jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Matthew E. Dwyer | Markus Sutter | Cheryl A. Kerfeld
Source: Communications Biology (article, 2024-11-24)
Field: Materials Science — Materials Chemistry

Characterization of a widespread sugar phosphate-processing bacterial microcompartment Many prokaryotes form Bacterial Microcompartments (BMCs) that encapsulate segments of specialized metabolic pathways to enhance catalysis. The various functions of metabolosomes, catabolic BMCs, are dictated by the signature enzyme that processes initial substrates of the confined pathway. The components and native functions of several metabolosomes have been experimentally characterized; however one of the most prevalent across all bacteria has yet to be studied. Sugar Phosphate Utilizing (SPU) BMC loci encode enzymes predicted to be involved in sugar phosphate metabolism. The SPU genetic loci are found in organisms occupying habitats ranging from soils to hot springs, highlighting the ubiquity of the SPU BMC. We bioinformatically characterized seven SPU subtypes, all which contain an enzyme unique to SPU BMCs, a deoxyribose 5-phosphate aldolase (DERA). Here, we define the fundamental characteristics of SPU BMCs and have expressed, purified, and characterized a set of SPU core enzymes. These include a protein-protein complex formed between a SPU BMC DERA and a predicted ribose 5-phosphate isomerase. Further, we show that the SPU BMC DERA is catalytically active and propose that it acts as the universal signature enzyme for the SPU BMC, with implications for fundamental understanding and biotechnological applications of SPU BMCs. Aldolase A ; Enzyme ; Biology ; Sugar phosphates ; Biochemistry ; Isomerase ; Catabolism ; Sugar ; Bacteria ; Genetics ; Computational biology Enzyme Structure and Function ; Biochemical and Molecular Research ; Genomics and Phylogenetic Studies
Authors: Sarif Surorejo | Muhamad Rizal Ubaidillah | Syefudin Syefudin | Gunawan Gunawan
Source: Journal of Intelligent Decision Support System (IDSS) (article, 2024-06-30)
Field: Chemistry — Analytical Chemistry

Detection of normal chicken meat and tiren chicken using naïve bayes classifier and glcm feature extraction The chicken farming industry is an important sector in the Indonesian economy, but there are food security issues with the presence of tiren chicken. This research aims to develop a more accurate and efficient method of detection of tiren chickens using Naive Bayes Classifier with Gaussian and Bernoulli kernels and GLCM feature extraction. Data is collected from various internet sources, then pre-processing and feature extraction is carried out. The Naive Bayes Classifier algorithm is implemented with two kernels and evaluated using accuracy, precision, recall, and f1-score metrics. The Gaussian kernel showed an accuracy of 0.75, higher than Bernoulli's kernel which was only 0.50. Models with Gaussian kernels have high performance in detecting tiren chickens and normal chicken precision. The combination of Gaussian and Bernoulli kernels and GLCM feature extraction is effective in improving the detection accuracy of tiren chickens, contributing significantly to food safety and consumer confidence Naive Bayes classifier ; Pattern recognition (psychology) ; Artificial intelligence ; Feature extraction ; Classifier (UML) ; Bayes' theorem ; Computer science ; Bayesian probability ; Support vector machine Spectroscopy and Chemometric Analyses
Authors: Paul J. Robinson | Adam Rettig | Hieu Q. Dinh | Meng‐Fu Chen | Joonho Lee
Source: arXiv (Cornell University) (preprint, 2024-03-19)
Field: Chemistry — Physical and Theoretical Chemistry

Condensed-Phase Quantum Chemistry Molecular quantum chemistry has seen enormous progress in the last few decades thanks to the more advanced and sophisticated numerical techniques and computing power. Following the recent interest in extending these capabilities to condensed-phase problems, we summarize basic knowledge of condensed-phase quantum chemistry for ones with experience in molecular quantum chemistry. We highlight recent efforts in this direction, including solving the electron repulsion integrals bottleneck and implementing hybrid density functional theory and wavefunction methods, and lattice dynamics for periodic systems within atom-centered basis sets. Many computational techniques presented here are inspired by the extensive method developments rooted in quantum chemistry. In this Focus Article, we selectively focus on the computational techniques rooted in molecular quantum chemistry, emphasize some challenges, and point out open questions. We hope our perspectives will encourage researchers to pursue this exciting and promising research avenue. Chemistry ; Quantum chemistry ; Quantum ; Chemical physics ; Physics ; Quantum mechanics ; Organic chemistry ; Catalysis ; Reaction mechanism Various Chemistry Research Topics ; History and advancements in chemistry
Authors: Dong-Dong Jiang | Jian-Li Shao
Source: arXiv (Cornell University) (preprint, 2024-05-08)
Field: Materials Science — Materials Chemistry

