric, electronic, spectroscopic features, a theoretical model was also built using Gaussian 09 supporting basis set of B3LYP/6-311 + + G (d,p) as well as this basis set was used to performed the geometry optimization, harmonic vibration simulations, molecule electrostatic potential (MEP), frontier molecular orbitals (FMOs), and Mulliken's population analysis. A scaling factor of 0.9600 results in a attractive correspondence between the title compound vibrational spectrum and the experimental IR spectrum. In this innovative effort of this synthesized molecule gives more in-vitro research, such as anti-oxidant, antibacterial and antidiabetic tests were conducted. It revealed significant bactericidal activity with a maximum zone of inhibition of (19.0 ± 1.7mm) at 2.5 g/ml against Pseudomonas aeruginosa (ATCC 27853), presented maximum DPPH scavenging trait ranging from (30.8 ± 1.3% to 81.2 ± 1.5%) against Vitamin C. Likewise, it showed maximum H 2 O 2 scavenging trait ranging from (37.9 ± 2.5% to 75.8 ± 6.4%) against ascorbic acid. In Anti-diabetic activities, it demonstrated α-amylase inhibition of 20.9 ± 2.4to 73.5 ± 2.3% and α-glucosidase inhibition by display activity of (23.3 ± 1.5 to 79.7 ± 1.2 U/L). In-silico molecular docking investigates using Auto Dock 4.0 showed the active pocket of the 1HD2 protein to estimate genotoxicology. By molecular docking, the title molcule exhibited a higher binding interaction energy of -6.5 (Kcal/mol) against the target 1HD2 protein. The obtained results suggest that the ADME characteristics of the examined drugs were predicted using their retention data (RM0 ). These early outcomes might be used to help choose new medication candidates and were shown to have no mutagenic effects when tested for toxicity. Chemistry ; Basis set ; DPPH ; Density functional theory ; Computational chemistry ; Molecule ; Natural bond orbital ; Mulliken population analysis ; Ascorbic acid ; Antibacterial activity ; Docking (animal) ; Stereochemistry ; Organic chemistry ; Antioxidant ; Bacteria Synthesis and biological activity ; Synthesis and Characterization of Heterocyclic Compounds ; Nonlinear Optical Materials Research
Authors: Aleksei Makogon | Frédéric Kanoufi | Viacheslav Shkirskiy
Source: ChemRxiv (preprint, 2023-12-01)
Field: Materials Science — Materials Chemistry

Is Unsupervised Machine Learning Sufficient to Decode the Complexities of Electrochemical Impedance Spectra? As electrochemical research undergoes rapid technological progression, the acquisition of substantial amounts of electrochemical impedance spectra (EIS) becomes increasingly feasible. Yet, this advancement introduces intricate challenges in data processing, automation, and interpretation. This paper delves into the sufficiency of unsupervised machine learning (ML) in decoding EIS complexities, examining its strengths, limitations, and potential pathways for optimization. As we navigated the intricacies of non-linear dimensionality reduction, spotlighting t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP) algorithms, a pattern emerged: these techniques excel at categorizing divergent impedance spectra but show limitations when faced with analogous circuit configurations, especially those substituting a capacitor with a constant phase element. This observation not only underscores a limitation but also accentuates that unsupervised ML approaches, alone, may not fully unravel the nuances of EIS spectra. In the concluding section of our manuscript, we discuss the implications of this finding from a practical standpoint, particularly for electrochemists seeking to apply these methods in their work. Computer science ; Unsupervised learning ; Dimensionality reduction ; Nonlinear dimensionality reduction ; Electrical impedance ; Decoding methods ; Artificial intelligence ; Projection (relational algebra) ; Machine learning ; Pattern recognition (psychology) ; Algorithm ; Electrical engineering ; Engineering Machine Learning in Materials Science ; Machine Learning and ELM ; Non-Destructive Testing Techniques
Authors: Kimberly Wei Wei Tay | Suk Fun Chin | Mohd Effendi Wasli | Zaki Musa
Source: Pertanika journal of science & technology (article, 2023-10-02)
Field: Materials Science — Biomaterials

