jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Arthur V. Dolmatov | Sergey S. Maklakov | Anastasia V. Artemova | Dmitry A. Petrov | Artem O. Shiryaev | Andrey N. Lagarkov
Source: Sensors (article, 2023-02-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deposition of Thick SiO2 Coatings to Carbonyl Iron Microparticles for Thermal Stability and Microwave Performance Thick dielectric SiO<sub>2</sub> shells on the surface of iron particles enhance the thermal and electrodynamic parameters of the iron. A technique to deposit thick, 500-nm, SiO<sub>2</sub> shell to the surface of carbonyl iron (CI) particles was developed. The method consists of repeated deposition of SiO<sub>2</sub> particles with air drying between iterations. This method allows to obtain thick dielectric shells up to 475 nm on individual CI particles. The paper shows that a thick SiO<sub>2</sub> protective layer reduces the permittivity of the 'Fe-SiO<sub>2</sub>-paraffin' composite in accordance with the Maxwell Garnett medium theory. The protective shell increases the thermal stability of iron, when heated in air, by shifting the transition temperature to the higher oxide. The particle size, the thickness of the SiO<sub>2</sub> shells, and the elemental analysis of the samples were studied using a scanning electron microscope. A coaxial waveguide and the Nicholson-Ross technique were used to measure microwave permeability and permittivity of the samples. A vibrating-sample magnetometer (VSM) was used to measure the magnetostatic data. A synchronous thermal analysis was applied to measure the thermal stability of the coated iron particles. The developed samples can be applied for electromagnetic compatibility problems, as well as the active material for various types of sensors. Microwave ; Thermal stability ; Deposition (geology) ; Carbonyl iron ; Materials science ; Chemical engineering ; Thermal ; Nanotechnology ; Chemistry ; Composite material ; Engineering ; Telecommunications Electromagnetic wave absorption materials ; Microwave Dielectric Ceramics Synthesis ; Copper Interconnects and Reliability
Authors: Simon Schick | Robert Groten | Gunnar Henrik Seide
Source: Polymers (article, 2023-03-09)
Field: Materials Science — Biomaterials

Performance Spectrum of Home-Compostable Biopolymer Fibers Compared to a Petrochemical Alternative Manufacturers of technical polymers must increasingly consider the degradability of their products due to the growing public interest in topics such as greenhouse gas emissions and microplastic pollution. Biobased polymers are part of the solution, but they are still more expensive and less well characterized than conventional petrochemical polymers. Therefore, few biobased polymers with technical applications have reached the market. Polylactic acid (PLA) is the most widely-used industrial thermoplastic biopolymer and is mainly found in the areas of packaging and single-use products. It is classed as biodegradable but only breaks down efficiently above the glass transition temperature of ~60 °C, so it persists in the environment. Some commercially available biobased polymers can break down under normal environmental conditions, including polybutylene succinate (PBS), polybutylene adipate terephthalate (PBAT) and thermoplastic starch (TPS), but they are used far less than PLA. This article compares polypropylene, a petrochemical polymer and benchmark for technical applications, with the commercially available biobased polymers PBS, PBAT and TPS, all of which are home-compostable. The comparison considers processing (using the same spinning equipment to generate comparable data) and utilization. Draw ratios ranged from 29 to 83, with take-up speeds from 450 to 1000 m/min. PP achieved benchmark tenacities over 50 cN/tex with these settings, while PBS and PBAT achieved over 10cN/tex. By comparing the performance of biopolymers to petrochemical polymers in the same melt-spinning setting, it is easier to decide which polymer to use in a particular application. This study shows the possibility that home-compostable biopolymers are suitable for products with lower mechanical properties. Only spinning the materials on the same machine with the same settings produces comparable data. This research, therefore, fills the niche and provides comparable data. To our knowledge, this report is the first direct comparison of polypropylene and biobased polymers in the same spinning process with the same parameter settings. Biopolymer ; Petrochemical ; Materials science ; Polymer ; Polybutylene succinate ; Polypropylene ; Polylactic acid ; Polymer science ; Biodegradation ; Thermoplastic ; Bioplastic ; Environmental pollution ; Pulp and paper industry ; Waste management ; Composite material ; Environmental science ; Organic chemistry ; Chemistry ; Engineering biodegradable polymer synthesis and properties ; Additive Manufacturing and 3D Printing Technologies ; Microplastics and Plastic Pollution
Authors: Tianwei Xue | Li Peng | Olga A. Syzgantseva | Maria A. Syzgantseva | Peiwen Guo | Huiyan Lai | Ruiqing Li | Jiawen Chen | Shumu Li | Xiaomei Yan | Shuliang Yang | Jun Li | Buxing Han | Wendy L. Queen
Source: Angewandte Chemie International Edition (article, 2023-09-04)
Field: Chemistry — Inorganic Chemistry

