jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nurul Kamilah Khairol Anuar | Huey Ling Tan | Ying Pei Lim | Mohamad Sufian So’aib | Noor Fitrah Abu Bakar
Source: Frontiers in Energy Research (review, 2021-04-26)
Field: Materials Science — Materials Chemistry

A Review on Multifunctional Carbon-Dots Synthesized From Biomass Waste: Design/ Fabrication, Characterization and Applications Carbon-Dots (C-Dots) have drawn much attention in recent years owing to their remarkable properties such as high biocompatibility, low toxicity, nano-scale size, and ease of modification with good tuneable photoluminescence performance. These unique properties have led C-Dots to become a promising platform for bioimaging, metal ion sensing and an antibacterial agent. C-Dots can be prepared using the top-down and bottom-up approaches, in which the latter method is commonly used for large scale and low-cost synthesis. C-Dots can be synthesized using sustainable raw materials or green biomass since it is environmentally friendly, in-expensive and most importantly, promotes the minimization of waste production. However, using biomass waste to produce high-quality C-Dots is still a matter of concern waiting for resolution, and this will be the main focus of this review. Fundamental understanding of C-Dots such as structure analysis, physical and chemical properties of C-Dots, various synthesis methodology and type of raw materials used are also discussed and correlated comprehensively. Additionally, factors affecting the bandgap of the C-Dots and the strategies to overcome these shortcomings are also covered. Moreover, formation mechanism of C-Dots focusing on the hydrothermal method, option and challenges to scale up the C-Dots production are explored. It is expected that the great potential of producing C-Dots from agricultural waste a key benefit in view of their versatility in a wide range of applications. Nanotechnology ; Materials science ; Raw material ; Quantum dot ; Characterization (materials science) ; Environmentally friendly ; Biomass (ecology) ; Chemistry Carbon and Quantum Dots Applications ; Quantum Dots Synthesis And Properties ; Nanocluster Synthesis and Applications
Authors: Bankole I. Oladapo | S. Abolfazl Zahedi | Sikiru Oluwarotimi Ismail | Francis T. Omigbodun
Source: Colloids and Surfaces B Biointerfaces (review, 2021-03-27)
Field: Materials Science — Biomaterials

3D printing of PEEK and its composite to increase biointerfaces as a biomedical material- A review Peek ; Materials science ; Biocompatibility ; Nanotechnology ; Composite material ; Polymer biodegradable polymer synthesis and properties ; Bone Tissue Engineering Materials ; Orthopaedic implants and arthroplasty
Authors: Xiang Chen | Xinyan Liu | Xin Shen | Qiang Zhang
Source: Angewandte Chemie International Edition (review, 2021-07-01)
Field: Materials Science — Materials Chemistry

Applying Machine Learning to Rechargeable Batteries: From the Microscale to the Macroscale Emerging machine learning (ML) methods are widely applied in chemistry and materials science studies and have led to a focus on data-driven research. This Minireview summarizes the application of ML to rechargeable batteries, from the microscale to the macroscale. Specifically, ML offers a strategy to explore new functionals for density functional theory calculations and new potentials for molecular dynamics simulations, which are expected to significantly enhance the challenging descriptions of interfaces and amorphous structures. ML also possesses a great potential to mine and unveil valuable information from both experimental and theoretical datasets. A quantitative "structure-function" correlation can thus be established, which can be used to predict the ionic conductivity of solids as well as the battery lifespan. ML also exhibits great advantages in strategy optimization, such as fast-charge procedures. The future combination of multiscale simulations, experiments, and ML is also discussed and the role of humans in data-driven research is highlighted. Microscale chemistry ; Battery (electricity) ; Computer science ; Function (biology) ; Nanotechnology ; Focus (optics) ; Amorphous solid ; Materials science ; Biochemical engineering ; Artificial intelligence ; Chemistry ; Physics ; Engineering ; Mathematics ; Thermodynamics Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Battery Materials and Technologies
Authors: Steven G. Louie | Yang‐Hao Chan | Felipe H. da Jornada | Zhenglu Li | Diana Y. Qiu
Source: Nature Materials (review, 2021-05-27)
Field: Materials Science — Materials Chemistry

