jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Patricia Cazón | Eduardo Morales‐Sánchez | Gonzalo Velázquez | Manuel Vázquez
Source: Journal of Chemical Education (article, 2022-05-31)
Field: Materials Science — Biomaterials

Measurement of the Water Vapor Permeability of Chitosan Films: A Laboratory Experiment on Food Packaging Materials In recent years, the food industry has been undergoing major changes driven by new social and environmental demands. Nowadays, biodegradable packaging is perceived as the most feasible alternative for food packaging. Great efforts are being devoted to the development of new materials from agro-industrial wastes and byproducts that are opening new alternatives and trends in the biopolymer sector. The aim of this laboratory exercise is to provide a tool to motivate active learning of new technologies and trends in the chemistry of biopolymers. The presented guide introduces to undergraduate students to the basic concepts of polysaccharide-based biopolymers and the characterization of functional properties. This exercise is focused on barrier properties, specifically on water vapor permeability. Chitosan is used as an example of a biopolymeric matrix, and its synthesis and water vapor permeability measurement are described in detail. In addition, a guide for the 3D printing of a temperature-controlled permeability chamber is provided, which will allow the experiment to be carried out in less time as the equilibrium state of the system is not broken when the permeability cup is weighed. It is expected that this exercise and the complementary Supporting Information will be of great interest to biopolymer teaching and research laboratories. Biopolymer ; Chitosan ; Permeability (electromagnetism) ; Food packaging ; Water vapor ; Nanotechnology ; Biochemical engineering ; Food industry ; Process engineering ; Chemical engineering ; Materials science ; Chemistry ; Environmental science ; Mechanical engineering ; Engineering ; Polymer ; Composite material ; Food science ; Organic chemistry Nanocomposite Films for Food Packaging ; biodegradable polymer synthesis and properties ; Advanced Sensor and Energy Harvesting Materials
Authors: Yunfei Shi | Pei‐Lin Kang | Cheng Shang | Zhi‐Pan Liu
Source: Journal of the American Chemical Society (article, 2022-07-18)
Field: Materials Science — Materials Chemistry

Methanol Synthesis from CO<sub>2</sub>/CO Mixture on Cu–Zn Catalysts from Microkinetics-Guided Machine Learning Pathway Search Methanol synthesis on industrial Cu/ZnO/Al<sub>2</sub>O<sub>3</sub> catalysts via the hydrogenation of CO and CO<sub>2</sub> mixture, despite several decades of research, is still puzzling due to the nature of the active site and the role of CO<sub>2</sub> in the feed gas. Herein, with the large-scale machine learning atomic simulation, we develop a microkinetics-guided machine learning pathway search to explore thousands of reaction pathways for CO<sub>2</sub> and CO hydrogenations on thermodynamically favorable Cu-Zn surface structures, including Cu(111), Cu(211), and Zn-alloyed Cu(211) surfaces, from which the lowest energy pathways are identified. We find that Zn decorates at the step-edge at Cu(211) up to 0.22 ML under reaction conditions with the Zn-Zn dimeric sites being avoided. CO<sub>2</sub> and CO hydrogenations occur exclusively at the step-edge of the (211) surface with up to 0.11 ML Zn coverage, where the low coverage of Zn (0.11 ML) does not much affect the reaction kinetics, but the higher coverages of Zn (0.22 ML) poison the catalyst. It is CO<sub>2</sub> hydrogenation instead of CO hydrogenation that dominates methanol synthesis, agreeing with previous isotope experiments. While metallic steps are identified as the major active site, we show that the [-Zn-OH-Zn-] chains (cationic Zn) can grow on Cu(111) surfaces under reaction conditions, which suggests the critical role of CO in the mixed gas for reducing the cationic Zn and exposing metal sites for methanol synthesis. Our results provide a comprehensive picture on the dynamic coupling of the feed gas composition, the catalyst active site, and the reaction activity in this complex heterogeneous catalytic system. Chemistry ; Catalysis ; Methanol ; Cationic polymerization ; Metal ; Transition metal ; Zinc ; Inorganic chemistry ; Chemical engineering ; Organic chemistry Machine Learning in Materials Science ; Catalysts for Methane Reforming ; CO2 Reduction Techniques and Catalysts
Authors: Xuping Yang | Chuan Yuan | Sirong He | Ding Jiang | Bin Cao | Shuang Wang
Source: Fuel (article, 2022-08-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning prediction of specific capacitance in biomass derived carbon materials: Effects of activation and biochar characteristics Biochar ; Mean squared error ; Test set ; Gradient boosting ; Linear regression ; Artificial neural network ; Biological system ; Biomass (ecology) ; Capacitance ; Computer science ; Random forest ; Materials science ; Mathematics ; Statistics ; Machine learning ; Chemistry ; Pyrolysis ; Agronomy Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; Electrocatalysts for Energy Conversion
Authors: Alaa Fathy | Yasser M. Sabry | Ian W. Hunter | Diaa Khalil | Tarik Bourouina
Source: Laser & Photonics Review (review, 2022-05-10)
Field: Chemistry — Spectroscopy

