jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Azizatul Karimah | Muhammad Ridho | Sasa Sofyan Munawar | Ismadi Ismadi | Yusup Amin | Ratih Damayanti | Muhammad Adly Rahandi Lubis | Asri Peni Wulandari | Nurindah Nurindah | Apri Heri Iswanto | Ahmad Fudholi | Mochamad Asrofi | Euis Saedah | Nasmi Herlina Sari | Bayu Rizky Pratama | Widya Fatrıasarı | Deded Sarip Nawawi | Sanjay Mavinkere Rangappa | Suchart Siengchin
Source: Polymers (review, 2021-12-07)
Field: Materials Science — Polymers and Plastics

A Comprehensive Review on Natural Fibers: Technological and Socio-Economical Aspects Asian countries have abundant resources of natural fibers, but unfortunately, they have not been optimally utilized. The facts showed that from 2014 to 2020, there was a shortfall in meeting national demand of over USD 2.75 million per year. Therefore, in order to develop the utilization and improve the economic potential as well as the sustainability of natural fibers, a comprehensive review is required. The study aimed to demonstrate the availability, technological processing, and socio-economical aspects of natural fibers. Although many studies have been conducted on this material, it is necessary to revisit their potential from those perspectives to maximize their use. The renewability and biodegradability of natural fiber are part of the fascinating properties that lead to their prospective use in automotive, aerospace industries, structural and building constructions, bio packaging, textiles, biomedical applications, and military vehicles. To increase the range of applications, relevant technologies in conjunction with social approaches are very important. Hence, in the future, the utilization can be expanded in many fields by considering the basic characteristics and appropriate technologies of the natural fibers. Selecting the most prospective natural fiber for creating national products can be assisted by providing an integrated management system from a digitalized information on potential and related technological approaches. To make it happens, collaborations between stakeholders from the national R&D agency, the government as policy maker, and academic institutions to develop national bioproducts based on domestic innovation in order to move the circular economy forward are essential. Bioproducts ; Order (exchange) ; Government (linguistics) ; Natural (archaeology) ; Automotive industry ; Agency (philosophy) ; Natural resource ; Sustainability ; Business ; Aerospace ; Engineering ; Environmental economics ; Risk analysis (engineering) ; Economics ; Waste management ; Ecology Natural Fiber Reinforced Composites ; Additive Manufacturing and 3D Printing Technologies ; Advanced Cellulose Research Studies
Authors: Marjan Majdinasab | Mona Daneshi | Jean‐Louis Marty
Source: Talanta (review, 2021-04-22)
Field: Chemistry — Analytical Chemistry

Recent developments in non-enzymatic (bio)sensors for detection of pesticide residues: Focusing on antibody, aptamer and molecularly imprinted polymer Molecularly imprinted polymer ; Aptamer ; Biosensor ; Chemistry ; Nanotechnology ; Molecular imprinting ; Pesticide ; Biochemical engineering ; Materials science ; Molecular biology ; Biochemistry Analytical chemistry methods development ; Advanced biosensing and bioanalysis techniques ; Electrochemical sensors and biosensors
Authors: Florian Meier | Niklas Köhler | Andreas‐David Brunner | Jean-Marc H. Wanka | Eugenia Voytik | Maximilian T. Strauss | Fabian J. Theis | Matthias Mann
Source: Nature Communications (article, 2021-02-19)
Field: Chemistry — Spectroscopy

Deep learning the collisional cross sections of the peptide universe from a million experimental values The size and shape of peptide ions in the gas phase are an under-explored dimension for mass spectrometry-based proteomics. To investigate the nature and utility of the peptide collisional cross section (CCS) space, we measure more than a million data points from whole-proteome digests of five organisms with trapped ion mobility spectrometry (TIMS) and parallel accumulation-serial fragmentation (PASEF). The scale and precision (CV < 1%) of our data is sufficient to train a deep recurrent neural network that accurately predicts CCS values solely based on the peptide sequence. Cross section predictions for the synthetic ProteomeTools peptides validate the model within a 1.4% median relative error (R > 0.99). Hydrophobicity, proportion of prolines and position of histidines are main determinants of the cross sections in addition to sequence-specific interactions. CCS values can now be predicted for any peptide and organism, forming a basis for advanced proteomics workflows that make full use of the additional information. Peptide ; Fragmentation (computing) ; Proteome ; Mass spectrometry ; Sequence (biology) ; Proteomics ; Computer science ; Peptide sequence ; Computational biology ; Physics ; Chemistry ; Bioinformatics ; Biology ; Biochemistry ; Chromatography Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Muhammad Irfan Maulana Kusdhany | Stephen Matthew Lyth
Source: Carbon (article, 2021-04-14)
Field: Materials Science — Materials Chemistry