Discovering and understanding materials through computation Mesoscopic physics ; Computer science ; Nanotechnology ; Management science ; Data science ; Perspective (graphical) ; Field (mathematics) ; Physics ; Materials science ; Artificial intelligence ; Engineering ; Mathematics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; 2D Materials and Applications
Authors: Daniel Reker | Yulia Rybakova | Ameya R. Kirtane | Ruonan Cao | Jee Won Yang | Natsuda Navamajiti | Apolonia Gardner | Rosanna M. Zhang | Tina Esfandiary | Johanna L’Heureux | Thomas von Erlach | Elena M. Smekalova | Dominique Leboeuf | Kaitlyn Hess | Aaron Lopes | Jaimie Rogner | Joy Collins | Siddartha Tamang | Keiko Ishida | P. Chamberlain | Dong Soo Yun | Abigail K. R. Lytton‐Jean | Christian K. Soule | Jaime H. Cheah | Alison Hayward | Róbert Langer | Giovanni Traverso
Source: Nature Nanotechnology (article, 2021-03-25)
Field: Materials Science — Biomaterials

Computationally guided high-throughput design of self-assembling drug nanoparticles Throughput ; Nanoparticle ; Nanotechnology ; Computer science ; Materials science ; Drug ; Medicine Nanoparticle-Based Drug Delivery ; Computational Drug Discovery Methods ; Nanoparticles: synthesis and applications
Authors: Anna Herrera-Chacón | Xavier Cetó | Manel del Valle
Source: Analytical and Bioanalytical Chemistry (review, 2021-04-30)
Field: Chemistry — Analytical Chemistry

Molecularly imprinted polymers - towards electrochemical sensors and electronic tongues Molecularly imprinted polymer ; Nanotechnology ; Molecular recognition ; Materials science ; Substrate (aquarium) ; Molecular imprinting ; Polymer ; Electrochemistry ; Combinatorial chemistry ; Computer science ; Selectivity ; Chemistry ; Molecule ; Catalysis ; Electrode ; Organic chemistry Analytical chemistry methods development ; Analytical Chemistry and Sensors ; Advanced Chemical Sensor Technologies
Authors: Ke Wang | Alexander W. Dowling
Source: Current Opinion in Chemical Engineering (article, 2021-10-22)
Field: Materials Science — Materials Chemistry

Bayesian optimization for chemical products and functional materials Leverage (statistics) ; Biochemical engineering ; Computer science ; Relevance (law) ; Bayesian probability ; Nanotechnology ; Artificial intelligence ; Engineering ; Materials science Machine Learning in Materials Science ; Process Optimization and Integration ; Computational Drug Discovery Methods
Authors: Seongin Hong | Nicolò Zagni | Sooho Choo | Na Liu | Seungho Baek | Arindam Bala | Hocheon Yoo | Byung Ha Kang | Hyun Jae Kim | Hyung Joong Yun | Muhammad A. Alam | Sunkook Kim
Source: Nature Communications (article, 2021-06-11)
Field: Materials Science — Materials Chemistry

Highly sensitive active pixel image sensor array driven by large-area bilayer MoS2 transistor circuitry Various large-area growth methods for two-dimensional transition metal dichalcogenides have been developed recently for future electronic and photonic applications. However, they have not yet been employed for synthesizing active pixel image sensors. Here, we report on an active pixel image sensor array with a bilayer MoS<sub>2</sub> film prepared via a two-step large-area growth method. The active pixel of image sensor is composed of 2D MoS<sub>2</sub> switching transistors and 2D MoS<sub>2</sub> phototransistors. The maximum photoresponsivity (R<sub>ph</sub>) of the bilayer MoS<sub>2</sub> phototransistors in an 8 × 8 active pixel image sensor array is statistically measured as high as 119.16 A W<sup>-1</sup>. With the aid of computational modeling, we find that the main mechanism for the high R<sub>ph</sub> of the bilayer MoS<sub>2</sub> phototransistor is a photo-gating effect by the holes trapped at subgap states. The image-sensing characteristics of the bilayer MoS<sub>2</sub> active pixel image sensor array are successfully investigated using light stencil projection. Transistor ; Bilayer ; Image sensor ; Pixel ; Optoelectronics ; Materials science ; Computer science ; Nanotechnology ; Electrical engineering ; Chemistry ; Computer vision ; Voltage ; Membrane ; Engineering ; Biochemistry 2D Materials and Applications ; Gas Sensing Nanomaterials and Sensors ; Transition Metal Oxide Nanomaterials
Authors: M.D. Gregory M. Weiss | Jiahui Fan | Mickaël Claudel | Thomas Sonntag | Pascal Didier | Carole Ronzani | Luc Lebeau | Françoise Pons
Source: Journal of Nanobiotechnology (article, 2021-01-06)
Field: Materials Science — Materials Chemistry