Rapid, Selective Extraction of Silver from Complex Water Matrices with a Metal–Organic Framework/Oligomer Composite Constructed via Supercritical CO<sub>2</sub> Every year vast quantities of silver are lost in various waste streams; this, combined with its limited, diminishing supply and rising demand, makes silver recovery of increasing importance. Thus, herein, we report a controllable, green process to produce a host of highly porous metal-organic framework (MOF)/oligomer composites using supercritical carbon dioxide (ScCO<sub>2</sub> ) as a medium. One resulting composite, referred to as MIL-127/Poly-o-phenylenediamine (PoPD), has an excellent Ag<sup>+</sup> adsorption capacity, removal efficiency (>99 %) and provides rapid Ag<sup>+</sup> extraction in as little as 5 min from complex liquid matrices. Notably, the composite can also reduce sliver concentrations below the levels (<0.1 ppm) established by the United States Environmental Protection Agency. Using theoretical simulations, we find that there are spatially ordered polymeric units inside the MOF that promote the complexation of Ag<sup>+</sup> over other common competing ions. Moreover, the oligomer is able to reduce silver to its metallic state, also providing antibacterial properties. Oligomer ; Supercritical fluid ; Composite number ; Materials science ; Metal-organic framework ; Extraction (chemistry) ; Supercritical carbon dioxide ; Adsorption ; Metal ; Porosity ; Chemical engineering ; Supercritical fluid extraction ; Metal ions in aqueous solution ; Composite material ; Chemistry ; Polymer chemistry ; Organic chemistry ; Metallurgy Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Adsorption and biosorption for pollutant removal
Authors: Qing Jiang | Yi Han | Ya Zou | Chunyan Chi
Source: Journal of Materials Chemistry C (article, 2023-01-01)
Field: Chemistry — Organic Chemistry

Quinodimethane embedded expanded helicenes and their open-shell diradical dications/dianions Two new quinodimethane embedded expanded [11]- and [13]helicenes were synthesized, showing a closed-shell quinoidal structure. Their dications and dianions exhibit significant open-shell diradical character with small singlet–triplet energy gaps. Diradical ; Materials science ; Shell (structure) ; Open shell ; Chemistry ; Organic chemistry ; Composite material ; Physics ; Atomic physics Synthesis and Properties of Aromatic Compounds ; DNA and Nucleic Acid Chemistry ; Synthesis of heterocyclic compounds
Authors: Giuseppina Anna Corrente | Dora A. González | Ece Aktas | Agostina Lina Capodilupo | Francesco Ruighi | Gianluca Accorsi | D. Imbardelli | Cristina Rodríguez‐Seco | Eugenia Martínez‐Ferrero | Emilio Palomares | Amerigo Beneduci
Source: Journal of Materials Chemistry C (article, 2023-01-01)
Field: Materials Science — Polymers and Plastics

