jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Anjitha Thadathil | Hareesh Pradeep | Deepak Joshy | Yahya A. Ismail | Pradeepan Periyat
Source: Materials Advances (article, 2022-01-01)
Field: Materials Science — Polymers and Plastics

Polyindole and polypyrrole as a sustainable platform for environmental remediation and sensor applications This review focuses on the synthesis and improvement of two types of N-containing conducting polymers – polypyrrole and polyindole – as well as an in-depth analysis of their binary and ternary nanocomposites in the treatment of contaminants, sensing, and green chemistry. Polypyrrole ; Ternary operation ; Environmental remediation ; Nanocomposite ; Nanotechnology ; Materials science ; Conductive polymer ; Polymer ; Computer science ; Contamination ; Polymerization ; Composite material Conducting polymers and applications ; Analytical Chemistry and Sensors ; Electrochemical sensors and biosensors
Authors: Alice E. A. Allen | Alexandre Tkatchenko
Source: Science Advances (article, 2022-05-06)
Field: Materials Science — Materials Chemistry

Machine learning of material properties: Predictive and interpretable multilinear models Machine learning models can provide fast and accurate predictions of material properties but often lack transparency. Interpretability techniques can be used with black box solutions, or alternatively, models can be created that are directly interpretable. We revisit material datasets used in several works and demonstrate that simple linear combinations of nonlinear basis functions can be created, which have comparable accuracy to the kernel and neural network approaches originally used. Linear solutions can accurately predict the bandgap and formation energy of transparent conducting oxides, the spin states for transition metal complexes, and the formation energy for elpasolite structures. We demonstrate how linear solutions can provide interpretable predictive models and highlight the new insights that can be found when a model can be directly understood from its coefficients and functional form. Furthermore, we discuss how to recognize when intrinsically interpretable solutions may be the best route to interpretability. Interpretability ; Multilinear map ; Computer science ; Nonlinear system ; Artificial intelligence ; Artificial neural network ; Machine learning ; Transparency (behavior) ; Linear model ; Black box ; Simple (philosophy) ; Energy (signal processing) ; Mathematics ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Mark Schubert | Guido Panzarasa | Ingo Burgert
Source: Chemical Reviews (review, 2022-12-19)
Field: Chemistry — Organic Chemistry

Sustainability in Wood Products: A New Perspective for Handling Natural Diversity Wood is a renewable resource with excellent qualities and the potential to become a key element of a future bioeconomy. The increasing environmental awareness and drive to achieve sustainability is leading to a resurgence of research on wood materials. Nevertheless, the global climate changes and associated consequences will soon challenge the wood-value chains in several regions (e.g., central Europe). To cope with these challenges, it is necessary to rethink the current practice of wood sourcing and transformation. The goal of this review is to address the intrinsic natural diversity of wood, from its origin to its technological consequences for the present and future manufacturing of wood products. So far, industrial processes have been optimized to repress the variability of wood properties, enabling more efficient processing and production of reliable products. However, the need to preserve biodiversity and the impact of climate change on forests call for new wood processing techniques and green chemistry protocols for wood modification as enabling factors necessary for managing a more diverse wood provision in the future. This article discusses the past developments that have resulted in the current wood value chains and provides a perspective about how natural variability could be turned into an asset for making truly sustainable wood products. After briefly introducing the chemical and structural complexity of wood, the methods conventionally adopted for industrial homogenization and modification of wood are discussed in relation to their evolution toward increased sustainability. Finally, a perspective is given on technological potentials of machine learning techniques and of novel functional wood materials. Here the main message is that through a combination of sustainable forestry, adherence to green chemistry principles and adapted processes based on machine learning, the wood industry could not only overcome current challenges but also thrive in the near future despite the awaiting challenges. Sustainability ; Wood processing ; Biodiversity ; Climate change ; Natural resource ; Asset (computer security) ; Business ; Biochemical engineering ; Environmental resource management ; Natural resource economics ; Environmental science ; Ecology ; Computer science ; Engineering ; Economics Wood and Agarwood Research ; Wood Treatment and Properties ; Lignin and Wood Chemistry
Authors: Mayanak K. Gupta | Jingxuan Ding | Dipanshu Bansal | D. L. Abernathy | G. Ehlers | Naresh C. Osti | Wolfgang G. Zeier | Olivier Delaire
Source: Advanced Energy Materials (article, 2022-05-08)
Field: Materials Science — Materials Chemistry

