jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Micha J. Birklbauer | Fränze Müller | Sowmya Sivakumar Geetha | Manuel Matzinger | Karl Mechtler | Viktoria Dorfer
Source: Communications Chemistry (article, 2024-12-19)
Field: Materials Science — Materials Chemistry

Proteome-wide non-cleavable crosslink identification with MS Annika 3.0 reveals the structure of the C. elegans Box C/D complex The field of crosslinking mass spectrometry has seen substantial advancements over the past decades, enabling the structural analysis of proteins and protein complexes and serving as a powerful tool in protein-protein interaction studies. However, data analysis of large non-cleavable crosslink studies is still a mostly unsolved problem due to its n-squared complexity. We here introduce an algorithm for the identification of non-cleavable crosslinks implemented in our crosslinking search engine MS Annika that is based on sparse matrix multiplication and allows for proteome-wide searches on commodity hardware. We compare our algorithm to other state-of-the-art crosslinking search engines commonly used in the field and conclude that MS Annika unifies high sensitivity, accurate FDR estimation and computational performance, outperforming competing tools. Application of this algorithm enabled us to employ a proteome-wide search of C. elegans nuclei samples, where we were able to uncover previously unknown protein interactions and conclude a comprehensive structural analysis that provides a detailed view of the Box C/D complex. Moreover, our algorithm will enable researchers to conduct similar studies that were previously unfeasible. Identification (biology) ; Proteome ; Computational biology ; Chemistry ; Biology ; Bioinformatics Enzyme Structure and Function ; Protein Structure and Dynamics ; Photosynthetic Processes and Mechanisms
Authors: Desouky Abd‐El‐Haleem | Marwa R. Elkatory | Gadallah Abu‐Elreesh
Source: Scientific Reports (article, 2024-11-08)
Field: Materials Science — Biomaterials

Uncovering novel polyhydroxyalkanoate biosynthesis genes and unique pathway in yeast hanseniaspora valbyensis for sustainable bioplastic production This study delves into the exploration of polyhydroxyalkanoate (PHA) biosynthesis genes within wild-type yeast strains, spotlighting the exceptional capabilities of isolate DMG-2. Through meticulous screening, DMG-2 emerged as a standout candidate, showcasing vivid red fluorescence indicative of prolific intracellular PHA granules. Characterization via FTIR spectroscopy unveiled a diverse biopolymer composition within DMG-2, featuring distinct functional groups associated with PHA and polyphosphate. Phylogenetic analysis placed DMG-2 within the Hanseniaspora valbyensis NRRL Y-1626 group, highlighting its distinct taxonomic classification. Subsequent investigation into DMG-2's PHA biosynthesis genes yielded promising outcomes, with successful cloning and efficient PHA accumulation confirmed in transgenic E. coli cells. Protein analysis of ORF1 revealed its involvement in sugar metabolism, supported by its cellular localization and identification of functional motifs. Genomic analysis revealed regulatory elements within ORF1, shedding light on potential splice junctions and transcriptional networks influencing PHA synthesis pathways. Spectroscopic analysis of the biopolymer extracted from transgenic E. coli DMG2-1 provided insights into its co-polymer nature, comprising segments of PHB, PHV, and polyphosphate. GC-MS analysis further elucidated the intricate molecular architecture of the polymer. In conclusion, this study represents a pioneering endeavor in exploring PHA biosynthesis genes within yeast cells, with isolate DMG-2 demonstrating remarkable potential. The findings offer valuable insights for advancing sustainable bioplastic production and hold significant implications for biotechnological applications. Bioplastic ; Polyhydroxyalkanoates ; Biosynthesis ; Yeast ; Gene ; Biology ; Metabolic engineering ; Computational biology ; Biotechnology ; Genetics ; Bacteria ; Ecology biodegradable polymer synthesis and properties ; Biofuel production and bioconversion ; Microplastics and Plastic Pollution
Authors: Kai Zhang | Zhan Wang | Shuying Ma | Chen Wu | Xiaoyang Zhao
Source: Molecules (article, 2024-09-13)
Field: Materials Science — Materials Chemistry