Direct Absorption and Photoacoustic Spectroscopy for Gas Sensing and Analysis: A Critical Review Abstract Optical spectroscopy has a broad scientific basis in chemistry, physics, and material science, with diverse applications in medicine, pharmaceuticals, agriculture, and environmental monitoring. Fourier transform infrared (FTIR) spectrometers and tunable laser spectrometers (TLS) are key devices for measuring optical spectra. Superior performance in terms of sensitivity, selectivity, accuracy, and resolution is required for applications in gas sensing. This review deals with gas measurement based on either direct optical absorption spectroscopy or photoacoustic spectroscopy. Both approaches are applicable to FTIR spectroscopy or TLS. In photoacoustic spectroscopy, cantilever‐based photoacoustic spectroscopy is focused due its high performance. A literature survey is conducted revealing the recent technological advances. Theoretical fundamental detection limits are derived for TLS and FTIR, considering both direct absorption and photoacoustic spectroscopies. A theoretical comparison reveals which technology performs better. The minimum normalized absorption coefficient and normalized noise equivalent absorption coefficient appear as key parameters for this comparison. For TLS‐based systems, direct absorption spectroscopy is found to be the best for lower laser power and longer path length. For FTIR‐based systems, direct absorption is found to be the best for low temperature sources, higher spectrometer throughput, faster mirror velocity, and longer gas cells. Photoacoustic spectroscopy ; Fourier transform infrared spectroscopy ; Spectroscopy ; Spectrometer ; Absorption (acoustics) ; Tunable diode laser absorption spectroscopy ; Infrared spectroscopy ; Fourier transform spectroscopy ; Absorption spectroscopy ; Analytical Chemistry (journal) ; Materials science ; Optics ; Chemistry ; Laser ; Tunable laser ; Physics Spectroscopy and Laser Applications ; Atmospheric Ozone and Climate ; Advanced Chemical Sensor Technologies
Authors: Samy Selim | Mha Albqmi | Mohammad M. Al‐Sanea | Taghreed S. Alnusaire | Mohammed S. Almuhayawi | Hamada AbdElgawad | Soad K. Al Jaouni | Amr Elkelish | Shaimaa Hussein | Mona Warrad | Mohamed T. El‐Saadony
Source: Frontiers in Nutrition (review, 2022-11-08)
Field: Chemistry — Organic Chemistry