Equivalence analysis between Quasi-coarse-grained and Atomistic Simulations In recent years, simulation methods based on the scaling of atomic potential functions, such as quasi-coarse-grained dynamics and coarse-grained dynamics, have shown promising results for modeling crystalline systems at multiple scales. However, this letter presents evidence suggesting that the spatiotemporal trajectories of coarse-grained systems generated by such simulation methods exhibit a complete correspondence with those of specific molecular dynamics systems. In essence, current coarse-grained simulation methods involve a direct amplification of the results obtained from molecular dynamics simulations across spatial and temporal scales, yet they may lack the capability to adequately capture authentic scale effects. Consequently, the findings of related studies warrant careful re-evaluation. Furthermore, this study underscores the importance of not only verifying the consistency of mesoscale simulation methods with microscopic simulations but also meticulously assessing their capability to accurately forecast mesoscale physical phenomena. Equivalence (formal languages) ; Statistical physics ; Mathematics ; Econometrics ; Computer science ; Physics ; Pure mathematics Block Copolymer Self-Assembly ; Microstructure and mechanical properties ; Theoretical and Computational Physics
Authors: Mees Dieperink | Alexander Skorikov | Nathalie Claes | Sara Bals | Wiebke Albrecht
Source: arXiv (Cornell University) (preprint, 2024-10-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Considerations for Electromagnetic Simulations for a Quantitative Correlation of Optical Spectroscopy and Electron Tomography of Plasmonic Nanoparticles The optical cross sections of plasmonic nanoparticles are intricately linked to the morphology of the particle. If this connection can be made accurately enough, it would become possible to determine a particles shape solely from its measured optical cross sections. For that, electromagnetic simulations can be used to bridge the morphology and optical properties assuming that they can be performed in an accurate manner. In this paper, we study key factors that influence the accuracy of electromagnetic simulations. First, we compare several standard electromagnetic simulation methods and discuss in detail the effects of the meshing accuracy, choice of dielectric function and inclusion of a substrate for the boundary element method. To help the boundary element methods complex parametrization, we develop a workflow including reconstruction, meshing and mesh simplification steps to be able to use electron tomography data as input for these simulations. In particular, we analyze how the choice of reconstruction algorithm and the intricacies of image segmentation influence the simulated optical cross sections and correlate it to induced shape errors, which can be minimized in the data processing pipeline. In our case, optimal results could be obtained by using the Total Variation Minimization (TVM) reconstruction method in combination with Otsu thresholding and slight smoothing, which was important to create a reliable and watertight surface mesh using the marching cubes algorithm, especially for more complex shapes. Plasmon ; Spectroscopy ; Electron ; Plasmonic nanoparticles ; Nanoparticle ; Electron tomography ; Materials science ; Tomography ; Optics ; Physics ; Computational physics ; Nanotechnology ; Nuclear physics ; Quantum mechanics ; Scanning electron microscope Gold and Silver Nanoparticles Synthesis and Applications ; Plasmonic and Surface Plasmon Research
Authors: A. V. Pak | Matthew L. Brown | Paul L. A. Popelier
Source: Acta Crystallographica Section A Foundations and Advances (article, 2024-12-19)
Field: Materials Science — Materials Chemistry