Theoretical Study on the Thermal Decomposition Mechanism of Fe(EDTA)− and Fe(EDTMP)− The decomposition mechanisms of Fe(EDTA)<sup>-</sup> and Fe(EDTMP)<sup>-</sup> complexes, widely used in various industrial applications, were investigated through a theoretical approach. Despite their structural similarities, the phosphonic acid and carboxylic acid groups in these complexes lead to vastly different decomposition behaviors. Fe(EDTA)<sup>-</sup>, stabilized by delocalized π bonds in carboxylic acid groups, exhibited higher stability than that of Fe(EDTMP)<sup>-</sup>, which has only σ bonds in phosphonic acid groups. Interaction Region Indicator (IRI) analysis revealed that the steric hindrance of Fe(EDTMP)<sup>-</sup> was stronger than that of Fe(EDTA)<sup>-</sup>. Ab initio molecular dynamics simulations revealed that Fe(EDTMP)<sup>-</sup> undergoes rapid decomposition due to the ease of breaking P-C bonds and the repulsion between phosphonic acid groups. In contrast, Fe(EDTA)<sup>-</sup> decomposes more slowly. These findings suggest the incorporation of phosphonic acid groups for easier degradation pathways when designing organic acid molecules. Understanding these mechanisms provides a basis for developing strategies for wastewater treatment in industrial settings. Mechanism (biology) ; Thermal decomposition ; Decomposition ; Chemistry ; Environmental chemistry ; Chemical engineering ; Physics ; Engineering ; Organic chemistry ; Quantum mechanics Thermal and Kinetic Analysis ; Energetic Materials and Combustion ; Chemical Thermodynamics and Molecular Structure
Authors: Mohammad Mohammadi Shahir | Mehdi Mirzaei | Maryam Farbodi | Sadra Rafatnia
Source: IET Control Theory and Applications (article, 2024-04-15)
Field: Materials Science — Materials Chemistry

Estimation of shape memory alloy actuator dynamics to design reduced‐order position controller with input saturation Abstract This study focuses on the precise model estimation for a position control problem actuated by a shape memory alloy (SMA) wire. Because the hysteresis characteristic of SMA introduces complexities in system modelling and adds degrees of freedom, a model with reduced order is implemented for controller design. This model is online updated by calculating a complementary term from the measured data to compensate for the SMA actuator dynamics and other parametric uncertainties. The position controller, derived from the formulated reduced‐order model, adapts itself to real conditions and is cost‐effective due to the use of only displacement sensor. The saturation of the control input is modelled within the structure of a constrained optimization problem solved by Karush–Kuhn–Tucker theorem. The boundedness of mean and covariance of tracking error and its derivative is demonstrated by stochastic analysis. The experimental results conducted on a platform incorporating a SMA wire show the efficiency of the proposed system in precisely controlling the position by admissible voltage range. The comparative results with a sliding mode controller indicate higher accuracy for the proposed controller to reduce the effect of uncertainties. Control theory (sociology) ; Controller (irrigation) ; Position (finance) ; Actuator ; Parametric statistics ; SMA* ; Computer science ; Mathematics ; Algorithm ; Control (management) Shape Memory Alloy Transformations ; Piezoelectric Actuators and Control ; Iterative Learning Control Systems
Authors: Zachary M. Sherman | Delia J. Milliron | Thomas M. Truskett
Source: ACS Nano (article, 2024-08-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Distribution of Single-Particle Resonances Determines the Plasmonic Response of Disordered Nanoparticle Ensembles Understanding how colloidal soft materials interact with light is crucial to the rational design of optical metamaterials. Electromagnetic simulations are computationally expensive and have primarily been limited to model systems described by a small number of particles-dimers, small clusters, and small periodic unit cells of superlattices. In this work we study the optical properties of bulk, disordered materials comprising a large number of plasmonic colloidal nanoparticles using Brownian dynamics simulations and the mutual polarization method. We investigate the far-field and near-field optical properties of both colloidal fluids and gels, which require thousands of nanoparticles to describe statistically. We show that these disordered materials exhibit a distribution of particle-level plasmonic resonance frequencies that determines their ensemble optical response. Nanoparticles with similar resonant frequencies form anisotropic and oriented clusters embedded within the otherwise isotropic and disordered microstructures. These collectively resonating morphologies can be tuned with the frequency and polarization of incident light. Knowledge of particle resonant distributions may help to interpret and compare the optical responses of different colloidal structures, correlate and predict optical properties, and rationally design soft materials for applications harnessing light. Plasmon ; Materials science ; Nanoparticle ; Anisotropy ; Metamaterial ; Polarization (electrochemistry) ; Isotropy ; Particle (ecology) ; Brownian dynamics ; Brownian motion ; Chemical physics ; Nanotechnology ; Condensed matter physics ; Physics ; Optoelectronics ; Optics ; Chemistry ; Quantum mechanics Gold and Silver Nanoparticles Synthesis and Applications ; Plasmonic and Surface Plasmon Research ; Orbital Angular Momentum in Optics
Authors: Hongkun Ouyang | Lingling Tang | Jinglong Ma | Tao Pang
Source: Plants (article, 2024-04-22)
Field: Chemistry — Analytical Chemistry