Valorizing the usage of olive leaves, bioactive compounds, biological activities, and food applications: A comprehensive review Olive oil production is a significant source of economic profit for Mediterranean nations, accounting for around 98 percent of global output. Olive oil usage has increased dramatically in recent years, owing to its organoleptic characteristics and rising knowledge of its health advantages. The culture of olive trees and the manufacture of industrial and table olive oil produces enormous volumes of solid waste and dark liquid effluents, involving olive leaves, pomace, and olive oil mill wastewaters. These by-products cause an economic issue for manufacturers and pose major environmental concerns. As a result, partial reuse, like other agronomical production wastes, is a goal to be achieved. Because these by-products are high in bioactive chemicals, which, if isolated, might denote components with significant added value for the food, cosmetic, and nutraceutical sectors, indeed, they include significant amounts of beneficial organic acids, carbohydrates, proteins, fibers, and phenolic materials, which are distributed differently between the various wastes depending on the olive oil production method and table olive agronomical techniques. However, the extraction and recovery of bioactive materials from chosen by-products is a significant problem of their reasonable value, and rigorous detection and quantification are required. The primary aims of this review in this context are to outline the vital bioactive chemicals in olive by-products, evaluate the main developments in extraction, purification, and identification, and study their uses in food packaging systems and safety problems. Pomace ; Context (archaeology) ; Olive oil ; Nutraceutical ; Value added ; Reuse ; Biotechnology ; Pulp and paper industry ; Environmental science ; Business ; Waste management ; Food science ; Engineering ; Chemistry ; Biology Edible Oils Quality and Analysis ; Phytochemicals and Antioxidant Activities ; Dye analysis and toxicity
Authors: Elisabeth Weyandt | Luigi Leanza | Riccardo Capelli | Giovanni M. Pavan | Ghislaine Vantomme | E. W. Meijer
Source: Nature Communications (article, 2022-01-11)
Field: Materials Science — Biomaterials

Controlling the length of porphyrin supramolecular polymers via coupled equilibria and dilution-induced supramolecular polymerization Multi-component systems often display convoluted behavior, pathway complexity and coupled equilibria. In recent years, several ways to control complex systems by manipulating the subtle balances of interaction energies between the individual components have been explored and thereby shifting the equilibrium between different aggregate states. Here we show the enantioselective chain-capping and dilution-induced supramolecular polymerization with a Zn<sup>2+</sup>-porphyrin-based supramolecular system when going from long, highly cooperative supramolecular polymers to short, disordered aggregates by adding a monotopic Mn<sup>3+</sup>-porphyrin monomer. When mixing the zinc and manganese centered monomers, the Mn<sup>3+</sup>-porphyrins act as chain-cappers for Zn<sup>2+</sup>-porphyrin supramolecular polymers, effectively hindering growth of the copolymer and reducing the length. Upon dilution, the interaction between chain-capper and monomers weakens as the equilibria shift and long supramolecular polymers form again. This dynamic modulation of aggregate morphology and length is achieved through enantioselectivity in the aggregation pathways and concentration-sensitive equilibria. All-atom and coarse-grained molecular simulations provide further insights into the mixing of the species and their exchange dynamics. Our combined experimental and theoretical approach allows for precise control of molecular self-assembly and chiral discrimination in complex systems. Supramolecular chemistry ; Porphyrin ; Supramolecular polymers ; Polymerization ; Monomer ; Dilution ; Polymer ; Chemistry ; Copolymer ; Materials science ; Molecule ; Photochemistry ; Organic chemistry ; Thermodynamics Supramolecular Self-Assembly in Materials ; Porphyrin and Phthalocyanine Chemistry ; Supramolecular Chemistry and Complexes
Authors: Jean‐Paul Booth | Miran Mozetič | Anton Nikiforov | Christian Oehr
Source: Plasma Sources Science and Technology (article, 2022-05-18)
Field: Materials Science — Surfaces, Coatings and Films