Reversible vis-NIR electrochromic/electrofluorochromic switching in dual-functional devices modulated by different benzothiadiazole-arylamine anodic components Herein we show all-in-one electrochromic and electrofluorochromic devices where the ethyl viologen cathode is coupled to different arylamine-BT-arylamine anodes, by which modulation of light absorption and emission in the vis-NIR range is achieved. Electrochromism ; Materials science ; Anode ; Cathode ; Viologen ; Optoelectronics ; Electrochromic devices ; Absorption (acoustics) ; Modulation (music) ; Photochemistry ; Electrode ; Composite material ; Chemistry ; Physical chemistry Conducting polymers and applications ; Transition Metal Oxide Nanomaterials ; Perovskite Materials and Applications
Authors: Ahmed E. Abasaeed | Mahmud L. Sofiu | Kenit Acharya | Ahmed I. Osman | Anis H. Fakeeha | Raja Alotaibi | Ahmed A. Ibrahim | Abdulrhman S. Al‐Awadi | Hossein Bayahia | Salma A. Al‐Zahrani | Rawesh Kumar | Ahmed S. Al‐Fatesh
Source: Energy Science & Engineering (article, 2023-01-26)
Field: Materials Science — Materials Chemistry

The influence of Ni stability, redox, and lattice oxygen capacity on catalytic hydrogen production via methane dry reforming in innovative metal oxide systems Abstract Finding a robust catalytic system for hydrogen production via dry reforming of methane (DRM) remains a challenge. Herein, MNi 0.9 Zr 1 − x Y x O 3 (M = Ce, La, and La 0.6 Ce 0.4 ; x = 0.00, 0.05, 0.07, and 0.09) catalyst was prepared by the sol–gel method, tested for DRM and characterized by surface area and porosity, X‐ray diffraction, H 2 ‐temperature programmed reduction, thermogravimetry, and transmission electron microscopy. In La 0.6 Ce 0.4 NiO 3 catalyst, the substitution of Ni by 0.1% Zr results in a constant high catalytic activity (83% hydrogen yield at 800°C) due to the presence of reducible “NiO‐species interacted strongly with the support” (stable metallic Ni over reduced catalyst) and redox input by ceria phase for laying instant lattice oxygen during lag‐off period of CO 2 . Substitution of Ni by Zr and Y in the CeNiO 3 catalyst system nurtures Ni 3 Y (providing highly stable metallic Ni for CH 4 decomposition) and cerium yttrium oxide phases (providing strong redox input). CeNi 0.9 Zr 0.01 Y 0.09 O 3 shows 85% H 2 yield at 800°C. Catalysis ; Materials science ; Hydrogen production ; Non-blocking I/O ; Redox ; Cerium oxide ; Oxide ; Cerium ; Hydrogen ; Inorganic chemistry ; Yttrium ; Chemistry ; Metallurgy Catalytic Processes in Materials Science ; Catalysts for Methane Reforming ; Catalysis and Oxidation Reactions
Authors: Mana Nabavian Kalat | Maria Staszczak | Leszek Urbański | Carlos Polvorinos-Fernández | Carlos Aguilar Vega | Mariana Cristea | Daniela Ioniţă | Andrés Díaz Lantada | E. A. Pieczyska
Source: Materials & Design (article, 2023-08-21)
Field: Materials Science — Polymers and Plastics

Investigating a shape memory epoxy resin and its application to engineering shape-morphing devices empowered through kinematic chains and compliant joints 4D printing is the additive manufacturing (3D printing) of objects that can transform their shape in a controlled and predictable way when subjected to external stimuli. A thermo-responsive shape memory polymer (SMP) is a highly suitable material to 4D print smart devices, due to its actuation function and the capability of recovering its original shape from the deformed one upon heating. This study presents the results of employing an epoxy resin in the additive manufacturing of complex-shaped smart devices with shape-morphing properties using laser stereolithography (SLA). To quantify the shape memory behaviour of the shape memory epoxy (SMEp), we first investigate the thermomechanical properties of the 3D-printed specimens in a tensile testing machine coupled with an environmental thermal chamber. This approach allows us to determine the shape fixity and recovery of SMEp. Next, we propose effective designs of complex-shaped devices, with the aim of promoting shape morphing through micro-actuators and compliant joints acting as active regions in combination with multiplying mechanisms or kinematic chains in each of the devices. We manufacture the complex-shaped prototypes by using SLA directly from the computer-aided designs. The shape memory trials of the 3D-printed prototypes reveal quite precise shape recovery of the devices, illustrating their shape-memory. In fact, the inclusion of micro-actuators and compliant joints within the complex-geometry devices allows for local triggering, deformation and recovery, resulting in a prompt response of the devices to heat. Therefore, innovative designs, along with the suitable smart material and high-quality manufacturing process, lead to 4D printed devices with fast actuation and shape-morphing properties. Overall, this research may contribute to the development of smart materials and 4D printing technology for applications in fields such as biomedical engineering, robotics, transport and aerospace engineering. Stereolithography ; Shape-memory alloy ; Morphing ; Shape-memory polymer ; Materials science ; Actuator ; 3D printing ; Smart material ; Epoxy ; Mechanical engineering ; Deformation (meteorology) ; Computer science ; Composite material ; Artificial intelligence Polymer composites and self-healing ; Advanced Materials and Mechanics ; Advanced Sensor and Energy Harvesting Materials
Authors: Xiaoning Qian | Byung-Jun Yoon | Raymundo Arróyave | Xiaofeng Qian | Edward R. Dougherty
Source: Patterns (review, 2023-11-01)
Field: Materials Science — Materials Chemistry