Application of Hyperspectral Technology with Machine Learning for Brix Detection of Pastry Pears Sugar content is an essential indicator for evaluating crisp pear quality and categorization, being used for fruit quality identification and market sales prediction. In this study, we paired a support vector machine (SVM) algorithm with genetic algorithm optimization to reliably estimate the sugar content in crisp pears. We evaluated the spectral data and actual sugar content in crisp pears, then applied three preprocessing methods to the spectral data: standard normal variable transformation (SNV), multivariate scattering correction (MSC), and convolution smoothing (SG). Support vector regression (SVR) models were built using processing approaches. According to the findings, the SVM model preprocessed with convolution smoothing (SG) was the most accurate, with a correlation coefficient 0.0742 higher than that of the raw spectral data. Based on this finding, we used competitive adaptive reweighting (CARS) and the continuous projection algorithm (SPA) to select key representative wavelengths from the spectral data. Finally, we used the retrieved characteristic wavelength data to create a support vector machine model (GASVR) that was genetically tuned. The correlation coefficient of the SG-GASVR model in the prediction set was higher by 0.0321 and the root mean square prediction error (RMSEP) was lower by 0.0267 compared with those of the SG-SVR model. The SG-CARS-GASVR model had the highest correlation coefficient, at 0.8992. In conclusion, the developed SG-CARS-GASVR model provides a reliable method for detecting the sugar content in crisp pear using hyperspectral technology, thereby increasing the accuracy and efficiency of the quality assessment of crisp pear. Pastry ; Hyperspectral imaging ; Brix ; Computer science ; Artificial intelligence ; Food science ; Chemistry ; Sugar Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture
Authors: К. В. Воронин | Georgy A. Ermolaev | Maria G. Burdanova | Aleksandr S. Slavich | Adilet N. Toksumakov | Dmitry I. Yakubovsky | Maksim I. Paukov | Ying Xie | Qian Liu | Daria S. Kopylova | Dmitry V. Krasnikov | Davit Ghazaryan | Denis G. Baranov | A. I. Chernov | Albert G. Nasibulin | Jin Zhang | Aleksey V. Arsenin | Valentyn S. Volkov
Source: Advanced Science (article, 2024-07-31)
Field: Chemistry — Organic Chemistry

Programmable Carbon Nanotube Networks: Controlling Optical Properties Through Orientation and Interaction The lattice geometry of natural materials and the structural geometry of artificial materials are crucial factors determining their physical properties. Most materials have predetermined geometries that lead to fixed physical characteristics. Here, the demonstration of a carbon nanotube network serves as an example of a system with controllable orientation achieving on-demand optical properties. Such a network allows programming their optical response depending on the orientation of the constituent carbon nanotubes and leads to the switching of its dielectric tensor from isotropic to anisotropic. Furthermore, it also allows for the achievement of wavelength-dispersion for their principal optical axes - a recently discovered phenomenon in van der Waals triclinic crystals. The results originate from two unique carbon nanotubes features: uniaxial anisotropy from the well-defined cylindrical geometry and the intersection interaction among individual carbon nanotubes. The findings demonstrate that shaping the relative orientations of carbon nanotubes or other quasi-one-dimensional materials of cylindrical symmetry within a network paves the way to a universal method for the creation of systems with desired optical properties. Carbon nanotube ; Materials science ; Anisotropy ; van der Waals force ; Isotropy ; Orientation (vector space) ; Nanotechnology ; Condensed matter physics ; Chemical physics ; Geometry ; Optics ; Physics ; Mathematics ; Molecule Polydiacetylene-based materials and applications ; Carbon Nanotubes in Composites ; Photonic Crystals and Applications
Authors: Samuel Domínguez-Cid | Diego F. Larios | Julio Barbancho | Francisco Javier Molina | Javier A. Guerra | Carlos León
Source: Sensors (article, 2024-02-20)
Field: Chemistry — Analytical Chemistry