Foundations of plasma surface functionalization of polymers for industrial and biological applications Abstract Polymer materials are widely employed in many fields due to the ease with which they can be formed into complex shapes, their versatile mechanical properties, light weight, and low cost. However, many applications are hindered by the chemical compatibility of polymer surfaces, which are generally hydrophobic and bond poorly to other media such as paints, glues, metals and biological media. While polymer surfaces can be treated by wet chemical processes, the aggressive reagents employed are detrimental to the environment, limiting the range of modifications that can be achieved by this route. Plasma functionalization is an attractive alternative, offering great versatility in the processed surface characteristics, and generally using environmentally benign compounds such as rare gases, oxygen and nitrogen, as well as very small quantities of organic precursors. Since the modified surfaces are only a few monolayers thick, these processes are extremely rapid and low in cost. The first industrial process to be developed was plasma oxidation, which increases the surface energy of the polymer, improving the adhesion of paint, glue and metal to the component. Plasma oxidation can be achieved using both low-pressure and atmospheric pressure (APP) discharges. Subsequently, many other processes have emerged, allowing other functional groups to be grafted, including amines, hydroxyl and carboxylic acid groups. Plasma polymerization, starting from gaseous monomers, allows a whole new family of surface chemistries to be created. These processes have many exciting applications in the biomedical field due to the control they give on biocompatibility and selective interaction with living cells. This article will present the fundamentals of plasma interactions with polymers, the plasma devices employed (both at low-pressure and at APP) with their advantages and drawbacks, and a survey of current and future applications. Surface modification ; Polymer ; Reagent ; Biocompatibility ; Chemistry ; Nanotechnology ; Biomolecule ; Plasma polymerization ; Chemical engineering ; Polymerization ; Chemical process ; Monomer ; Materials science ; Organic chemistry Surface Modification and Superhydrophobicity ; Diamond and Carbon-based Materials Research ; Plasma Applications and Diagnostics
Authors: Cheuk Hei Chan | Mingzi Sun | Bolong Huang
Source: EcoMat (article, 2022-03-07)
Field: Materials Science — Materials Chemistry

Application of machine learning for advanced material prediction and design Abstract In material science, traditional experimental and computational approaches require investing enormous time and resources, and the experimental conditions limit the experiments. Sometimes, traditional approaches may not yield satisfactory results for the desired purpose. Therefore, it is essential to develop a new approach to accelerate experimental progress and avoid unnecessary wasting of time and resources. As a data‐driven method, machine learning provides reliable and accurate performance to solve problems in material science. This review first outlines the fundamental information of machine learning. It continues with the research concerning the prediction of various properties of materials by machine learning. Then it discusses the methods for the discovery of new materials and the prediction of their structural information. Finally, we summarize other applications of machine learning in material science. This review will be beneficial for future application of machine learning in more material science research. image Computer science ; Machine learning ; Artificial intelligence ; Data science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Guanjian Cheng | Xin-Gao Gong | Wan‐Jian Yin
Source: Nature Communications (article, 2022-03-21)
Field: Materials Science — Materials Chemistry

Crystal structure prediction by combining graph network and optimization algorithm Crystal structure prediction is a long-standing challenge in condensed matter and chemical science. Here we report a machine-learning approach for crystal structure prediction, in which a graph network (GN) is employed to establish a correlation model between the crystal structure and formation enthalpies at the given database, and an optimization algorithm (OA) is used to accelerate the search for crystal structure with lowest formation enthalpy. The framework of the utilized approach (a database + a GN model + an optimization algorithm) is flexible. We implemented two benchmark databases, i.e., the open quantum materials database (OQMD) and Matbench (MatB), and three OAs, i.e., random searching (RAS), particle-swarm optimization (PSO) and Bayesian optimization (BO), that can predict crystal structures at a given number of atoms in a periodic cell. The comparative studies show that the GN model trained on MatB combined with BO, i.e., GN(MatB)-BO, exhibit the best performance for predicting crystal structures of 29 typical compounds with a computational cost three orders of magnitude less than that required for conventional approaches screening structures through density functional theory calculation. The flexible framework in combination with a materials database, a graph network, and an optimization algorithm may open new avenues for data-driven crystal structural predictions. Crystal structure ; Algorithm ; Particle swarm optimization ; Crystal structure prediction ; Computer science ; Crystal (programming language) ; Optimization algorithm ; Benchmark (surveying) ; Graph ; Machine learning ; Artificial intelligence ; Theoretical computer science ; Mathematics ; Chemistry ; Mathematical optimization ; Crystallography Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Firoozeh Abolhasani Zadeh | Dmitry Olegovich Bokov | Omar Dheyauldeen Salahdin | Walid Kamal Abdelbasset | Mohammed Abed Jawad | Mustafa M. Kadhim | Maytham T. Qasim | Hamzah H. Kzar | Moaed E. Al‐Gazally | Yasser Fakri Mustafa | Mehrdad Khatami
Source: RENDICONTI LINCEI (article, 2022-03-21)
Field: Materials Science — Materials Chemistry