Response Surface Methodology: A Versatile Tool for the Optimization of Particle Sizes of Cellulose Beads Synthesis parameters are of utmost importance for controlling the particle sizes of cellulose beads. This study aims to investigate the effects of synthesis parameters e.g., stirring speed (250–1250 rpm), surfactant concentrations (0.5–6.0% w/v), cellulose concentrations (1–5% w/v), and reaction temperature (30-100°C) on the particle sizes for micron-sized cellulose beads (µCBs) as well as other parameters e.g. the volume (1.0 mL) and concentration (0.1–1.0% w/v) of cellulose for nanosized (nCBs) cellulose beads using the response surface methodology (RSM). A total of 27 runs were conducted applying RSM based on the central composite design approach with Minitab-19. Cellulose concentrations were shown to have the most significant effect on both µCBs and nCBs. Under optimized conditions, the minimum and maximum mean particle size of µCBs that could be achieved were 15.3 µm and 91 µm, respectively. The predicted mean particle size for nCBs was obtained at 0.01 nm as the smallest and 200 nm as the biggest particle size under the optimum conditions. This study envisages that RSM and experiments for targeted applications such as biomedicine and agriculture could optimize the particle sizes of cellulose beads. Response surface methodology ; Cellulose ; Particle size ; Central composite design ; Materials science ; Particle (ecology) ; Pulmonary surfactant ; Box–Behnken design ; Composite number ; Chemical engineering ; Chromatography ; Composite material ; Chemistry Advanced Cellulose Research Studies ; Electrohydrodynamics and Fluid Dynamics ; Electrospun Nanofibers in Biomedical Applications
Authors: Ioannis Sgouralis | Kelechi Akwataghibe | Mubark Mebrat | Wade D. Van Horn
Source: Biophysical Journal (article, 2023-02-01)
Field: Chemistry — Analytical Chemistry

Fully automated analysis of NMR data with Bayesian nonparametric statistical learning Nonparametric statistics ; Identification (biology) ; Deconvolution ; Benchmark (surveying) ; Computer science ; Characterization (materials science) ; Bayesian probability ; Noise (video) ; Artificial intelligence ; Data mining ; Consistency (knowledge bases) ; Biological system ; Pattern recognition (psychology) ; Machine learning ; Algorithm ; Mathematics ; Statistics ; Physics ; Biology Spectroscopy and Chemometric Analyses ; Metabolomics and Mass Spectrometry Studies ; Gene expression and cancer classification
Authors: Nicolò G. Di Novo | Alvise Bagolini | Nicola M. Pugno
Source: arXiv (Cornell University) (preprint, 2023-07-24)
Field: Materials Science — Surfaces, Coatings and Films

Ice-bridging frustration by self-ejection of single droplets results in superior anti-frosting surfaces Surfaces capable of delaying the frosting passively and facilitating its removal are highly desirable in fields where ice introduces inefficiencies and risks. Coalescence jumping, enabled by highly hydrophobic surfaces, is already exploited to slow down the frosting but it is insufficient to completely eliminate the propagation by ice-bridging. We show how the self-ejection of single condensation droplets can frustrate the ice bridges of all the condensation droplets leading to a frost velocity lower than 0.5 um/s thus dropping below the current limits of passive surfaces by a factor of at least 2. Arrays of truncated microcones, covered by uniformly hydrophobic nanostructures, enable individual condensation droplets to growth and self-propel towards the top of the microstructures and to self-eject once a precise volume is reached. The independency of self-ejection on the neighbour droplets allows a precise control on the droplets' size and distance distributions and the ice-bridging frustration. The most performant microstructures tend to cones with a sharp tip on which the percentage of self-ejection is maximum. Looking towards applications, tapered microstructures allow maximising the percentage of self-ejecting drops while maintaining a certain mechanical strength. Further, it is shown that inserted pinning sites are not essential, which greatly facilitates manufacturing. Bridging (networking) ; Coalescence (physics) ; Frost (temperature) ; Frustration ; Materials science ; Condensation ; Microstructure ; Nanotechnology ; Chemical physics ; Mechanics ; Composite material ; Chemistry ; Condensed matter physics ; Meteorology ; Physics ; Computer science ; Astrobiology Surface Modification and Superhydrophobicity ; Icing and De-icing Technologies ; Advanced Sensor and Energy Harvesting Materials
Authors: Hongxin Wang | Songping D. Huang | Anthony Young | Stephen P. Cramer | Yoshitaka Yoda | Lei Li
Source: Physchem (article, 2023-12-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