Identification of Olives Using In-Field Hyperspectral Imaging with Lightweight Models During the growing season, olives progress through nine different phenological stages, starting with bud development and ending with senescence. During their lifespan, olives undergo changes in their external color and chemical properties. To tackle these properties, we used hyperspectral imaging during the growing season of the olives. The objective of this study was to develop a lightweight model capable of identifying olives in the hyperspectral images using their spectral information. To achieve this goal, we utilized the hyperspectral imaging of olives while they were still on the tree and conducted this process throughout the entire growing season directly in the field without artificial light sources. The images were taken on-site every week from 9:00 to 11:00 a.m. UTC to avoid light saturation and glitters. The data were analyzed using training and testing classifiers, including Decision Tree, Logistic Regression, Random Forest, and Support Vector Machine on labeled datasets. The Logistic Regression model showed the best balance between classification success rate, size, and inference time, achieving a 98% F1-score with less than 1 KB in parameters. A reduction in size was achieved by analyzing the wavelengths that were critical in the decision making, reducing the dimensionality of the hypercube. So, with this novel model, olives in a hyperspectral image can be identified during the season, providing data to enhance a farmer's decision-making process through further automatic applications. Hyperspectral imaging ; Random forest ; Support vector machine ; Decision tree ; Computer science ; Artificial intelligence ; Remote sensing ; Environmental science ; Pattern recognition (psychology) ; Geography Spectroscopy and Chemometric Analyses ; Edible Oils Quality and Analysis ; Remote Sensing in Agriculture
Authors: Donghyun Kim | Patric Elf | Fritjof Nilsson | Mikael S. Hedenqvist | Anette Larsson
Source: Biomacromolecules (article, 2024-06-24)
Field: Materials Science — Biomaterials

In-Depth Understanding of the Effect of the Distribution of Substituents on the Morphology and Physical Properties of Ethylcellulose: Molecular Dynamics Simulations Insights Ethylcellulose (EC) is a crucial cellulose derivative with widespread applications, particularly in the pharmaceutical industry, where precise property adjustments through chemical modification are imperative. The degree of substitution (DS) and the localization of substituents along the cellulose chains are pivotal factors in this process. However, the impact of the substituent location within the repeating unit of EC remains unexplored. To address this gap, we conducted molecular dynamics simulations on amorphous EC, comparing randomly and uniformly substituted ethyl groups in the repeating units. This comprehensive study of pairwise interactions revealed significant differences in intramolecular and intermolecular hydrogen-bonding capabilities, depending on whether the hydroxyl groups were substituted at C2, C3, or C6. While our simulations demonstrated that substituent localization in the repeating unit influenced the density, number of hydrogen bonds, and conformations, the DS emerged as the dominant determinant. This insight led us to propose and validate a hypothesis: a straightforward linear function using the properties of uniform models and molar fractions can predict the properties of randomly substituted EC with a given DS. This innovative approach is anticipated to contribute to the selection of cellulose derivatives with desirable properties for the pharmaceutical industry and new applications in other fields. Substituent ; Intermolecular force ; Hydrogen bond ; Intramolecular force ; Molecular dynamics ; Chemistry ; Cellulose ; Context (archaeology) ; Amorphous solid ; Computational chemistry ; Stereochemistry ; Crystallography ; Molecule ; Organic chemistry Advanced Cellulose Research Studies ; Lignin and Wood Chemistry ; Biofuel production and bioconversion
Authors: Botond B. Antal | Anthony G. Chesebro | Helmut H. Strey | Lilianne R. Mujica‐Parodi | Corey Weistuch
Source: PLoS Computational Biology (article, 2024-07-18)
Field: Materials Science — Materials Chemistry

