jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xiao Yang | Wei Zhang | Xuezhi Qin | Miaomiao Cui | Yunting Guo | Ting Wang | Kaiqiang Wang | Zhenqiang Shi | Chao Zhang | Wanbo Li | Zuankai Wang
Source: Biomimetics (review, 2022-07-04)
Field: Materials Science — Surfaces, Coatings and Films

Recent Progress on Bioinspired Antibacterial Surfaces for Biomedical Application Surface bacterial fouling has become an urgent global challenge that calls for resilient solutions. Despite the effectiveness in combating bacterial invasion, antibiotics are susceptible to causing microbial antibiotic resistance that threatens human health and compromises the medication efficacy. In nature, many organisms have evolved a myriad of surfaces with specific physicochemical properties to combat bacteria in diverse environments, providing important inspirations for implementing bioinspired approaches. This review highlights representative natural antibacterial surfaces and discusses their corresponding mechanisms, including repelling adherent bacteria through tailoring surface wettability and mechanically killing bacteria via engineering surface textures. Following this, we present the recent progress in bioinspired active and passive antibacterial strategies. Finally, the biomedical applications and the prospects of these antibacterial surfaces are discussed. Nanotechnology ; Bacteria ; Antibiotics ; Biochemical engineering ; Human health ; Materials science ; Microbiology ; Biology ; Engineering ; Medicine Polymer Surface Interaction Studies ; Surface Modification and Superhydrophobicity ; Graphene and Nanomaterials Applications
Authors: Mohammed Gagaoua | Vânia Zanella Pinto | Gülden Gökşen | Laura Alessandroni | Melisa Lamri | Amira Leila Dib | Fatma Boukid
Source: Coatings (article, 2022-05-08)
Field: Materials Science — Biomaterials

Electrospinning as a Promising Process to Preserve the Quality and Safety of Meat and Meat Products Fresh and processed meat products are staple foods worldwide. However, these products are considered perishable foods and their deterioration depends partly on the inner and external properties of meat. Beyond conventional meat preservation approaches, electrospinning has emerged as a novel effective alternative to develop active and intelligent packaging. Thus, this review aims to discuss the advantages and shortcomings of electrospinning application for quality and safety preservation of meat and processed meat products. Electrospun fibres are very versatile, and their features can be modulated to deliver functional properties such as antioxidant and antimicrobial effects resulting in shelf-life extension and in some cases product quality improvement. Compared to conventional processes, electrospun fibres provide advantages such as casting and coating in the fabrication of active systems, indicators, and sensors. The approaches for improving, stabilizing, and controlling the release of active compounds and highly sensitive, rapid, and reliable responsiveness, under changes in real-time are still challenging for innovative packaging development. Despite their advantages, the active and intelligent electrospun fibres for meat packaging are still restricted to research and not yet widely used for commercial products. Industrial validation of lab-scale achievements of electrospinning might boost their commercialisation. Safety must be addressed by evaluating the impact of electrospun fibres migration from package to foods on human health. This information will contribute into filling knowledge gaps and sustain clear regulations. Electrospinning ; Active packaging ; Food packaging ; Quality (philosophy) ; Process (computing) ; Shelf life ; Biochemical engineering ; Nanotechnology ; Business ; Computer science ; Materials science ; Food science ; Engineering ; Chemistry ; Polymer Electrospun Nanofibers in Biomedical Applications ; Nanocomposite Films for Food Packaging ; Advanced Sensor and Energy Harvesting Materials
Authors: Sheng‐Qi Qiu | Yu Chen | Xiang‐Jun Peng | Shi‐Jiang He | Jun Kee Cheng | Yong‐Bin Wang | Shao‐Hua Xiang | Jun Song | Peiyuan Yu | Junmin Zhang | Bin Tan
Source: Angewandte Chemie International Edition (article, 2022-09-16)
Field: Chemistry — Organic Chemistry