Cytotoxicity evaluation of environmentally friendly synthesis Copper/Zinc bimetallic nanoparticles on MCF-7 cancer cells Bimetallic strip ; Nanoparticle ; Transmission electron microscopy ; Materials science ; Zinc ; Nanotechnology ; Nuclear chemistry ; Nanomedicine ; Copper ; MTT assay ; Drug delivery ; Chemical engineering ; Chemistry ; Metallurgy ; Cell growth ; Biochemistry ; Metal Nanoparticles: synthesis and applications
Authors: Zhe Gao | Guofu Wang | Tingyu Lei | Zhengxing Lv | Mi Xiong | Liancheng Wang | Shuangfeng Xing | Jingyuan Ma | Zheng Jiang | Yong Qin
Source: Nature Communications (article, 2022-01-10)
Field: Materials Science — Materials Chemistry

Enhanced hydrogen generation by reverse spillover effects over bicomponent catalysts The contribution of the reverse spillover effect to hydrogen generation reactions is still controversial. Herein, the promotion functions for reverse spillover in the ammonia borane hydrolysis reaction are proven by constructing a spatially separated NiO/Al<sub>2</sub>O<sub>3</sub>/Pt bicomponent catalyst via atomic layer deposition and performing in situ quick X-ray absorption near-edge structure (XANES) characterization. For the NiO/Al<sub>2</sub>O<sub>3</sub>/Pt catalyst, NiO and Pt nanoparticles are attached to the outer and inner surfaces of Al<sub>2</sub>O<sub>3</sub> nanotubes, respectively. In situ XANES results reveal that for ammonia borane hydrolysis, the H species generated at NiO sites spill across the support to the Pt sites reversely. The reverse spillover effects account for enhanced H<sub>2</sub> generation rates for NiO/Al<sub>2</sub>O<sub>3</sub>/Pt. For the CoO<sub>x</sub>/Al<sub>2</sub>O<sub>3</sub>/Pt and NiO/TiO<sub>2</sub>/Pt catalysts, reverse spillover effects are also confirmed. We believe that an in-depth understanding of the reverse effects will be helpful to clarify the catalytic mechanisms and provide a guide for designing highly efficient catalysts for hydrogen generation reactions. Ammonia borane ; XANES ; Catalysis ; Non-blocking I/O ; Hydrogen spillover ; Materials science ; Hydrogen production ; Hydrogen ; Chemical engineering ; Spillover effect ; Absorption (acoustics) ; Chemistry ; Spectroscopy ; Organic chemistry Hydrogen Storage and Materials ; MXene and MAX Phase Materials ; Ammonia Synthesis and Nitrogen Reduction
Authors: Peder Lyngby | Kristian S. Thygesen
Source: npj Computational Materials (article, 2022-11-11)
Field: Materials Science — Materials Chemistry

Data-driven discovery of 2D materials by deep generative models Abstract Efficient algorithms to generate candidate crystal structures with good stability properties can play a key role in data-driven materials discovery. Here, we show that a crystal diffusion variational autoencoder (CDVAE) is capable of generating two-dimensional (2D) materials of high chemical and structural diversity and formation energies mirroring the training structures. Specifically, we train the CDVAE on 2615 2D materials with energy above the convex hull Δ H hull &lt; 0.3 eV/atom, and generate 5003 materials that we relax using density functional theory (DFT). We also generate 14192 new crystals by systematic element substitution of the training structures. We find that the generative model and lattice decoration approach are complementary and yield materials with similar stability properties but very different crystal structures and chemical compositions. In total we find 11630 predicted new 2D materials, where 8599 of these have Δ H hull &lt; 0.3 eV/atom as the seed structures, while 2004 are within 50 meV of the convex hull and could potentially be synthesised. The relaxed atomic structures of all the materials are available in the open Computational 2D Materials Database (C2DB). Our work establishes the CDVAE as an efficient and reliable crystal generation machine, and significantly expands the space of 2D materials. Generative grammar ; Computer science ; Generative model ; Data science ; Artificial intelligence Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; X-ray Diffraction in Crystallography