jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Weiran Zhang | Xiaojun Wang | Yuyang Wang | Guojian Yang | Chang Gu | Wenxuan Zheng | Yu‐Mo Zhang | Minjie Li | Sean Xiao‐An Zhang
Source: Nature Communications (article, 2019-04-05)
Field: Materials Science — Polymers and Plastics

Bio-inspired ultra-high energy efficiency bistable electronic billboard and reader Bistable display has been a long-awaited goal due to its zero energy cost when maintaining colored or colorless state and electrochromic material has been highly considered as a potential way to achieve bistable display due to its simple structure and possible manipulation. However, it is extremely challenging with insurmountable technical barriers related to traditional electrochromic mechanisms. Herein a prototype for bistable electronic billboard and reader with high energy efficiency is demonstrated with excellent bistability (decay 7% in an hour), reversibility (10<sup>4</sup> cycles), coloration efficiency (430 cm<sup>2 </sup>C<sup>-1</sup>) and very short voltage stimulation time (2 ms) for color switching, which greatly outperforms current products. This is achieved by stabilization of redox molecule via intermolecular ion transfer to the supramolecular bonded colorant and further stabilization of the electrochromic molecules in semi-solid media. This promising approach for ultra-energy-efficient display will promote the development of switching molecules, devices and applications in various fields of driving/navigation/industry as display to save energy. Bistability ; Electrochromism ; Electrochromic devices ; Materials science ; Intermolecular force ; Nanotechnology ; Molecular switch ; Optoelectronics ; Efficient energy use ; Computer science ; Molecule ; Chemistry ; Electrical engineering ; Electrode ; Engineering Conducting polymers and applications ; Perovskite Materials and Applications ; Advanced Sensor and Energy Harvesting Materials
Authors: Ghanshyam Pilania | Carl N. Iverson | Turab Lookman | Babetta L. Marrone
Source: Journal of Chemical Information and Modeling (article, 2019-11-07)
Field: Materials Science — Materials Chemistry

Machine-Learning-Based Predictive Modeling of Glass Transition Temperatures: A Case of Polyhydroxyalkanoate Homopolymers and Copolymers Polyhydroxyalkanoate-based polymers-being ecofriendly, biosynthesizable, and economically viable and possessing a broad range of tunable properties-are currently being actively pursued as promising alternatives for petroleum-based plastics. The vast chemical complexity accessible within this class of polymers gives rise to challenges in the rational discovery of novel polymer chemistries for specific applications. The burgeoning field of polymer informatics addresses this challenge via providing tools and strategies for accelerated property prediction and materials design via surrogate machine-learning models built on reliable past data. In this contribution, we use glass transition temperature <i>T</i><sub>g</sub> as an example target property to demonstrate promise of the data-enabled route to accelerated learning of accurate structure-property mappings in PHA-based polymers. Our analysis uses a data set of experimentally measured <i>T</i><sub>g</sub> values, polymer molecular weights, and a polydispersity index for PHA-based homo- and copolymers that was carefully assembled from the literature. A fingerprinting scheme that captures key properties based on topology, shape, and charge/polarity of specific chemical units or motifs forming the polymer backbone was devised to numerically represent the polymers. A validated statistical learning model is then developed to allow for a mapping of the polymer fingerprints onto the property space in a physically meaningful and reliable manner. Once developed, the model can not only rapidly predict the property of new PHA polymers but also provide uncertainties underlying the predictions. The model is further combined with an evolutionary-algorithm-based search strategy to efficiently identify multicomponent polymer compositions with a prespecified <i>T</i><sub>g</sub>. While the present contribution is focused specifically on <i>T</i><sub>g</sub>, the surrogate model development approach put forward here is general and can, in principle, be extended to a range of other properties. Polymer ; Glass transition ; Chemical space ; Computer science ; Materials science ; Property (philosophy) ; Copolymer ; Polymer architecture ; Machine learning ; Biochemical engineering ; Nanotechnology ; Biological system ; Chemistry ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; biodegradable polymer synthesis and properties
Authors: Juán Andrés | Paul W. Ayers | Roberto A. Boto | Ramon Carbó‐Dorca | Henry Chermette | Jerzy Ciosłowski | Julia Contreras‐García | David L. Cooper | Gernot Frenking | Carlo Gatti | Farnaz Heidar‐Zadeh | Laurent Joubert | Ángel Martín Pendás | Eduard Matito | I. Mayer | Alston J. Misquitta | Yirong Mo | Julien Pilmé | Paul L. A. Popelier | Martin Rahm | Eloy Ramos‐Cordoba | Pedro Salvador | W. H. Eugen Schwarz | Shant Shahbazian | Bernard Silvi | Miquel Solà | Krzysztof Szalewicz | Vincent Tognetti | Frank Weinhold | Émilie‐Laure Zins
Source: Journal of Computational Chemistry (article, 2019-06-28)
Field: Materials Science — Materials Chemistry