Asymmetric Construction of an Aryl‐Alkene Axis by Palladium‐Catalyzed Suzuki–Miyaura Coupling Reaction The application of Suzuki-Miyaura coupling reaction to forge the atropisomeric biaryls has seen remarkable progress but exploration of this chemistry to directly forge chiral C(aryl)-C(alkene) axis is underdeveloped. The replacement of arene substrates by alkenes intensifies the challenges in terms of reactivity, configurational atropostability of product and selectivity control. By meticulous ligand design and fine-tuning of reaction parameters, we identified a highly active 3,3'-triphenylsilyl-substituted phosphite ligand to realize arene-alkene Suzuki-Miyaura coupling of hindered aryl halides and vinyl boronates under very mild conditions. The axially chiral acyclic aryl-alkenes were generated in commendable efficiency, enantioselectivity and E/Z selectivity. Alkene ; Aryl ; Chemistry ; Palladium ; Selectivity ; Ligand (biochemistry) ; Catalysis ; Aryl halide ; Enantioselective synthesis ; Combinatorial chemistry ; Halide ; Reactivity (psychology) ; Medicinal chemistry ; Organic chemistry ; Alkyl Axial and Atropisomeric Chirality Synthesis ; Molecular spectroscopy and chirality ; Alkaloids: synthesis and pharmacology
Authors: Bapan Pramanik
Source: Gels (review, 2022-09-07)
Field: Materials Science — Biomaterials

Short Peptide-Based Smart Thixotropic Hydrogels Thixotropy is a fascinating feature present in many gel systems that has garnered a lot of attention in the medical field in recent decades. When shear stress is applied, the gel transforms into sol and immediately returns to its original state when resting. The thixotropic nature of the hydrogel has inspired scientists to entrap and release enzymes, therapeutics, and other substances inside the human body, where the gel acts as a drug reservoir and can sustainably release therapeutics. Furthermore, thixotropic hydrogels have been widely used in various therapeutic applications, including drug delivery, cornea regeneration and osteogenesis, to name a few. Because of their inherent biocompatibility and structural diversity, peptides are at the forefront of cutting-edge research in this context. This review will discuss the rational design and self-assembly of peptide-based thixotropic hydrogels with some representative examples, followed by their biomedical applications. Thixotropy ; Self-healing hydrogels ; Biocompatibility ; Context (archaeology) ; Drug delivery ; Nanotechnology ; Materials science ; Computer science ; Biology ; Composite material ; Polymer chemistry Supramolecular Self-Assembly in Materials ; Photochromic and Fluorescence Chemistry ; RNA Interference and Gene Delivery
Authors: Shebeer A. Rahim | Matthew Joseph | T. S. Sampath Kumar | T. Hanas
Source: Frontiers in Materials (article, 2022-02-11)
Field: Materials Science — Biomaterials

Recent Progress in Surface Modification of Mg Alloys for Biodegradable Orthopedic Applications The combination of light weight, strength, biodegradability, and biocompatibility of magnesium (Mg) alloys can soon break the paradigm for temporary orthopedic implants. As the fulfillment of Mg-based implants inside the physiological environment depends on the interaction at the tissue–implant interface, surface modification appears to be a more practical approach to control the rapid degradation rate. This article reviews recent progress on surface modification of Mg-based materials to tailor the degradation rate and biocompatibility for orthopedic applications. A critical analysis of the advantages and limitations of the various surface modification techniques employed are also included for easy reference of the readers. Biocompatibility ; Surface modification ; Materials science ; Biomedical engineering ; Biodegradation ; Degradation (telecommunications) ; Implant ; Nanotechnology ; Magnesium ; Computer science ; Mechanical engineering ; Medicine ; Surgery ; Chemistry ; Engineering ; Metallurgy Magnesium Alloys: Properties and Applications ; Bone Tissue Engineering Materials ; Aluminum Alloys Composites Properties
Authors: Raffaella Petruccelli | G. Bartolini | Tommaso Ganino | Samanta Zelasco | Luca Lombardo | Enzo Perri | Mauro Durante | Rodolfo Bernardi
Source: Plants (review, 2022-05-20)
Field: Chemistry — Organic Chemistry