Strongly Anharmonic Phonons and Their Role in Superionic Diffusion and Ultralow Thermal Conductivity of Cu<sub>7</sub>PSe<sub>6</sub> Abstract The quest for advanced superionic materials requires understanding their complex atomic dynamics, but detailed studies of the interplay between lattice vibrations and ionic diffusion remain scarce. Here inelastic and quasielastic neutron scattering measurements in the superionic argyrodite Cu 7 PSe 6 are reported, combined with molecular dynamics (MD) based on ab initio and machine‐learned potentials, providing critical insights into the atomistic mechanisms underlying fast ion conduction. The results reveal how long‐range Cu diffusion is limited by intercluster hopping, controlled by selective anharmonic phonons of the crystalline framework. Further, the Green–Kubo simulations reproduce the ultralow lattice thermal conductivity and identify contributions from mobile ions, phonons, and their cross‐correlations. The mode resolved analysis shows that the thermal conductivity is dominated by low‐energy acoustic phonon modes of the overall crystal framework. The analysis of mode‐resolved spectral functions further show that vibrational modes with significant Cu contributions are strongly damped, corresponding to the breakdown of associated phonon quasiparticles. These results highlight the importance of strongly anharmonic effects in superionic systems, in which the traditional quasiharmonic phonon picture is insufficient, and pave the way toward combining machine‐learning accelerated simulations with neutron scattering experiments to rationalize the complex atomic dynamics underlying ionic and thermal transport. Anharmonicity ; Phonon ; Materials science ; Condensed matter physics ; Thermal conductivity ; Inelastic neutron scattering ; Thermal conduction ; Ionic conductivity ; Fast ion conductor ; Scattering ; Ion ; Phonon scattering ; Neutron scattering ; Physics ; Optics ; Quantum mechanics Advanced Thermoelectric Materials and Devices ; Advanced Condensed Matter Physics ; Thermal Expansion and Ionic Conductivity
Authors: Linus C. Erhard | Jochen Rohrer | Karsten Albe | Volker L. Deringer
Source: npj Computational Materials (article, 2022-04-28)
Field: Materials Science — Materials Chemistry

A machine-learned interatomic potential for silica and its relation to empirical models Abstract Silica (SiO 2 ) is an abundant material with a wide range of applications. Despite much progress, the atomistic modelling of the different forms of silica has remained a challenge. Here we show that by combining density-functional theory at the SCAN functional level with machine-learning-based interatomic potential fitting, a range of condensed phases of silica can be accurately described. We present a Gaussian approximation potential model that achieves high accuracy for the thermodynamic properties of the crystalline phases, and we compare its performance (and performance–cost trade-off) with that of multiple empirically fitted interatomic potentials for silica. We also include amorphous phases, assessing the ability of the potentials to describe structures of melt-quenched glassy silica, their energetic stability, and the high-pressure structural transition to a mainly sixfold-coordinated phase. We suggest that rather than standing on their own, machine-learned potentials for silica may be used in conjunction with suitable empirical models, each having a distinct role and complementing the other, by combining the advantages of the long simulation times afforded by empirical potentials and the near-quantum-mechanical accuracy of machine-learned potentials. This way, our work is expected to advance atomistic simulations of this key material and to benefit further computational studies in the field. Interatomic potential ; Work (physics) ; Statistical physics ; Density functional theory ; Range (aeronautics) ; Gaussian ; Amorphous silica ; Computer science ; Materials science ; Force field (fiction) ; Silica glass ; Machine learning ; Chemical physics ; Artificial intelligence ; Molecular dynamics ; Thermodynamics ; Computational chemistry ; Chemistry ; Physics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; X-ray Diffraction in Crystallography
Authors: Yuki Takechi‐Haraya | Takashi Ohgita | Yosuke Demizu | Hiroyuki Saito | Ken‐ichi Izutsu | Kumiko Sakai‐Kato
Source: AAPS PharmSciTech (review, 2022-05-20)
Field: Materials Science — Biomaterials