X-ray and Nuclear Spectroscopies to Reveal the Element-Specific Oxidation States and Electronic Spin States for Nanoparticulated Manganese Cyanidoferrates and Analogs In this publication, the potential non-gadolinium magnetic resonant imaging agent - the nanoparticulate K<sub>2</sub>Mn[Fe(CN)<sub>6</sub>], its comparison sample KFe[Co(CN)<sub>6</sub>], as well as their reference samples were measured and analyzed with Mn, Co and Fe L-edge X-ray absorption spectroscopy (L XAS). From the information obtained, we conclude that K<sub>2</sub>Mn[Fe (CN)<sub>6</sub>] has a high spin (hs)-Mn(II) and a low spin (ls)-Fe(II), while KFe[Co(CN)<sub>6</sub>] has a hs-Fe(II) and a ls-Co(III). In these Prussian blue (PB) analog structures, L XAS also concluded that the hs-Mn(II) in K<sub>2</sub>Mn[Fe(CN)<sub>6</sub>] or the hs-Fe(II) in KFe[Co(CN)<sub>6</sub>] is bonding to the N in the [M(CN)<sub>6</sub>]<sup>4-/3-</sup> ions [where M = Fe(II) or Co(III)], while the ls-Fe(II) in K<sub>2</sub>Mn[Fe(CN)<sub>6</sub>] or the ls-Co(III) in KFe[Co(CN)<sub>6</sub>] is bonding to the C in the [M(CN)<sub>6</sub>]<sup>4-/3-</sup> ion, suggesting the complexed metalloligand [Mn(II) or Fe(II)] occupy the N-bound site in PB. Then, nuclear resonant vibrational spectroscopy (NRVS) was used to confirm the results from the L XAS measurements: the Mn(II), Eu(III), Gd(III), Fe(II) cations complexed by [M(CN)<sub>6</sub>]<sup>n-</sup>-metalloligand are all taking the N-bound site in the PB-like structures. Our NRVS studies also approve that iron in K<sub>2</sub>Mn[Fe(CN)<sub>6</sub>] compound has 2+ oxidation state and is surrounding by the C donor atoms in the [M(CN)<sub>6</sub>]<sup>n-</sup> ions. Manganese ; Materials science ; Oxidation state ; Spin states ; Spin (aerodynamics) ; Crystallography ; Chemistry ; Inorganic chemistry ; Physics ; Metallurgy ; Metal Magnetism in coordination complexes ; Inorganic Chemistry and Materials ; Crystal Structures and Properties
Authors: Ella M. King | Chrisy Xiyu Du | Qian-Ze Zhu | Samuel S. Schoenholz | Michael P. Brenner
Source: arXiv (Cornell University) (preprint, 2023-12-08)
Field: Materials Science — Materials Chemistry

Programmable patchy particles for materials design Direct design of complex functional materials would revolutionize technologies ranging from printable organs to novel clean energy devices. However, even incremental steps towards designing functional materials have proven challenging. If the material is constructed from highly complex components, the design space of materials properties rapidly becomes too computationally expensive to search. On the other hand, very simple components such as uniform spherical particles are not powerful enough to capture rich functional behavior. Here, we introduce a differentiable materials design model with components that are simple enough to design yet powerful enough to capture complex materials properties: rigid bodies composed of spherical particles with directional interactions (patchy particles). We showcase the method with self-assembly designs ranging from open lattices to self-limiting clusters, all of which are notoriously challenging design goals to achieve using purely isotropic particles. By directly optimizing over the location and interaction of the patches on patchy particles using gradient descent, we dramatically reduce the computation time for finding the optimal building blocks. Ranging ; Computer science ; Simple (philosophy) ; Limiting ; Isotropy ; Computation ; Material Design ; Nanotechnology ; Computational science ; Materials science ; Mechanical engineering ; Physics ; Algorithm ; Engineering Pickering emulsions and particle stabilization ; Advanced Materials and Mechanics ; Modular Robots and Swarm Intelligence
Authors: Mousa Hawan Naeem | Sameer Hassan Al-Nesrawy | Mohammed H. Al-Maamori
Source: East European Journal of Physics (article, 2023-09-04)
Field: Materials Science — Polymers and Plastics

Influence of Lead Nanoparticles on Structural, Morphological, and Mechanical Characteristics of (SiR-PU/Micro-Pb) Composites and Radiation Shielding Applications This research includes the manufacture of a polymeric nanocomposite consisting of silicone rubber/polyurethane as a base, with the addition of the first filler of micro-lead with a ratio of 300 pphr and the second filler of nano-lead with different ratios (0, 0.2, 0.4, 0.6, 0.8 pphr). With the addition of hexane (liquid state) to the superposition using the casting technique at room temperature. The structural properties of the surfaces of the samples were measured using Fourier transformation spectroscopy (FT-IR) and the scanning electron microscope (SEM). In addition to studying the mechanical properties represented by each hardness, tensile, elongation, and elastic modulus. (FT-IR) showed the absence of a chemical reaction for all samples. While SEM measurements showed a homogeneous distribution of micro-lead and nano-lead in the presence of hexane equally, and there were no voids in the eyes of the prepared rubber equally. For the mechanical properties, we see that the hardness, tensile strength and modulus of elasticity continue to improve with the increase in the number of lead nanoparticles. And a decrease in elongation as a result of inverse proportion to the modulus of elasticity. From the results obtained, this composite can be used in gamma ray attenuation applications in shielding, especially in medical and industrial fields. Materials science ; Composite material ; Ultimate tensile strength ; Scanning electron microscope ; Natural rubber ; Polyurethane ; Nanocomposite ; Silicone rubber ; Young's modulus ; Fourier transform infrared spectroscopy ; Nanoparticle ; Elongation ; Composite number ; Nanotechnology ; Chemical engineering Polymer Nanocomposite Synthesis and Irradiation ; Radiation Shielding Materials Analysis ; Polymer Nanocomposites and Properties
Authors: Pascal Pernot
Source: arXiv (Cornell University) (preprint, 2023-10-18)
Field: Materials Science — Materials Chemistry