Density of surface charge is a more predictive factor of the toxicity of cationic carbon nanoparticles than zeta potential Thus, this study clearly reveals that the surface charge density of a cationic carbon NP rather than the absolute value of its ζ-potential is a relevant descriptor of its in vitro and in vivo toxicity. Zeta potential ; Surface charge ; Toxicity ; Cationic polymerization ; Chemistry ; Oxidative stress ; Viability assay ; Biophysics ; Nanoparticle ; Nuclear chemistry ; Nanotechnology ; Biochemistry ; In vitro ; Materials science ; Biology ; Organic chemistry ; Physical chemistry Nanoparticles: synthesis and applications ; Electrochemical Analysis and Applications ; Heavy Metal Exposure and Toxicity
Authors: Julian A. Hueffel | Theresa Sperger | Ignacio Funes‐Ardoiz | Jas S. Ward | Kari Rissanen | Franziska Schoenebeck
Source: Science (article, 2021-11-25)
Field: Chemistry — Inorganic Chemistry

Accelerated dinuclear palladium catalyst identification through unsupervised machine learning Although machine learning bears enormous potential to accelerate developments in homogeneous catalysis, the frequent need for extensive experimental data can be a bottleneck for implementation. Here, we report an unsupervised machine learning workflow that uses only five experimental data points. It makes use of generalized parameter databases that are complemented with problem-specific in silico data acquisition and clustering. We showcase the power of this strategy for the challenging problem of speciation of palladium (Pd) catalysts, for which a mechanistic rationale is currently lacking. From a total space of 348 ligands, the algorithm predicted, and we experimentally verified, a number of phosphine ligands (including previously never synthesized ones) that give dinuclear Pd<sup>(I)</sup> complexes over the more common Pd<sup>(0)</sup> and Pd<sup>(II)</sup> species. Bottleneck ; Palladium ; Cluster analysis ; Homogeneous ; Computer science ; Identification (biology) ; Catalysis ; Workflow ; Unsupervised learning ; In silico ; Artificial intelligence ; Machine learning ; Combinatorial chemistry ; Chemistry ; Mathematics ; Biology ; Database ; Organic chemistry Asymmetric Hydrogenation and Catalysis ; Machine Learning in Materials Science ; Chemical Synthesis and Analysis
Authors: Alauddin Ahmed | Donald J. Siegel
Source: Patterns (article, 2021-06-24)
Field: Chemistry — Inorganic Chemistry

Predicting hydrogen storage in MOFs via machine learning The H<sub>2</sub> capacities of a diverse set of 918,734 metal-organic frameworks (MOFs) sourced from 19 databases is predicted via machine learning (ML). Using only 7 structural features as input, ML identifies 8,282 MOFs with the potential to exceed the capacities of state-of-the-art materials. The identified MOFs are predominantly hypothetical compounds having low densities (<0.31 g cm<sup>-3</sup>) in combination with high surface areas (>5,300 m<sup>2</sup> g<sup>-1</sup>), void fractions (∼0.90), and pore volumes (>3.3 cm<sup>3</sup> g<sup>-1</sup>). The relative importance of the input features are characterized, and dependencies on the ML algorithm and training set size are quantified. The most important features for predicting H<sub>2</sub> uptake are pore volume (for gravimetric capacity) and void fraction (for volumetric capacity). The ML models are available on the web, allowing for rapid and accurate predictions of the hydrogen capacities of MOFs from limited structural data; the simplest models require only a single crystallographic feature. Hydrogen storage ; Computer science ; Machine learning ; Artificial intelligence ; Chemistry ; Hydrogen ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Alba Collado | David J. Nelson | Steven P. Nolan
Source: Chemical Reviews (review, 2021-07-14)
Field: Chemistry — Organic Chemistry