A computationally efficient quasi-harmonic study of ice polymorphs using the FFLUX force field FFLUX is a multipolar machine-learned force field that uses Gaussian process regression models trained on data from quantum chemical topology calculations. It offers an efficient way of predicting both lattice and free energies of polymorphs, allowing their stability to be assessed at finite temperatures. Here the Ih, II and XV phases of ice are studied, building on previous work on formamide crystals and liquid water. A Gaussian process regression model of the water monomer was trained, achieving sub-kJ mol<sup>-1</sup> accuracy. The model was then employed in simulations with a Lennard-Jones potential to represent intermolecular repulsion and dispersion. Lattice constants of the FFLUX-optimized crystal structures were comparable with those calculated by PBE+D3, with FFLUX calculations estimated to be 10<sup>3</sup>-10<sup>5</sup> times faster. Lattice dynamics calculations were performed on each phase, with ices Ih and XV found to be dynamically stable through phonon dispersion curves. However, ice II was incorrectly identified as unstable due to the non-bonded potential used, with a new phase (labelled here as II' and to our knowledge not found experimentally) identified as more stable. This new phase was also found to be dynamically stable using density functional theory but, unlike in FFLUX calculations, II remained the more stable phase. Finally, Gibbs free energies were accessed through the quasi-harmonic approximation for the first time using FFLUX, allowing thermodynamic stability to be assessed at different temperatures and pressures through the construction of a phase diagram. Phase diagram ; Intermolecular force ; Thermodynamics ; Force field (fiction) ; Lattice (music) ; Phonon ; Gaussian ; London dispersion force ; Density functional theory ; Statistical physics ; Chemistry ; Materials science ; Phase (matter) ; Physics ; Condensed matter physics ; Computational chemistry ; Quantum mechanics ; Molecule ; van der Waals force Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Material Dynamics and Properties
Authors: Ibrahim Shomope | Nabil Abdel Jabbar | Ghaleb A. Husseini
Source: AIP conference proceedings (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Support vector regression (SVR) for prediction of ultrasound drug release Over the last decade, increasing interest in drug delivery systems (DDS) has been directed toward the application of machine learning (ML), a sub-field of artificial intelligence (AI), for analyzing and predicting drug release profiles and behavior. In this study, the support vector regressor (SVR) technique was employed using experimental drug release data for the prediction of drug release from Albumin (ALB)-targeted liposomes at different ultrasound power densities (ultrasound is used as a trigger mechanism). The best drug release prediction was obtained at 9 W/cm2 with a predictive accuracy of 0.9992 (99.92%). In addition, to evaluate the performance of this model, four statistical parameters were utilized, i.e., mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE) and the coefficient of determination (R2). These results further complemented the predictive performance of the chosen machine learning technique. Support vector machine ; Regression ; Computer science ; Drug ; Regression analysis ; Machine learning ; Statistics ; Mathematics ; Pharmacology ; Medicine Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Sapta Sindhu Paul Chowdhury | Sourav Thapliyal | Santosh Mogurampelly
Source: arXiv (Cornell University) (preprint, 2024-11-21)
Field: Materials Science — Materials Chemistry

Anomalous Temperature Induced Transition and Convergence of Thermal Conductivity in Germanene Monolayer We report an anomalous temperature-induced transition in thermal conductivity in germanene monolayer around a critical temperature $T_c = 350 \, \text{K}$. Equilibrium molecular dynamics simulations reveal a transition from $κ\sim T^{-2}$ scaling below $T_c$ to $κ\sim T^{-1/2}$ above, contrasting with conventional $κ\sim T^{-1}$ behavior. This anomalous scaling correlates with the long-scale characteristic timescale $τ_2$ obtained from double exponential fitting of the heat current autocorrelation function. Phonon mode analysis using normal mode decomposition indicates that a redshift in ZO phonons reduces the acoustic-optical phonon gap, causing an overlap, enhances the phonon-phonon scattering, driving the anomalous scaling behavior. Moreover, nonequilibrium simulations find a convergent thermal conductivity of germanene with sample size, in agreement with mode coupling theory, owing to the high scattering of ZA phonons due to the inherent buckling of germanene. Germanene ; Monolayer ; Thermal conductivity ; Materials science ; Condensed matter physics ; Transition (genetics) ; Thermal ; Nanotechnology ; Physics ; Chemistry ; Thermodynamics ; Composite material ; Silicene ; Graphene Thermal properties of materials ; Graphene research and applications ; Advanced Physical and Chemical Molecular Interactions
Authors: I.K. Sarma | Alak Chandra Deka - | Nirmal Goswami
Source: International Journal For Multidisciplinary Research (article, 2024-09-12)
Field: Materials Science — Biomaterials