Nine questions on energy decomposition analysis The paper collects the answers of the authors to the following questions: Is the lack of precision in the definition of many chemical concepts one of the reasons for the coexistence of many partition schemes? Does the adoption of a given partition scheme imply a set of more precise definitions of the underlying chemical concepts? How can one use the results of a partition scheme to improve the clarity of definitions of concepts? Are partition schemes subject to scientific Darwinism? If so, what is the influence of a community's sociological pressure in the "natural selection" process? To what extent does/can/should investigated systems influence the choice of a particular partition scheme? Do we need more focused chemical validation of Energy Decomposition Analysis (EDA) methodology and descriptors/terms in general? Is there any interest in developing common benchmarks and test sets for cross-validation of methods? Is it possible to contemplate a unified partition scheme (let us call it the "standard model" of partitioning), that is proper for all applications in chemistry, in the foreseeable future or even in principle? In the end, science is about experiments and the real world. Can one, therefore, use any experiment or experimental data be used to favor one partition scheme over another? © 2019 Wiley Periodicals, Inc. Decomposition ; Energy (signal processing) ; Computer science ; Chemistry ; Mathematics ; Statistics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Chemical Physics Studies
Authors: Prashant Dogra | Joseph D. Butner | Yao-Li Chuang | Sergio Caserta | Shreya Goel | C. Jeffrey Brinker | Vittorio Cristini | Zhihui Wang
Source: Biomedical Microdevices (review, 2019-04-04)
Field: Materials Science — Biomaterials

Mathematical modeling in cancer nanomedicine: a review Cancer continues to be among the leading healthcare problems worldwide, and efforts continue not just to find better drugs, but also better drug delivery methods. The need for delivering cytotoxic agents selectively to cancerous cells, for improved safety and efficacy, has triggered the application of nanotechnology in medicine. This effort has provided drug delivery systems that can potentially revolutionize cancer treatment. Nanocarriers, due to their capacity for targeted drug delivery, can shift the balance of cytotoxicity from healthy to cancerous cells. The field of cancer nanomedicine has made significant progress, but challenges remain that impede its clinical translation. Several biophysical barriers to the transport of nanocarriers to the tumor exist, and a much deeper understanding of nano-bio interactions is necessary to change the status quo. Mathematical modeling has been instrumental in improving our understanding of the physicochemical and physiological underpinnings of nanomaterial behavior in biological systems. Here, we present a comprehensive review of literature on mathematical modeling works that have been and are being employed towards a better understanding of nano-bio interactions for improved tumor delivery efficacy. Nanomedicine ; Nanocarriers ; Drug delivery ; Status quo ; Nanotechnology ; Cancer ; Medicine ; Risk analysis (engineering) ; Materials science ; Political science ; Nanoparticle Nanoparticle-Based Drug Delivery ; Graphene and Nanomaterials Applications ; Molecular Communication and Nanonetworks
Authors: Giuseppe Arrabito | Aurelio Bonasera | G. Prestopino | A. Orsini | A. Mattoccia | Eugenio Martinelli | Bruno Pignataro | P. G. Medaglia
Source: Crystals (article, 2019-07-15)
Field: Materials Science — Materials Chemistry