Optimizing Catalyst and Reaction Conditions in Gold(I) Catalysis–Ligand Development This review considers phosphine and <i>N</i>-heterocyclic carbene complexes of gold(I) that are used as (pre)catalysts for a range of reactions in organic synthesis. These are divided according to the structure of the ligand, with the narrative focusing on studies that offer a quantitative comparison between the ligands and readily available or widely used existing systems. Chemistry ; Catalysis ; Phosphine ; Carbene ; Ligand (biochemistry) ; Combinatorial chemistry ; Reaction conditions ; Organic chemistry ; Nanotechnology Catalytic Alkyne Reactions ; Catalytic Cross-Coupling Reactions ; Asymmetric Hydrogenation and Catalysis
Authors: Nishu Goyal | Frankline Jerold
Source: Environmental Science and Pollution Research (review, 2021-11-25)
Field: Materials Science — Materials Chemistry

Biocosmetics: technological advances and future outlook Cosmetics ; Business ; Biochemical engineering ; Circular economy ; Ingredient ; Raw material ; Product (mathematics) ; New product development ; Risk analysis (engineering) ; Engineering ; Marketing ; Chemistry Nanoparticles: synthesis and applications ; Bioeconomy and Sustainability Development ; Chemistry and Chemical Engineering
Authors: Yang Cheng | Xiang Zhang | Yixiu Qin | Pei Dong | Wei Yao | John Matz | Pulickel M. Ajayan | Jianfeng Shen | Mingxin Ye
Source: Nature Communications (article, 2021-07-02)
Field: Chemistry — Spectroscopy

Super-elasticity at 4 K of covalently crosslinked polyimide aerogels with negative Poisson’s ratio The deep cryogenic temperatures encountered in aerospace present significant challenges for the performance of elastic materials in spacecraft and related apparatus. Reported elastic carbon or ceramic aerogels overcome the low-temperature brittleness in conventional elastic polymers. However, complicated fabrication process and high costs greatly limited their applications. In this work, super-elasticity at a deep cryogenic temperature of covalently crosslinked polyimide (PI) aerogels is achieved based on scalable and low-cost directional dimethyl sulfoxide crystals assisted freeze gelation and freeze-drying strategy. The covalently crosslinked chemical structure, cellular architecture, negative Poisson's ratio (-0.2), low volume shrinkage (3.1%), and ultralow density (6.1 mg/cm<sup>3</sup>) endow the PI aerogels with an elastic compressive strain up to 99% even in liquid helium (4 K), almost zero loss of resilience after dramatic thermal shocks (∆T = 569 K), and fatigue resistance over 5000 times compressive cycles. This work provides a new pathway for constructing polymer-based materials with super-elasticity at deep cryogenic temperature, demonstrating much promise for extensive applications in ongoing and near-future aerospace exploration. Materials science ; Polyimide ; Ceramic ; Polymer ; Composite material ; Elasticity (physics) ; Covalent bond ; Aerogel ; Brittleness ; Rubber elasticity ; Poisson's ratio ; Fabrication ; Chemical engineering ; Layer (electronics) ; Organic chemistry ; Poisson distribution ; Chemistry Aerogels and thermal insulation ; Cellular and Composite Structures ; Silicone and Siloxane Chemistry
Authors: Luca Salvatore | Nunzia Gallo | Maria Lucia Natali | Alberta Terzi | Alessandro Sannino | Marta Madaghiele
Source: Frontiers in Bioengineering and Biotechnology (review, 2021-04-27)
Field: Materials Science — Biomaterials

Mimicking the Hierarchical Organization of Natural Collagen: Toward the Development of Ideal Scaffolding Material for Tissue Regeneration Biological materials found in living organisms, many of which are proteins, feature a complex hierarchical organization. Type I collagen, a fibrous structural protein ubiquitous in the mammalian body, provides a striking example of such a hierarchical material, with peculiar architectural features ranging from the amino acid sequence at the nanoscale (primary structure) up to the assembly of fibrils (quaternary structure) and fibers, with lengths of the order of microns. Collagen plays a dominant role in maintaining the biological and structural integrity of various tissues and organs, such as bone, skin, tendons, blood vessels, and cartilage. Thus, "artificial" collagen-based fibrous assemblies, endowed with appropriate structural properties, represent ideal substrates for the development of devices for tissue engineering applications. In recent years, with the ultimate goal of developing three-dimensional scaffolds with optimal bioactivity able to promote both regeneration and functional recovery of a damaged tissue, numerous studies focused on the capability to finely modulate the scaffold architecture at the microscale and the nanoscale in order to closely mimic the hierarchical features of the extracellular matrix and, in particular, the natural patterning of collagen. All of these studies clearly show that the accurate characterization of the collagen structure at the submolecular and supramolecular levels is pivotal to the understanding of the relationships between the nanostructural/microstructural properties of the fabricated scaffold and its macroscopic performance. Several studies also demonstrate that the selected processing, including any crosslinking and/or sterilization treatments, can strongly affect the architecture of collagen at various length scales. The aim of this review is to highlight the most recent findings on the development of collagen-based scaffolds with optimized properties for tissue engineering. The optimization of the scaffolds is particularly related to the modulation of the collagen architecture, which, in turn, impacts on the achieved bioactivity. Scaffold ; Extracellular matrix ; Regeneration (biology) ; Microscale chemistry ; Materials science ; Cartilage ; Nanotechnology ; Tissue engineering ; Chemistry ; Biomedical engineering ; Anatomy ; Cell biology ; Biology ; Biochemistry Collagen: Extraction and Characterization ; Bone Tissue Engineering Materials ; Electrospun Nanofibers in Biomedical Applications
Authors: Maninder Meenu | Chinmay Kurade | Bala Chakravarthy Neelapu | Sahil Kalra | Hosahalli S. Ramaswamy | Yong Yu
Source: Trends in Food Science & Technology (review, 2021-09-30)
Field: Chemistry — Analytical Chemistry