Bio-enzymatic Surface Modification of Banana Fibre and Improvement in Blending Potentiality with Other Natural Fibre Besides the nutritive value of banana fruits the biomass can also be used as natural resources for fibre extraction with indisputable advantages over synthetic reinforcement materials such as low cost, low density, non-toxicity, comparable strength, and minimum waste disposal problem. Microbial pectinolytic enzymes are known to play an important role in a number of industrial processes including degumming of natural fibers. In the present study the microbial and enzymatic retting process continued for optimization of quality banana fibre extraction. The isolated 17 fungal strains having different degree of Pectin degrading properties of which 6 strains also having laccase production properties. Among the strains studied Phoma herbarum and Aspergillius niger showed higher enzymatic production on pectin plate assay. In bio-enzymatic treatment enzyme produce from Phoma herbarum and Aspergillus niger showed higher degumming response at 45ºC with a treatment duration of 6h.The optimization of pectinase production by submerged fermentation by using Plackett-Burman Design. The highest enzyme activities, were recorded as 7.5 (U/ml) from Phoma herbarum (ENZ-5/2h-treatment) shows better results with degumming time six (6) and recorded higher Tenacity (225.155/den) and Toughness (2.206g/den) which was followed by Aspergillus niger (7.0 U/ml, ENZ-1/2h treatment) with Tenacity (147.851/den) and Toughness (2.669g/den). Surface modification ; Materials science ; Natural (archaeology) ; Composite material ; Natural fiber ; Engineering ; Biology ; Chemical engineering ; Fiber Silk-based biomaterials and applications ; Enzyme Production and Characterization ; Electrospun Nanofibers in Biomedical Applications
Authors: Gayatri Patel | Louis‐S. Bouchard
Source: arXiv (Cornell University) (preprint, 2024-08-15)
Field: Materials Science — Biomaterials

Applications of aligned nanofiber for tissue engineering In tissue engineering, we seek to address comprehensive tissue repair and regeneration needs. Aligned nanofibers have emerged as powerful and versatile tools, attributable to their structural and biochemical congruence with the natural extracellular matrix (ECM). This review delineates the contemporary applications of aligned nanofibers in tissue engineering, spotlighting their implementation across musculoskeletal, neural, and cardiovascular tissue domains. The influence of fiber alignment on critical cellular behaviors - cell adhesion, migration, orientation, and differentiation - is reviewed. We also discuss how nanofibers are improved by adding growth factors, peptides, and drugs to help tissues regenerate better. Comprehensive analyses of in vivo trials and clinical studies corroborate the efficacy and safety of these fibers in tissue engineering applications. The review culminates with exploring extant challenges, concurrently charting prospective avenues in aligned nanofiber-centric tissue engineering. Nanofiber ; Tissue engineering ; Nanotechnology ; Materials science ; Engineering ; Computer science ; Biomedical engineering Electrospun Nanofibers in Biomedical Applications ; Tissue Engineering and Regenerative Medicine
Authors: Soumi Haldar | Lorenzo A. Mariano | Alessandro Lunghi | Laura Gagliardi
Source: arXiv (Cornell University) (preprint, 2024-12-10)
Field: Materials Science — Materials Chemistry

The Role of Electron Correlation Beyond the Active Space in Achieving Quantitative Predictions of Spin-Phonon Relaxation Single-molecule magnets (SMMs) are promising candidates for molecular-scale data storage and processing due to their strong magnetic anisotropy and long spin relaxation times. However, as temperature rises, interactions between electronic states and lattice vibrations accelerate spin relaxation, significantly limiting their practical applications. Recently, ab initio simulations have made it possible to advance our understanding of phonon-induced magnetic relaxation, but significant deviations from experiments have often been observed. The description of molecules' electronic structure has been mostly based on complete active space self-consistent field (CASSCF) calculations, and the impact of electron correlation beyond the active space remains largely unexplored. In this study, we provide the first systematic investigation of spin-phonon relaxation in SMMs with post-CASSCF multiconfigurational methods, specifically CAS followed by second-order perturbation theory and multiconfiguration pair-density functional theory. Taking Co(II)- and Dy(III)-based SMMs as case studies, we analyze how electron correlation influences spin-phonon relaxation rates across a range of temperatures, comparing theoretical predictions with experimental observations. Our findings demonstrate that post-CASSCF treatments make it possible to achieve quantitative predictions for Co(II)-based SMMs. For Dy(III)-based systems, however, accurate predictions require consideration of additional effects, underscoring the urgent necessity of further advancing the study of the effects of electronic correlation in these complex systems. Condensed matter physics ; Phonon ; Relaxation (psychology) ; Spin (aerodynamics) ; Physics ; Space (punctuation) ; Electron ; Correlation ; Statistical physics ; Quantum mechanics ; Computer science ; Mathematics ; Medicine Machine Learning in Materials Science ; Electrochemical Analysis and Applications ; Organic and Molecular Conductors Research
Authors: Qi Kang | Jingxin Bao | Ran Li | Yingying Zuo | Yanxia Ye | Hua Huang
Source: International Journal of Concrete Structures and Materials (article, 2024-03-12)
Field: Materials Science — Materials Chemistry