Can bin-wise scaling improve consistency and adaptivity of prediction uncertainty for machine learning regression ? Binwise Variance Scaling (BVS) has recently been proposed as a post hoc recalibration method for prediction uncertainties of machine learning regression problems that is able of more efficient corrections than uniform variance (or temperature) scaling. The original version of BVS uses uncertainty-based binning, which is aimed to improve calibration conditionally on uncertainty, i.e. consistency. I explore here several adaptations of BVS, in particular with alternative loss functions and a binning scheme based on an input-feature (X) in order to improve adaptivity, i.e. calibration conditional on X. The performances of BVS and its proposed variants are tested on a benchmark dataset for the prediction of atomization energies and compared to the results of isotonic regression. Benchmark (surveying) ; Consistency (knowledge bases) ; Calibration ; Scaling ; Regression ; Computer science ; Variance (accounting) ; Extrapolation ; Isotonic regression ; Bin ; Algorithm ; Artificial intelligence ; Data mining ; Machine learning ; Mathematics ; Statistics Machine Learning in Materials Science ; Machine Learning and Data Classification ; Neural Networks and Applications
Authors: Waldemar Iwanek
Source: Molecules (article, 2023-11-04)
Field: Chemistry — Spectroscopy

Solvent and Substituent Size Influence on the Cyclochiral Rigidity of Aminomethylene Derivatives of Resorcin[4]arene Resorcin[4]arenes (R[4]A) are a group of macrocyclic compounds whose peculiar feature is the presence of eight hydroxyl groups in their structure. The directional formation of intramolecular hydrogen bonds with their participation leads to the formation of a cyclochiral racemic mixture of these compounds. Their stability strongly depends on the substituent and especially the environment in which they are located. The paper discusses the cyclochiral nature of aminomethylene derivatives of R[4]A (AMD-R[4]A). Their cyclochiral rigidity in non-polar solvents has been shown. The influence of the size of the alkyl groups in the amino substituents of AMD-R[4]A on their cyclochiral nature was noted. To calculate the reaction paths for their racemization, the nudged elastic band (NEB) method was employed using the semi-empirical DFT (GFN1-xTB) approach. The calculated activation barrier energies for their racemization in chloroform, obtained through various semi-empirical quantum chemical methods (SE), Hartree-Fock (HF), and density functionals theory (DFT), show good correlation with experimental observations. Among the tested methods, the B38LYP-D4 method is highly recommended due to its fast computational speed and accuracy, which is comparable to the time-consuming double-hybrid DH-revDSD-PBEP86 approach. Substituent ; Racemization ; Chemistry ; Intramolecular force ; Hydrogen bond ; Density functional theory ; Computational chemistry ; Alkyl ; Molecule ; Stereochemistry ; Organic chemistry Molecular spectroscopy and chirality ; Crystallography and molecular interactions ; DNA and Nucleic Acid Chemistry
Authors: Sema Şentürk | H. Gamsizkan | Mehmet Koray Gök | Yavuz Selim Aşçı | Aslı Gök
Source: ALKÜ Fen Bilimleri Dergisi (article, 2023-10-24)
Field: Materials Science — Materials Chemistry

Modelling and Optimization of Oxalic Acid Removal by Layered Double Hydroxide The main purpose of this study is the investigation of the optimization of the conditions of oxalic acid (OxA) adsorption using layered double hydroxide (LDH), modeling the adsorption with both the response surface methodology (RSM) and an artificial neural network (ANN). Mg-Al LDH was synthesized via the co-precipitation method and characterized by Fourier transform infrared spectroscopy (FTIR), inductively coupled plasma mass spectrometry (ICP-MS) and X-ray diffraction (XRD) techniques. The equilibrium time and kinetic model data required to realize the adsorption process design were examined. The process time, initial acid concentration, temperature, and adsorbent dosage as the independent variables were chosen while measuring the percentage of OxA removal. Modeling these results with both RSM and ANN techniques resulted in an ANN model showing a slightly better coefficient of determination than the RSM model. The models yielded consistent results for the optimal conditions of the process. Oxalic acid ; Response surface methodology ; Adsorption ; Central composite design ; Fourier transform infrared spectroscopy ; Hydroxide ; Process optimization ; Coefficient of determination ; Chemistry ; Materials science ; Analytical Chemistry (journal) ; Chromatography ; Inorganic chemistry ; Chemical engineering ; Mathematics ; Organic chemistry Layered Double Hydroxides Synthesis and Applications ; Aerogels and thermal insulation ; Mesoporous Materials and Catalysis