Achieving Occam’s razor: Deep learning for optimal model reduction All fields of science depend on mathematical models. Occam's razor refers to the principle that good models should exclude parameters beyond those minimally required to describe the systems they represent. This is because redundancy can lead to incorrect estimates of model parameters from data, and thus inaccurate or ambiguous conclusions. Here, we show how deep learning can be powerfully leveraged to apply Occam's razor to model parameters. Our method, FixFit, uses a feedforward deep neural network with a bottleneck layer to characterize and predict the behavior of a given model from its input parameters. FixFit has three major benefits. First, it provides a metric to quantify the original model's degree of complexity. Second, it allows for the unique fitting of data. Third, it provides an unbiased way to discriminate between experimental hypotheses that add value versus those that do not. In three use cases, we demonstrate the broad applicability of this method across scientific domains. To validate the method using a known system, we apply FixFit to recover known composite parameters for the Kepler orbit model and a dynamic model of blood glucose regulation. In the latter, we demonstrate the ability to fit the latent parameters to real data. To illustrate how the method can be applied to less well-established fields, we use it to identify parameters for a multi-scale brain model and reduce the search space for viable candidate mechanisms. occam ; Occam's razor ; Computer science ; Artificial intelligence ; Bottleneck ; Machine learning ; Metric (unit) ; Deep learning ; Artificial neural network ; Overfitting ; Algorithm ; Mathematics ; Statistics Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Model Reduction and Neural Networks
Authors: Shahin Jalali | Catarina S. P. Borges | Ricardo J. C. Carbas | Eduardo A. S. Marques | Alireza Akhavan‐Safar | Ana Sofia Oliveira Ferreira Barbosa | João C. Bordado | Lucas F. M. da Silva
Source: Materials (article, 2024-01-17)
Field: Materials Science — Polymers and Plastics

A Novel Technique for Substrate Toughening in Wood Single Lap Joints Using a Zero-Thickness Bio-Adhesive In contemporary engineering practices, the utilization of sustainable materials and eco-friendly techniques has gained significant importance. Wooden joints, particularly those created with polyurethan-based bio-adhesives, have garnered significant attention owing to their intrinsic environmental advantages and desirable mechanical properties. In comparison to conventional joining methods, adhesive joints offer distinct benefits such as an enhanced load distribution, reduced stress concentration, and improved aesthetic appeal. In this study, reference and toughened single-lap joint samples were investigated experimentally and numerically under quasi-static loading conditions. The proposed research methodology involves the infusion of a bio-adhesive into the wooden substrate, reinforcing the matrix of its surfaces. This innovative approach was developed to explore a synergetic effect of both wood and bio-adhesive. The experimentally validated results showcase a significant enhancement in joint strength, demonstrating an 85% increase when compared to joints with regular pine substrates. Moreover, the increased delamination thickness observed in toughened joints was found to increase the energy absorption of the joint. Adhesive ; Materials science ; Composite material ; Joint (building) ; Delamination (geology) ; Toughening ; Substrate (aquarium) ; Lap joint ; Structural engineering ; Toughness ; Layer (electronics) Natural Fiber Reinforced Composites ; Wood Treatment and Properties ; Textile materials and evaluations
Authors: Javier Gómez Sánchez | Xoan F. Sánchez–Romate | Francisco Javier Espadas | Silvia G. Prolongo | Alberto Jiménez‐Suárez
Source: Sensors (article, 2024-01-26)
Field: Materials Science — Polymers and Plastics

Electromechanical Properties of Smart Vitrimers Reinforced with Carbon Nanotubes for SHM Applications The Structural Health Monitoring (SHM) capabilities of a well-studied self-healing epoxy resin based on disulfide bonds, through the addition of carbon nanotubes (CNTs), are studied. Since these materials demonstrated, in recent works, a high dependency of the dynamic hardener content on the repair performance, this study aimed to analyze the effect of the vitrimeric chemistry on the electromechanical properties by studying different 2-aminophenyl disulfide (2-AFD) hardener and CNT contents. The electrical conductivity increases with both the CNT and AFD contents, in general. Moreover, an excess of AFD close to the stoichiometric ratio with a low CNT content improved the tensile strength by 45%, while higher AFD contents promoted its detriment by 41% due to a reduced crosslinking density. However, no significant difference in the mechanical properties was observed at a higher CNT content, regardless of the AFD ratio. The developed materials demonstrate a robust electromechanical response at quasi-static conditions. The sensitivity significantly increases at higher AFD ratios, from 0.69 to 2.22 for the 0.2 wt.%. CNT system, which is advantageous due to the enhanced repair performance of these vitrimeric materials with a higher hardener content. These results reveal the potential use of self-healing vitrimers as integrated SHM systems capable of detecting damages and self-repairing autonomously. Carbon nanotube ; Materials science ; Ultimate tensile strength ; Composite material ; Structural health monitoring Conducting polymers and applications ; Polymer composites and self-healing ; Smart Materials for Construction