Knowledge-driven learning, optimization, and experimental design under uncertainty for materials discovery Significant acceleration of the future discovery of novel functional materials requires a fundamental shift from the current materials discovery practice, which is heavily dependent on trial-and-error campaigns and high-throughput screening, to one that builds on knowledge-driven advanced informatics techniques enabled by the latest advances in signal processing and machine learning. In this review, we discuss the major research issues that need to be addressed to expedite this transformation along with the salient challenges involved. We especially focus on Bayesian signal processing and machine learning schemes that are uncertainty aware and physics informed for knowledge-driven learning, robust optimization, and efficient objective-driven experimental design. Computer science ; Bayesian optimization ; Salient ; Machine learning ; Artificial intelligence ; Knowledge extraction ; Data science ; Signal processing ; Informatics ; Bayesian probability ; Engineering ; Digital signal processing Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Neural Networks and Applications
Authors: Riley J. Hickman | Jurgis Ruža | Hermann Tribukait | Loı̈c M. Roch | Alberto García-Durán
Source: Reaction Chemistry & Engineering (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Equipping data-driven experiment planning for Self-driving Laboratories with semantic memory: case studies of transfer learning in chemical reaction optimization SeMOpt uses meta-/few-shot learning to enable knowledge transfer from previous experiments to accelerate Bayesian optimization of chemical reactions. Bayesian optimization ; Computer science ; Transfer of learning ; Artificial intelligence ; Transfer (computing) ; Bayesian probability ; Semantic memory ; Machine learning ; Psychology ; Neuroscience ; Cognition Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Microfluidic and Capillary Electrophoresis Applications
Authors: Felix Strieth‐Kalthoff | Han Hao | Vandana Rathore | Joshua S. Derasp | Théophile Gaudin | Nicholas H. Angello | Martin Seifrid | Ekaterina Trushina | Mason Guy | Junliang Liu | Xun Tang | Masashi Mamada | Wesley Wang | Tuul Tsagaantsooj | Cyrille Lavigne | Robert Pollice | Tony Wu | Kazuhiro Hotta | L Bodø | Shangyu Li | Mohammad Haddadnia | Agnieszka Wołoś | Rafał Roszak | Cher Tian Ser | Carlota Bozal‐Ginesta | Riley J. Hickman | Jenya Vestfrid | Andrés Aguilar‐Granda | Elena L. Klimareva | Ralph C. Sigerson | Wenduan Hou | Daniel Gahler | Sławomir Lach | Adrian Warzybok | Oleg Borodin | Simon Rohrbach | Benjamín Sánchez-Lengeling | Chihaya Adachi | Bartosz A. Grzybowski | Leroy Cronin | Jason E. Hein | Martin D. Burke | Alán Aspuru‐Guzik
Source: ChemRxiv (preprint, 2023-09-20)
Field: Materials Science — Materials Chemistry