New insights into hydrogen uptake on porous carbon materials via explainable machine learning To understand hydrogen uptake in porous carbon materials, we developed machine learning models to predict excess uptake at 77 K based on the textural and chemical properties of carbon, using a dataset containing 68 different samples and 1745 data points. Random forest is selected due to its high performance (R2 > 0.9), and analysis is performed using Shapley Additive Explanations (SHAP). It is found that pressure and Brunauer-Emmett-Teller (BET) surface area are the two strongest predictors of excess hydrogen uptake. Surprisingly, this is followed by a positive correlation with oxygen content, contributing up to ∼0.6 wt% additional hydrogen uptake, contradicting the conclusions of previous studies. Finally, pore volume has the smallest effect. The pore size distribution is also found to be important, since ultramicropores (dp < 0.7 nm) are found to be more positively correlated with excess uptake than micropores (dp < 2 nm). However, this effect is quite small compared to the role of BET surface area and total pore volume. The novel approach taken here can provide important insights in the rational design of carbon materials for hydrogen storage applications. Hydrogen ; Carbon fibers ; BET theory ; Porosity ; Volume (thermodynamics) ; Oxygen ; Specific surface area ; Chemistry ; Chemical engineering ; Materials science ; Thermodynamics ; Composite material ; Physical chemistry ; Organic chemistry ; Adsorption ; Physics Hydrogen Storage and Materials ; Advancements in Battery Materials ; Electrocatalysts for Energy Conversion
Authors: Cheng-May Fung | Chen-Chen Er | Lling-Lling Tan | Abdul Rahman Mohamed | Siang-Piao Chai
Source: Chemical Reviews (article, 2021-12-30)
Field: Materials Science — Materials Chemistry

Red Phosphorus: An Up-and-Coming Photocatalyst on the Horizon for Sustainable Energy Development and Environmental Remediation Photocatalysis is a perennial solution that promises to resolve deep-rooted challenges related to environmental pollution and energy deficit through harvesting the inexhaustible and renewable solar energy. To date, a cornucopia of photocatalytic materials has been investigated with the research wave presently steered by the development of novel, affordable, and effective metal-free semiconductors with fascinating physicochemical and semiconducting characteristics. Coincidentally, the recently emerged red phosphorus (RP) semiconductor finds itself fitting perfectly into this category ascribed to its earth abundant, low-cost, and metal-free nature. More notably, the renowned red allotrope of the phosphorus family is spectacularly bestowed with strengthened optical absorption features, propitious electronic band configuration, and ease of functionalization and modification as well as high stability. Comprehensively detailing RP's roles and implications in photocatalysis, this review article will first include information on different RP allotropes and their chemical structures, followed by the meticulous scrutiny of their physicochemical and semiconducting properties such as electronic band structure, optical absorption features, and charge carrier dynamics. Besides that, state-of-the-art synthesis strategies for developing various RP allotropes and RP-based photocatalytic systems will also be outlined. In addition, modification or functionalization of RP with other semiconductors for promoting effective photocatalytic applications will be discussed to assess its versatility and feasibility as a high-performing photocatalytic system. Lastly, the challenges facing RP photocatalysts and future research directions will be included to propel the feasible development of RP-based systems with considerably augmented photocatalytic efficiency. This review article aspires to facilitate the rational development of multifunctional RP-based photocatalytic systems by widening the cognizance of rational engineering as well as to fine-tune the electronic, optical, and charge carrier properties of RP. Photocatalysis ; Semiconductor ; Solar energy conversion ; Absorption (acoustics) ; Surface modification ; Renewable energy ; Solar energy ; Environmental remediation 2D Materials and Applications ; Advanced Photocatalysis Techniques ; Polyoxometalates: Synthesis and Applications
Authors: Elena Blundo | E. Cappelluti | Marco Felici | Giorgio Pettinari | A. Polimeni
Source: Applied Physics Reviews (article, 2021-06-01)
Field: Materials Science — Materials Chemistry