Layered Double Hydroxides: A Toolbox for Chemistry and Biology Layered double hydroxides (LDHs) are an emergent class of biocompatible inorganic lamellar nanomaterials that have attracted significant research interest owing to their high surface-to-volume ratio, the capability to accumulate specific molecules, and the timely release to targets. Their unique properties have been employed for applications in organic catalysis, photocatalysis, sensors, drug delivery, and cell biology. Given the widespread contemporary interest in these topics, time-to-time it urges to review the recent progresses. This review aims to summarize the most recent cutting-edge reports appearing in the last years. It firstly focuses on the application of LDHs as catalysts in relevant chemical reactions and as photocatalysts for organic molecule degradation, water splitting reaction, CO2 conversion, and reduction. Subsequently, the emerging role of these materials in biological applications is discussed, specifically focusing on their use as biosensors, DNA, RNA, and drug delivery, finally elucidating their suitability as contrast agents and for cellular differentiation. Concluding remarks and future prospects deal with future applications of LDHs, encouraging researches in better understanding the fundamental mechanisms involved in catalytic and photocatalytic processes, and the molecular pathways that are activated by the interaction of LDHs with cells in terms of both uptake mechanisms and nanotoxicology effects. Layered double hydroxides ; Nanotechnology ; Nanomaterials ; Drug delivery ; Photocatalysis ; Biocompatible material ; Chemistry ; Catalysis ; Biochemical engineering ; Materials science ; Engineering ; Organic chemistry Layered Double Hydroxides Synthesis and Applications ; Advanced Photocatalysis Techniques ; Polyoxometalates: Synthesis and Applications
Authors: Wenqian Feng | Yu Chai | Joe Forth | Paul D. Ashby | Thomas P. Russell | Brett A. Helms
Source: Nature Communications (article, 2019-03-06)
Field: Materials Science — Materials Chemistry

Harnessing liquid-in-liquid printing and micropatterned substrates to fabricate 3-dimensional all-liquid fluidic devices Systems comprised of immiscible liquids held in non-equilibrium shapes by the interfacial assembly and jamming of nanoparticle-polymer surfactants have significant potential to advance catalysis, chemical separations, energy storage and conversion. Spatially directing functionality within them and coupling processes in both phases remains a challenge. Here, we exploit nanoclay-polymer surfactant assemblies at an oil-water interface to produce a semi-permeable membrane between the liquids, and from them all-liquid fluidic devices with bespoke properties. Flow channels are fabricated using micropatterned 2D substrates and liquid-in-liquid 3D printing. The anionic walls of the device can be functionalized with cationic small molecules, enzymes, and colloidal nanocrystal catalysts. Multi-step chemical transformations can be conducted within the channels under flow, as can selective mass transport across the liquid-liquid interface for in-line separations. These all-liquid systems become automated using pumps, detectors, and control systems, revealing a latent ability for chemical logic and learning. Materials science ; Nanotechnology ; Microfluidics ; Fluidics ; Polymer ; Chemical engineering Pickering emulsions and particle stabilization ; Innovative Microfluidic and Catalytic Techniques Innovation ; Micro and Nano Robotics
Authors: Li Yan | Sevil Gonca | Guangyu Zhu | Wenjun Zhang | Xianfeng Chen
Source: Journal of Materials Chemistry B (review, 2019-01-01)
Field: Materials Science — Materials Chemistry

Layered double hydroxide nanostructures and nanocomposites for biomedical applications Layered double hydroxide (LDH) nanostructures and related nanocomposites have attracted significant interest in biomedical applications including cancer therapy, bioimaging and antibacterial treatment. These materials hold great advantages including low cost and facile preparation, convenient drug loading, high drug incorporation capacity, good biocompatibility, efficient intracellular uptake and endosome/lysosome escape, and natural biodegradability in an acidic environment. In this review, we summarize the development of three types of LDH nanostructures including pristine LDH, surface modified LDH, and LDH nanocomposites for a range of biomedical applications. The advantages and disadvantages of LDH nanostructures and insights into the future development are also discussed. Biocompatibility ; Hydroxide ; Materials science ; Nanotechnology ; Nanocomposite ; Layered double hydroxides ; Nanostructure ; Drug delivery ; Chemical engineering Layered Double Hydroxides Synthesis and Applications ; Inorganic Chemistry and Materials ; Advanced Photocatalysis Techniques
Authors: Haoyan Huo | Ziqin Rong | Olga Kononova | Wenhao Sun | Tiago Botari | Tanjin He | Vahe Tshitoyan | Gerbrand Ceder
Source: npj Computational Materials (article, 2019-07-08)
Field: Materials Science — Materials Chemistry