A concise review on food quality assessment using digital image processing Computer science ; Image processing ; Food quality ; Artificial intelligence ; Machine learning ; Feature extraction ; Quality (philosophy) ; Digital image processing ; Food processing ; Machine vision ; Image (mathematics) ; Food science Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Identification and Quantification in Food
Authors: Rosemary Cranston | Benoît H. Lessard
Source: RSC Advances (review, 2021-01-01)
Field: Materials Science — Materials Chemistry

Metal phthalocyanines: thin-film formation, microstructure, and physical properties Metal phthalocyanines (MPcs) are an abundant class of small molecules comprising of a highly conjugated cyclic structure with a central chelated metal ion. Due to their remarkable chemical, mechanical, and thermal stability MPcs have become popular for a multitude of applications since their discovery in 1907. The potential for peripheral and axial functionalization affords structural tailoring to create bespoke MPc complexes for various next generation applications. Specifically, thin-films of MPcs have found promising utility in medical and electronic applications where the need to understand the relationship between chemical structure and the resulting thin-film properties is an important ongoing field. This review aims to compile the fundamental principles of small molecule thin-film formation by physical vapour deposition and solution processing focusing on the nucleation and growth of crystallites, thermodynamic and kinetic considerations, and effects of deposition parameters on MPc thin-films. Additionally, the structure-property relationship of MPc thin-films is examined by film microstructure, morphology and physical properties. The topics discussed in this work will elucidate the foundations of MPc thin-films and emphasize the critical need for not only molecular design of new MPcs but the role of their processing in the formation of thin-films and how this ultimately governs the performance of the resulting application. Microstructure ; Thin film ; Materials science ; Conjugated system ; Metal ; Phthalocyanine ; Molecule ; Chemical engineering ; Metal-organic framework ; Nanotechnology ; Chemistry ; Polymer ; Organic chemistry ; Metallurgy ; Composite material ; Adsorption Porphyrin and Phthalocyanine Chemistry ; Copper-based nanomaterials and applications ; Organic Electronics and Photovoltaics
Authors: José F. Rodrigues | Larisa Florea | Maria Cristina Ferreira de Oliveira | Dermot Diamond | Osvaldo N. Oliveira
Source: Discover Materials (review, 2021-04-19)
Field: Materials Science — Materials Chemistry

Big data and machine learning for materials science Herein, we review aspects of leading-edge research and innovation in materials science that exploit big data and machine learning (ML), two computer science concepts that combine to yield computational intelligence. ML can accelerate the solution of intricate chemical problems and even solve problems that otherwise would not be tractable. However, the potential benefits of ML come at the cost of big data production; that is, the algorithms demand large volumes of data of various natures and from different sources, from material properties to sensor data. In the survey, we propose a roadmap for future developments with emphasis on computer-aided discovery of new materials and analysis of chemical sensing compounds, both prominent research fields for ML in the context of materials science. In addition to providing an overview of recent advances, we elaborate upon the conceptual and practical limitations of big data and ML applied to materials science, outlining processes, discussing pitfalls, and reviewing cases of success and failure. Big data ; Computer science ; Data science ; Artificial intelligence ; Data mining Machine Learning in Materials Science ; Mineral Processing and Grinding ; Computational Drug Discovery Methods