Strain-tuning of the electronic, optical, and vibrational properties of two-dimensional crystals The variegated family of two-dimensional (2D) crystals has developed rapidly since the isolation of its forerunner: Graphene. Their plane-confined nature is typically associated with exceptional and peculiar electronic, optical, magnetic, and mechanical properties, heightening the interest of fundamental science and showing promise for applications. Methods for tuning their properties on demand have been pursued, among which the application of mechanical stresses, allowed by the incredible mechanical robustness and flexibility of these atomically thin materials. Great experimental and theoretical efforts have been focused on the development of straining protocols and on the evaluation of their impact on the peculiar properties of 2D crystals, revealing a novel, alluring physics. The relevance held by strain for 2D materials is introduced in Sec. I. Sections II and III present the multiplicity of methods developed to induce strain, highlighting the peculiarities, effectiveness, and drawbacks of each technique. Strain has largely widened the 2D material phase space in a quasi-seamless manner, leading to new and rich scenarios, which are discussed in Secs. IV–VI of this work. The effects of strain on the electronic, optical, vibrational, and mechanical properties of 2D crystals are discussed, as well as the possibility to exploit strain gradients for single-photon emission, non-linear optics, or valley/spintronics. Quantitative surveys of the relevant parameters governing these phenomena are provided. This review seeks to provide a comprehensive state-of-the-art overview of the straining methods and strain-induced effects, and to shed light on possible future paths. The aims and developments, the tools and strategies, and the achievements and challenges of this research field are widely presented and discussed. Spintronics ; Materials science ; Graphene ; Nanotechnology ; Electronics ; Photon ; Condensed matter physics ; Engineering physics ; Optics ; Physics ; Chemistry ; Ferromagnetism 2D Materials and Applications ; MXene and MAX Phase Materials ; Graphene research and applications
Authors: Nathan J. Szymanski | Yan Zeng | Haoyan Huo | Christopher J. Bartel | Haegyeom Kim | Gerbrand Ceder
Source: Materials Horizons (review, 2021-01-01)
Field: Materials Science — Materials Chemistry

Toward autonomous design and synthesis of novel inorganic materials Autonomous experimentation driven by artificial intelligence (AI) provides an exciting opportunity to revolutionize inorganic materials discovery and development. Herein, we review recent progress in the design of self-driving laboratories, including robotics to automate materials synthesis and characterization, in conjunction with AI to interpret experimental outcomes and propose new experimental procedures. We focus on efforts to automate inorganic synthesis through solution-based routes, solid-state reactions, and thin film deposition. In each case, connections are made to relevant work in organic chemistry, where automation is more common. Characterization techniques are primarily discussed in the context of phase identification, as this task is critical to understand what products have formed during synthesis. The application of deep learning to analyze multivariate characterization data and perform phase identification is examined. To achieve "closed-loop" materials synthesis and design, we further provide a detailed overview of optimization algorithms that use active learning to rationally guide experimental iterations. Finally, we highlight several key opportunities and challenges for the future development of self-driving inorganic materials synthesis platforms. Nanotechnology ; Materials science ; Systems engineering ; Engineering Electronic and Structural Properties of Oxides ; Machine Learning in Materials Science ; Advanced Memory and Neural Computing
Authors: Teng Long | Nuno M. Fortunato | Ingo Opahle | Yixuan Zhang | Ilias Samathrakis | Chen Shen | Oliver Gutfleisch | Hongbin Zhang
Source: npj Computational Materials (article, 2021-05-10)
Field: Materials Science — Materials Chemistry