Semi-supervised machine-learning classification of materials synthesis procedures Abstract Digitizing large collections of scientific literature can enable new informatics approaches for scientific analysis and meta-analysis. However, most content in the scientific literature is locked-up in written natural language, which is difficult to parse into databases using explicitly hard-coded classification rules. In this work, we demonstrate a semi-supervised machine-learning method to classify inorganic materials synthesis procedures from written natural language. Without any human input, latent Dirichlet allocation can cluster keywords into topics corresponding to specific experimental materials synthesis steps, such as “grinding” and “heating”, “dissolving” and “centrifuging”, etc. Guided by a modest amount of annotation, a random forest classifier can then associate these steps with different categories of materials synthesis, such as solid-state or hydrothermal synthesis. Finally, we show that a Markov chain representation of the order of experimental steps accurately reconstructs a flowchart of possible synthesis procedures. Our machine-learning approach enables a scalable approach to unlock the large amount of inorganic materials synthesis information from the literature and to process it into a standardized, machine-readable database. Computer science ; Artificial intelligence ; Parsing ; Machine learning ; Natural language processing ; Latent Dirichlet allocation ; Scientific literature ; Topic model Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Catalysis and Oxidation Reactions
Authors: Guo Liang Goh | Shweta Agarwala | Wai Yee Yeong
Source: Advanced Materials Interfaces (review, 2019-01-02)
Field: Materials Science — Materials Chemistry

Directed and On‐Demand Alignment of Carbon Nanotube: A Review toward 3D Printing of Electronics Abstract Carbon nanotubes (CNTs) are 1D nanostructured materials with unique mechanical, optical, and electrical properties which can be potentially exploited for fabricating wide variety of devices. In addition, the biocompatibility of CNTs makes it attractive for wearable and implantable technology applications. Well‐aligned CNT structures show enhanced properties such as superior electron mobility, strain sensitivity, better mechanical property, and enhanced performance and reproducibility that are absent in their disordered counterparts, thus allowing more promising applications in various fields. With aligned CNTs, devices can be optimized to exhibit better performance with lesser materials and more miniature designs. This review summarizes the landscape of CNTs alignment, either during the growth or post‐growth processing. This paper delineates various CNTs alignment mechanism, process parameters, and challenges of each technique. A comparative discussion on the advantages, disadvantages, and degree of alignment of each technique is presented. A detailed discussion on the various applications that utilize properties of aligned CNTs devices is presented. The advent of 3D printing techniques for printing CNTs for novel and futuristic applications is also discussed. Carbon nanotube ; Materials science ; Nanotechnology ; Wearable technology ; Electronics ; 3D printing ; Biocompatibility ; Wearable computer ; Computer science ; Composite material ; Electrical engineering Carbon Nanotubes in Composites ; Advanced Sensor and Energy Harvesting Materials ; Supercapacitor Materials and Fabrication
Authors: Li Wang | Coucong Gong | Xinzhu Yuan | Gang Wei
Source: Nanomaterials (review, 2019-02-18)
Field: Materials Science — Biomaterials