Cold Stress, Freezing Adaptation, Varietal Susceptibility of Olea europaea L.: A Review Olive (<i>Olea europaea</i> L.) is an evergreen xerophytic tree characterizing vegetative landscape and historical-cultural identity of the Mediterranean Basin. More than 2600 cultivars constitute the rich genetic patrimony of the species cultivated in approximately 60 countries. As a subtropical species, the olive tree is quite sensitive to low temperatures, and air temperature is the most critical environmental factor limiting olive tree growth and production. In this present review, we explored the detrimental effects caused of low temperatures on olive cultivars, and analyzed the most frequently experimental procedures used to evaluate cold stress. Then, current findings freezing stress physiology and gene are summarized in olive tree, with an emphasis on adaptive mechanisms for cold tolerance. This review might clear the way for new research on adaptive mechanisms for cold acclimation and for improvement of olive growing management. Olea ; Evergreen ; Cultivar ; Biology ; Oleaceae ; Adaptation (eye) ; Mediterranean Basin ; Freezing tolerance ; Subtropics ; Olive trees ; Mediterranean climate ; Acclimatization ; Horticulture ; Botany ; Ecology ; Gene Edible Oils Quality and Analysis ; Plant Water Relations and Carbon Dynamics ; Plant Stress Responses and Tolerance
Authors: Qingzhao Chu | Xiaoya Chang | Kang Ma | Xiaolong Fu | Dongping Chen
Source: Physical Chemistry Chemical Physics (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Revealing the thermal decomposition mechanism of RDX crystals by a neural network potential A neural network potential (NNP) is developed to investigate the complex reaction dynamics of 1,3,5-trinitro-1,3,5-triazine (RDX) thermal decomposition. Our NNP model is proven to possess good computational efficiency and retain the <i>ab initio</i> accuracy, which allows the investigation of the entire decomposition process of bulk RDX crystals from an atomic perspective. A series of molecular dynamics (MD) simulations are performed on the NNP to calculate the physical and chemical properties of the RDX crystal. The results show that the NNP can accurately describe the physical properties of RDX crystals, such as the cell parameters and the equation of state. The simulations of RDX thermal decomposition reveal that the NNP could capture the evolution of species at <i>ab initio</i> accuracy. The complex reaction network was established, and a reaction mechanism of RDX decomposition was provided. The N-N homolysis is the dominant channel, which cannot be observed in previous DFT studies of isolated RDX molecule. In addition, the H abstraction reaction by NO<sub>2</sub> is found to be the critical pathway for NO and H<sub>2</sub>O formation, while the HONO elimination is relatively weak. The NNP gives an atomic insight into the complex reaction dynamics of RDX and can be extended to investigate the reaction mechanism of novel energetic materials. Thermal decomposition ; Mechanism (biology) ; Artificial neural network ; Decomposition ; Thermal ; Materials science ; Chemical physics ; Chemistry ; Chemical engineering ; Nanotechnology ; Computer science ; Thermodynamics ; Physics ; Artificial intelligence ; Organic chemistry ; Engineering Thermal and Kinetic Analysis ; Energetic Materials and Combustion ; Machine Learning in Materials Science
Authors: Baicheng Zhang | Xiaolong Zhang | Wenjie Du | Zhaokun Song | Guozhen Zhang | Guoqing Zhang | Yang Wang | Xin Chen | Jun Jiang | Yi Luo
Source: Proceedings of the National Academy of Sciences (article, 2022-10-03)
Field: Materials Science — Materials Chemistry

Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning Infusing "chemical wisdom" should improve the data-driven approaches that rely exclusively on historical synthetic data for automatic retrosynthesis planning. For this purpose, we designed a chemistry-informed molecular graph (CIMG) to describe chemical reactions. A collection of key information that is most relevant to chemical reactions is integrated in CIMG:NMR chemical shifts as vertex features, bond dissociation energies as edge features, and solvent/catalyst information as global features. For any given compound as a target, a product CIMG is generated and exploited by a graph neural network (GNN) model to choose reaction template(s) leading to this product. A reactant CIMG is then inferred and used in two GNN models to select appropriate catalyst and solvent, respectively. Finally, a fourth GNN model compares the two CIMG descriptors to check the plausibility of the proposed reaction. A reaction vector is obtained for every molecule in training these models. The chemical wisdom of reaction propensity contained in the pretrained reaction vectors is exploited to autocategorize molecules/reactions and to accelerate Monte Carlo tree search (MCTS) for multistep retrosynthesis planning. Full synthetic routes with recommended catalysts/solvents are predicted efficiently using this CIMG-based approach. Retrosynthetic analysis ; Computer science ; Molecule ; Artificial intelligence ; Dissociation (chemistry) ; Monte Carlo method ; Graph ; Chemistry ; Template ; Machine learning ; Biological system ; Data mining ; Theoretical computer science ; Mathematics ; Organic chemistry ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Won Jin Choi | Sang Hyun Lee | Bum Chul Park | Nicholas A. Kotov
Source: Journal of the American Chemical Society (review, 2022-12-09)
Field: Chemistry — Spectroscopy

Terahertz Circular Dichroism Spectroscopy of Molecular Assemblies and Nanostructures Chemical, physical, biological and materials engineering disciplines use a variety of chiroptical spectroscopies to probe geometrical and optical asymmetry in molecules and particles. Electronic (ECD) and vibrational (VCD) circular dichroism are the most common of these techniques and collectively enable the studies of electronic and vibronic transitions with energies between 0.1 and 5.0 eV. The vibrational states with characteristic energies in the range of 0.001-0.01 eV carry valuable information about concerted intermolecular motions in molecules and crystals involving multiple atoms. These vibronic transitions located in the terahertz (THz) part of the spectrum become increasingly more important for the chemistry, physics, and biology of complex molecules and materials However, the methodology and hardware of THz circular dichroism (TCD) are much less developed than the chiroptical spectroscopies for ultraviolet, visible, near- and mid infrared photons. Here we provide theoretical foundations, practical implementations, comparative assessments, and exemplary applications of TCD spectroscopy. We show that the sign, intensity, and position of TCD peaks are highly sensitive to the three-dimensional structure and long-range organization of molecular crystals, which offer unique capabilities to study (bio) molecules, their crystals, and nanoscale assemblies and apply the novel data processing methodologies. TCD also offers a convenient toolbox to identify new physical phenomena, such as chiral phonons and their propagation in nanostructured matter. We also discuss the major challenges, emerging opportunities and promising research directions, including broad investigation of chiral phonons observed in chiral (nano) crystals and emerging machine learning methodologies for TCD in biological and nanoscale structures. Ubiquity of low-frequency vibrations with rotational components in biomolecular structures, combined with sharpness of peaks in TCD spectra, enables a variety of technological translations. Spectroscopy ; Chemistry ; Terahertz radiation ; Chemical physics ; Vibrational circular dichroism ; Circular dichroism ; Phonon ; Intermolecular force ; Molecule ; Nanostructure ; Nanotechnology ; Vibronic spectroscopy ; Materials science ; Physics ; Optoelectronics ; Crystallography ; Condensed matter physics ; Quantum mechanics ; Organic chemistry Molecular spectroscopy and chirality ; Terahertz technology and applications ; Spectroscopy and Quantum Chemical Studies
Authors: Lixin Sun | Jonathan Vandermause | Simon Batzner | Yu Xie | David Clark | Wei Chen | Boris Kozinsky
Source: Journal of Chemical Theory and Computation (article, 2022-03-11)
Field: Materials Science — Materials Chemistry