Constrained crystals deep convolutional generative adversarial network for the inverse design of crystal structures Abstract Autonomous materials discovery with desired properties is one of the ultimate goals for materials science, and the current studies have been focusing mostly on high-throughput screening based on density functional theory calculations and forward modeling of physical properties using machine learning. Applying the deep learning techniques, we have developed a generative model, which can predict distinct stable crystal structures by optimizing the formation energy in the latent space. It is demonstrated that the optimization of physical properties can be integrated into the generative model as on-top screening or backward propagator, both with their own advantages. Applying the generative models on the binary Bi-Se system reveals that distinct crystal structures can be obtained covering the whole composition range, and the phases on the convex hull can be reproduced after the generated structures are fully relaxed to the equilibrium. The method can be extended to multicomponent systems for multi-objective optimization, which paves the way to achieve the inverse design of materials with optimal properties. Computer science ; Generative grammar ; Inverse ; Binary number ; Generative model ; Convex hull ; Range (aeronautics) ; Crystal (programming language) ; Artificial intelligence ; Mathematical optimization ; Algorithm ; Theoretical computer science ; Mathematics ; Regular polygon ; Materials science ; Geometry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Shuwen Yue | Maria Carolina Muniz | Marcos F. Calegari Andrade | Linfeng Zhang | Roberto Car | Athanassios Z. Panagiotopoulos
Source: The Journal of Chemical Physics (article, 2021-01-21)
Field: Materials Science — Materials Chemistry

When do short-range atomistic machine-learning models fall short? We explore the role of long-range interactions in atomistic machine-learning models by analyzing the effects on fitting accuracy, isolated cluster properties, and bulk thermodynamic properties. Such models have become increasingly popular in molecular simulations given their ability to learn highly complex and multi-dimensional interactions within a local environment; however, many of them fundamentally lack a description of explicit long-range interactions. In order to provide a well-defined benchmark system with precisely known pairwise interactions, we chose as the reference model a flexible version of the Extended Simple Point Charge (SPC/E) water model. Our analysis shows that while local representations are sufficient for predictions of the condensed liquid phase, the short-range nature of machine-learning models falls short in representing cluster and vapor phase properties. These findings provide an improved understanding of the role of long-range interactions in machine learning models and the regimes where they are necessary. Pairwise comparison ; Benchmark (surveying) ; Range (aeronautics) ; Cluster (spacecraft) ; Computer science ; Statistical physics ; Artificial intelligence ; Machine learning ; Phase (matter) ; Physics ; Materials science Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Jianbo Zhu | Xuemei Zhang | Muchun Guo | Jingyu Li | Jinsuo Hu | Songting Cai | Wei Cai | Yongsheng Zhang | Jiehe Sui
Source: npj Computational Materials (article, 2021-07-22)
Field: Materials Science — Materials Chemistry

Restructured single parabolic band model for quick analysis in thermoelectricity Abstract The single parabolic band (SPB) model has been widely used to preliminarily elucidate inherent transport behaviors of thermoelectric (TE) materials, such as their band structure and electronic thermal conductivity, etc. However, in the SPB calculation, it is necessary to determine some intermediate variables, such as Fermi level or the complex Fermi-Dirac integrals. In this work, we establish a direct carrier-concentration-dependent restructured SPB model, which eliminates Fermi-Dirac integrals and Fermi level calculation and emerges stronger visibility and usability in experiments. We have verified the reliability of such restructured model with 490 groups of experimental data from state-of-the-art TE materials and the relative error is less than 2%. Moreover, carrier effective mass, intrinsic carrier mobility and optimal carrier concentration of these materials are systematically investigated. We believe that our work can provide more convenience and accuracy for thermoelectric data analysis as well as instructive understanding on future optimization design. Work (physics) ; Thermoelectric effect ; Fermi level ; Seebeck coefficient ; Reliability (semiconductor) ; Effective mass (spring–mass system) ; Fermi Gamma-ray Space Telescope ; Electronic band structure ; Usability ; Condensed matter physics ; Statistical physics ; Computational physics ; Physics ; Materials science ; Computer science ; Electron ; Quantum mechanics Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Heusler alloys: electronic and magnetic properties
Authors: Tatsuhiko Yoshino | Shigeki Matsunaga
Source: ACS Catalysis (article, 2021-05-17)
Field: Chemistry — Organic Chemistry