Controlling the Self-Assembly of Biomolecules into Functional Nanomaterials through Internal Interactions and External Stimulations: A Review Biomolecular self-assembly provides a facile way to synthesize functional nanomaterials. Due to the unique structure and functions of biomolecules, the created biological nanomaterials via biomolecular self-assembly have a wide range of applications, from materials science to biomedical engineering, tissue engineering, nanotechnology, and analytical science. In this review, we present recent advances in the synthesis of biological nanomaterials by controlling the biomolecular self-assembly from adjusting internal interactions and external stimulations. The self-assembly mechanisms of biomolecules (DNA, protein, peptide, virus, enzyme, metabolites, lipid, cholesterol, and others) related to various internal interactions, including hydrogen bonds, electrostatic interactions, hydrophobic interactions, π⁻π stacking, DNA base pairing, and ligand⁻receptor binding, are discussed by analyzing some recent studies. In addition, some strategies for promoting biomolecular self-assembly via external stimulations, such as adjusting the solution conditions (pH, temperature, ionic strength), adding organics, nanoparticles, or enzymes, and applying external light stimulation to the self-assembly systems, are demonstrated. We hope that this overview will be helpful for readers to understand the self-assembly mechanisms and strategies of biomolecules and to design and develop new biological nanostructures or nanomaterials for desired applications. Biomolecule ; Nanotechnology ; Nanomaterials ; Self-assembly ; Stacking ; Materials science ; Chemistry Supramolecular Self-Assembly in Materials ; Advanced biosensing and bioanalysis techniques ; RNA Interference and Gene Delivery
Authors: Barry A. Badeau | Cole A. DeForest
Source: Annual Review of Biomedical Engineering (review, 2019-03-12)
Field: Materials Science — Biomaterials

Programming Stimuli-Responsive Behavior into Biomaterials Stimuli-responsive materials undergo triggered changes when presented with specific environmental cues. These dynamic systems can leverage biological signals found locally within the body as well as exogenous cues administered with spatiotemporal control, providing powerful opportunities in next-generation diagnostics and personalized medicine. Here, we review the synthetic and strategic advances used to impart diverse responsiveness to a wide variety of biomaterials. Categorizing systems on the basis of material type, number of inputs, and response mechanism, we examine past and ongoing efforts toward endowing biomaterials with customizable sensitivity. We draw an analogy to computer science, whereby a stimuli-responsive biomaterial transduces a set of inputs into a functional output as governed by a user-specified logical operator. We discuss Boolean and non-Boolean operations, as well as the various chemical and physical modes of signal transduction. Finally, we examine current limitations and promising directions in the ongoing development of programmable stimuli-responsive biomaterials. Computer science ; Leverage (statistics) ; Mechanism (biology) ; Human–computer interaction ; Variety (cybernetics) ; Neuroscience ; Artificial intelligence ; Biology Supramolecular Self-Assembly in Materials ; Advanced Sensor and Energy Harvesting Materials ; Modular Robots and Swarm Intelligence
Authors: Wei Han | Pu Huang | Liang Li | Fakun Wang | Peng Luo | Kailang Liu | Xing Zhou | Huiqiao Li | Xiuwen Zhang | Yi Cui | Tianyou Zhai
Source: Nature Communications (article, 2019-10-17)
Field: Materials Science — Materials Chemistry

Two-dimensional inorganic molecular crystals Two-dimensional molecular crystals, consisting of zero-dimensional molecules, are very appealing due to their novel physical properties. However, they are mostly limited to organic molecules. The synthesis of inorganic version of two-dimensional molecular crystals is still a challenge due to the difficulties in controlling the crystal phase and growth plane. Here, we design a passivator-assisted vapor deposition method for the growth of two-dimensional Sb<sub>2</sub>O<sub>3</sub> inorganic molecular crystals as thin as monolayer. The passivator can prevent the heterophase nucleation and suppress the growth of low-energy planes, and enable the molecule-by-molecule lateral growth along high-energy planes. Using Raman spectroscopy and in situ transmission electron microscopy, we show that the insulating α-phase of Sb<sub>2</sub>O<sub>3</sub> flakes can be transformed into semiconducting β-phase under heat and electron-beam irradiation. Our findings can be extended to the controlled growth of other two-dimensional inorganic molecular crystals and open up opportunities for potential molecular electronic devices. Nucleation ; Monolayer ; Materials science ; Crystal (programming language) ; Raman spectroscopy ; Molecular beam epitaxy ; Crystal growth ; Phase (matter) ; Molecule ; Chemical physics ; Transmission electron microscopy ; Nanotechnology ; Crystallography ; Chemistry ; Epitaxy ; Layer (electronics) ; Optics 2D Materials and Applications ; Electronic and Structural Properties of Oxides ; Perovskite Materials and Applications