Delocalized, Asynchronous, Closed-Loop Discovery of Organic Laser Emitters Contemporary materials discovery requires intricate sequences of synthesis, formulation and characterization that often span multiple locations with specialized expertise or instrumentation. To accelerate these workflows, we present a cloud-based strategy that enables delocalized and asynchronous design–make–test–analyze cycles. We showcase this approach through the exploration of molecular gain materials for organic solid-state lasers as a frontier application in molecular optoelectronics. Distributed robotic synthesis and in-line property characterization, orchestrated by a cloud-based AI experiment planner, resulted in the discovery of 21 new state-of-the-art materials. Automated gram-scale synthesis ultimately allowed for the verification of best-in-class stimulated emission in a thin-film device. Demonstrating the asynchronous integration of five laboratories across the globe, this workflow provides a blueprint for delocalizing – and democratizing – scientific discovery. Workflow ; Computer science ; Asynchronous communication ; Cloud computing ; Characterization (materials science) ; State (computer science) ; Nanotechnology ; Materials science ; Telecommunications ; Database ; Operating system ; Programming language Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Catalysis and Oxidation Reactions
Authors: Moksh Jain | Tristan Deleu | Jason Hartford | Chenghao Liu | Álex Hernández-García | Yoshua Bengio
Source: Digital Discovery (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

GFlowNets for AI-driven scientific discovery GFlowNets provide a general probabilistic framework for accelerating the computational phase of the scientific discovery process, which is crucial for tackling pressing challenges posed by global pandemics and the climate crisis. Computer science ; Machine learning ; Data science ; Scientific discovery ; Artificial intelligence ; Leverage (statistics) ; Pipeline (software) ; Context (archaeology) ; Probabilistic logic ; Uncertainty quantification ; Big data ; Data mining Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning and Data Classification
Authors: Vidushi Sharma | Maxwell J. Giammona | Dmitry Yu. Zubarev | Andy Tek | Khanh Nugyuen | Linda K. Sundberg | Daniele Congiu | Young‐Hye La
Source: Journal of Chemical Information and Modeling (article, 2023-11-10)
Field: Materials Science — Materials Chemistry

Formulation Graphs for Mapping Structure-Composition of Battery Electrolytes to Device Performance Advanced computational methods are being actively sought to address the challenges associated with the discovery and development of new combinatorial materials, such as formulations. A widely adopted approach involves domain-informed high-throughput screening of individual components that can be combined together to form a formulation. This manages to accelerate the discovery of new compounds for a target application but still leaves the process of identifying the right "formulation" from the shortlisted chemical space largely a laboratory experiment-driven process. We report a deep learning model, the Formulation Graph Convolution Network (F-GCN), that can map the structure-composition relationship of the formulation constituents to the property of liquid formulation as a whole. Multiple GCNs are assembled in parallel that featurize formulation constituents domain-intuitively on the fly. The resulting molecular descriptors are scaled based on the respective constituent's molar percentage in the formulation, followed by integration into a combined formulation descriptor that represents the complete formulation to an external learning architecture. The use case of the proposed formulation learning model is demonstrated for battery electrolytes by training and testing it on two exemplary data sets representing electrolyte formulations vs battery performance: one data set is sourced from the literature about Li/Cu half-cells, while the other is obtained by lab experiments related to lithium-iodide full-cell chemistry. The model is shown to predict performance metrics such as Coulombic efficiency (CE) and specific capacity of new electrolyte formulations with the lowest reported errors. The best-performing F-GCN model uses molecular descriptors derived from molecular graphs (GCNs) that are informed with HOMO-LUMO and electric moment properties of the molecules using a knowledge transfer technique. Battery (electricity) ; Electrolyte ; Composition (language) ; Computer science ; Chemistry ; Electrode ; Physics ; Thermodynamics ; Physical chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Michael J. Statt | Brian A. Rohr | Dan Guevarra | Ja'Nya Breeden | Santosh K. Suram | John M. Gregoire
Source: Digital Discovery (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

The materials experiment knowledge graph Graph representations of hierarchical knowledge, including experiment provenances, will help usher in a new era of data-driven materials science. Knowledge graph ; Graph ; Computer science ; Data science ; Information retrieval ; Theoretical computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Electron Microscopy Techniques and Applications