Chiral Carboxylic Acid Assisted Enantioselective C–H Activation with Achiral Cp<sup>x</sup>M<sup>III</sup> (M = Co, Rh, Ir) Catalysts Enantioselective C–H functionalization is a powerful tool for synthesizing chiral molecules. In the past few years, the combination of high-valent group 9 metals with achiral Cpx ligands and chiral carboxylic acids (CCA) has emerged as a promising catalytic system to enable selective cleavage and functionalization of enantiotopic C–H bonds. This Perspective summarizes the background, catalyst design, and applied reactions in detail, followed by a discussion of future directions. Enantioselective synthesis ; Catalysis ; Chemistry ; Surface modification ; Chirality (physics) ; Stereochemistry ; Carboxylic acid ; Molecule ; Combinatorial chemistry ; Medicinal chemistry ; Organic chemistry ; Chiral symmetry ; Physics Catalytic C–H Functionalization Methods ; Asymmetric Hydrogenation and Catalysis ; Cyclopropane Reaction Mechanisms
Authors: Christopher Kuenneth | Arunkumar Chitteth Rajan | Huan Tran | Lihua Chen | Chiho Kim | Rampi Ramprasad
Source: Patterns (article, 2021-04-01)
Field: Materials Science — Materials Chemistry

Polymer informatics with multi-task learning Modern data-driven tools are transforming application-specific polymer development cycles. Surrogate models that can be trained to predict properties of polymers are becoming commonplace. Nevertheless, these models do not utilize the full breadth of the knowledge available in datasets, which are oftentimes sparse; inherent correlations between different property datasets are disregarded. Here, we demonstrate the potency of multi-task learning approaches that exploit such inherent correlations effectively. Data pertaining to 36 different properties of over 13,000 polymers are supplied to deep-learning multi-task architectures. Compared to conventional single-task learning models, the multi-task approach is accurate, efficient, scalable, and amenable to transfer learning as more data on the same or different properties become available. Moreover, these models are interpretable. Chemical rules, that explain how certain features control trends in property values, emerge from the present work, paving the way for the rational design of application specific polymers meeting desired property or performance objectives. Property (philosophy) ; Exploit ; Materials informatics ; Informatics ; Transfer of learning ; Stability (learning theory) Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Advanced Graph Neural Networks
Authors: Wei Zhang | Ganesh Sigdel | Keenan J. Mintz | Elif S. Seven | Yiqun Zhou | Chunyu Wang | Roger M. Leblanc
Source: International Journal of Nanomedicine (review, 2021-07-01)
Field: Materials Science — Materials Chemistry

Carbon Dots: A Future Blood–Brain Barrier Penetrating Nanomedicine and Drug Nanocarrier Drug delivery across the blood-brain barrier (BBB) is one of the biggest challenges in modern medicine due to the BBB's highly semipermeable property that limits most therapeutic agents of brain diseases to enter the central nervous system (CNS). In recent years, nanoparticles, especially carbon dots (CDs), exhibit many unprecedented applications for drug delivery. Several types of CDs and CD-ligand conjugates have been reported successfully penetrating the BBB, which shows a promising progress in the application of CD-based drug delivery system (DDS) for the treatment of CNS diseases. In this review, our discussion of CDs includes their classification, preparations, structures, properties, and applications for the treatment of neurodegenerative diseases, especially Alzheimer's disease (AD) and brain tumor. Moreover, abundant functional groups on the surface, especially amine and carboxyl groups, allow CDs to conjugate with diverse drugs as versatile drug nanocarriers. In addition, structure of the BBB is briefly described, and mechanisms for transporting various molecules across the BBB and other biological barriers are elucidated. Most importantly, recent developments in drug delivery with CDs as BBB-penetrating nanodrugs and drug nanocarriers to target CNS diseases especially Alzheimer's disease and brain tumor are summarized. Eventually, future prospects of the CD-based DDS are discussed in combination with the development of artificial intelligence and nanorobots. Nanocarriers ; Drug delivery ; Nanomedicine ; Blood–brain barrier ; Nanotechnology ; Drug ; Targeted drug delivery ; Drug carrier ; Medicine ; Pharmacology ; Materials science ; Central nervous system ; Nanoparticle ; Internal medicine Carbon and Quantum Dots Applications ; Nanocluster Synthesis and Applications ; Graphene and Nanomaterials Applications