Analysis of the Strength of Different Minerals-Modified MPC Based on Mathematical Models Abstract The study discussed the effects of different mineral incorporations and the curing time on the strength of modified magnesium phosphate cement (MPC) mortars through mechanical tests, mathematical model analysis and microstructure characterization. Fly ash (FA), silica fume (SF), and metakaolin (MK), which exhibit excellent durability and bonding properties, were used to modify the MPC. A quantitative relationship was established between the strength of modified MPC mortars and the mineral incorporation and curing time. First, the strength of each mineral-modified MPC mortar cured in air with different mineral incorporations and curing durations was evaluated. The strengths of MPC mortars containing 10% fly ash, 15% silica fume, and 10% metakaolin—which perform best in their incorporations—were compared to analyze the function of the three minerals. To establish the relationship between strength and mineral incorporation and curing time, three mathematical models, linear model, general nonlinear model, and data distribution shape nonlinear model (DDSNM), are commonly used for material property analysis based on statistics. DDSNM best describes the trend of strength change among the three models and the error is small for three minerals. Based on DDSNM, the influence of various minerals on the strength of MPC mortar was quantitatively evaluated by calculating the variable partial derivatives, and verified by scanning electron microscopy and X-ray diffraction. MK performs the best in improving the flexural strength performance of MPC, while SF performs the best in the compressive strength. FA-MPC has low sensitivity to dosage fluctuations and is easy to prepare. Metakaolin ; Silica fume ; Fly ash ; Materials science ; Curing (chemistry) ; Compressive strength ; Mortar ; Flexural strength ; Composite material ; Cement ; Microstructure ; Durability Magnesium Oxide Properties and Applications
Authors: Yanli Zhao | Anrui Zhang | Huifang Yue | Houqin Wang | Yong Xin | Yi Zhou
Source: Materials (article, 2024-02-20)
Field: Materials Science — Materials Chemistry

Study of Microstructure and Performance Evaluation of Zr-Sn-Nb Joints by Electron Beam Welding In this work, Zr-Sn-Nb alloy was joined by electron beam welding (EBW). A defect-free Zr-Sn-Nb joint with sound appearance was obtained. The grains in the weld zone (WZ) and heat-affected zone (HAZ) are significantly coarsened. The columnar grains with a maximum grain size of 0.5 mm are distributed in the upper region of the WZ, while the equiaxed grains are almost located in the bottom region of the WZ. The WZ is mainly composed of the dominant α-Zr, α'-Zr and a few β phases. The grain orientation of WZ and HAZ is uniform, indicating that no obvious preferred orientation existed. Coarse grains and fine acicular α' phases increase the strength of the joint, but reduce the plasticity and toughness of the joint. The tensile strengths of the joints at room temperature (RT) and 375 °C were 438 MPa and 313 MPa, respectively. The RT impact energy of the joint is 18.5 J, which is only 58.3% of the BM. The high purity of the EBW process and unsignificant grain orientation minimizes damage to the corrosion resistance of Zr-Sn-Nb alloy joints. The corrosion weight gain of the joint specimen and the BM specimen were 12.91 mg/dm<sup>2</sup> and 12.64 mg/dm<sup>2</sup>, respectively, and the thicknesses of the cross-section corrosion layer were 12-15 μm and 9-12 μm, respectively. Materials science ; Equiaxed crystals ; Microstructure ; Electron beam welding ; Ultimate tensile strength ; Metallurgy ; Alloy ; Joint (building) ; Welding ; Grain size ; Heat-affected zone ; Toughness ; Composite material ; Corrosion ; Grain boundary ; Cathode ray ; Electron Nuclear Materials and Properties ; Fusion materials and technologies ; Hydrogen embrittlement and corrosion behaviors in metals