Multitask Machine Learning of Collective Variables for Enhanced Sampling of Rare Events Computing accurate reaction rates is a central challenge in computational chemistry and biology because of the high cost of free energy estimation with unbiased molecular dynamics. In this work, a data-driven machine learning algorithm is devised to learn collective variables with a multitask neural network, where a common upstream part reduces the high dimensionality of atomic configurations to a low dimensional latent space and separate downstream parts map the latent space to predictions of basin class labels and potential energies. The resulting latent space is shown to be an effective low-dimensional representation, capturing the reaction progress and guiding effective umbrella sampling to obtain accurate free energy landscapes. This approach is successfully applied to model systems including a 5D Müller Brown model, a 5D three-well model, the alanine dipeptide in vacuum, and an Au(110) surface reconstruction unit reaction. It enables automated dimensionality reduction for energy controlled reactions in complex systems, offers a unified and data-efficient framework that can be trained with limited data, and outperforms single-task learning approaches, including autoencoders. Computer science ; Dimensionality reduction ; Adaptive sampling ; Artificial intelligence ; Curse of dimensionality ; Chemical space ; Machine learning ; Artificial neural network ; Chemistry ; Mathematics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Protein Structure and Dynamics
Authors: Karteek K. Bejagam | Jessica Lalonde | Carl N. Iverson | Babetta L. Marrone | Ghanshyam Pilania
Source: The Journal of Physical Chemistry B (article, 2022-01-24)
Field: Materials Science — Materials Chemistry

Machine Learning for Melting Temperature Predictions and Design in Polyhydroxyalkanoate-Based Biopolymers Diminishing fossil fuel-based resources and ever-growing environmental concerns related to plastic pollution demand for the development of sustainable and biodegradable polymeric material alternatives. Polyhydroxyalkanoates (PHAs) represent an eco-friendly and economically viable class of polymers with a wide range of applications. However, the chemical diversity combined with tunable physical properties available within PHAs poses discovery and optimization challenges with respect to identifying optimal application-specific chemical compositions. Here we use an example of melting temperature (<i>T</i><sub>m</sub>) prediction to demonstrate the promise of machine learning (ML)-based techniques for establishing efficient structure-property mappings in PHA-based chemical space. We employ a manually curated data set of experimentally measured <i>T</i><sub>m</sub> values for a wide range of PHA homo- and copolymer chemistries along with their reported polymer molecular weights and polydispersity indices. Descriptors based on topology, shape, and charge/polarity of specific motifs forming the polymer backbone were then used to numerically represent the polymers. The ML models developed by using available data were used to rapidly predict the property of multicomponent PHA-based copolymers, while estimating uncertainties underlying the predictions. Combined with a previously developed glass transition temperature (<i>T</i><sub>g</sub>) prediction model and an evolutionary algorithm-based search strategy, the approach is demonstrated to address polymer design with multiobjective optimization challenges. Polyhydroxyalkanoates ; Melting temperature ; Materials science ; Biopolymer ; Biochemical engineering ; Process engineering ; Chemical engineering ; Nanotechnology ; Chemistry ; Polymer science ; Engineering ; Composite material ; Polymer ; Geology ; Bacteria Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Additive Manufacturing and 3D Printing Technologies
Authors: Nayely Pineda-Romero | Matthew Witman | Vitalie Stavila | Claudia Zlotea
Source: Intermetallics (article, 2022-05-11)
Field: Materials Science — Materials Chemistry

The effect of 10 at.% Al addition on the hydrogen storage properties of the Ti0.33V0.33Nb0.33 multi-principal element alloy Hydrogen storage ; Alloy ; Ternary operation ; Desorption ; Hydride ; Materials science ; Hydrogen ; Chemical engineering ; Phase (matter) ; Metallurgy ; Chemistry ; Adsorption ; Physical chemistry ; Computer science ; Metal ; Organic chemistry Hydrogen Storage and Materials ; MXene and MAX Phase Materials ; Nuclear Materials and Properties
Authors: Fanil T. Latypov | E. V. Fomin | Vasiliy S. Krasnikov | Alexander E. Mayer
Source: International Journal of Plasticity (article, 2022-06-10)
Field: Materials Science — Materials Chemistry

Dynamic compaction of aluminum with nanopores of varied shape: MD simulations and machine-learning-based approximation of deformation behavior Materials science ; Nanopore ; Nanoporous ; Deformation (meteorology) ; Deformation mechanism ; Porosity ; Anisotropy ; Composite material ; Nucleation ; Dislocation ; Mechanics ; Compaction ; Porous medium ; Nanotechnology ; Thermodynamics ; Optics ; Physics ; Microstructure Microstructure and mechanical properties ; Advanced ceramic materials synthesis ; Aluminum Alloys Composites Properties