Current Status and Challenges of Analytical Methods for Evaluation of Size and Surface Modification of Nanoparticle-Based Drug Formulations Nanomedicine ; Nanotechnology ; Surface modification ; Nanoparticle ; Materials science ; Standardization ; Drug delivery ; Computer science ; Chemical engineering ; Engineering Nanoparticle-Based Drug Delivery ; Inhalation and Respiratory Drug Delivery ; Biosimilars and Bioanalytical Methods
Authors: Qiushi Li | Xiaodong Cai | Tong Liu | Min Jia | Qiong Wu | Haoyang Zhou | Huanhuan Liu | Qianqian Wang | Xiaohui Ling | Cong Chen | Fan Ding | Qiong He | Yuanbo Zhang | Shiyi Xiao | Lei Zhou
Source: Nanophotonics (article, 2022-03-31)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Gate‐tuned graphene meta‐devices for dynamically controlling terahertz wavefronts Dynamical controls on terahertz (THz) wavefronts are crucial for many applications, but available mechanism requests tunable elements with sub-micrometer sizes that are difficult to find in the THz regime. Here, different from the <i>local-tuning</i> mechanism, we propose an alternative approach to construct wavefront-control meta-devices combining specifically designed metasurfaces and <i>globally tuned</i> graphene layers. Coupled-mode-theory (CMT) analyses reveal that graphene serves as a tunable loss to drive the whole meta-device to transit from one functional phase to another passing through an intermediate regime, exhibiting distinct far-field (FF) reflection wavefronts. As a proof of concept, we design/fabricate a graphene meta-device and experimentally demonstrate that it can reflect normally incident THz wave to pre-designed directions with different polarizations under appropriate gating voltages. We finally design a graphene meta-device and numerically demonstrate that it can generate vectorial THz beams with continuously varying polarization distributions upon gating. These findings pave the road to realizing a wide range of THz applications, such as sensing, imaging, and wireless communications. Terahertz radiation ; Wavefront ; Graphene ; Polarization (electrochemistry) ; Optoelectronics ; Materials science ; Biasing ; Optics ; Computer science ; Voltage ; Physics ; Nanotechnology Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Antenna Design and Analysis
Authors: Chao Zhu | Qile Fang | Renlan Liu | Wen Dong | Shuang Song | Yi Shen
Source: Environmental Science & Technology (article, 2022-04-27)
Field: Materials Science — Materials Chemistry

Insights into the Crucial Role of Electron and Spin Structures in Heteroatom-Doped Covalent Triazine Frameworks for Removing Organic Micropollutants The water shortage crisis, characterized by organic micropollutants (OMPs), urgently requires new materials and methods to deal with it. Although heteroatom doping has been developed into an effective method to modify carbon nanomaterials for various heterogeneous adsorption and catalytic oxidation systems, the active source regulated by intrinsic electron and spin structures is still obscure. Here, a series of nonmetallic element-doped (such as P, S, and Se) covalent triazine frameworks (CTFs) were constructed and applied to remove organic pollutants using the adsorption-photocatalysis process. The external mass transfer model (EMTM) and the homogeneous surface diffusion model (HSDM) were employed to describe the adsorption process. It was found that sulfur-doped CTF (S-CTF-1) showed a 25.6-fold increase in saturated adsorption capacity (554.7 μmol/g) and a 169.0-fold surge in photocatalytic kinetics (5.07 h<sup>-1</sup>), respectively, compared with the pristine CTF-1. A positive correlation between electron accumulation at the active site (N1 atom) and adsorption energy was further demonstrated with experimental results and theoretical calculations. Meanwhile, the photocatalytic degradation rates were greatly enhanced by forming a built-in electric field driven by spin polarization. In addition, S-CTF-1 still maintained a 98.3% removal of 2,2',4,4'-tetrahydroxybenzophenone (BP-2) micropollutants and 97.6% regeneration after six-cycle sequencing batch treatment in real water matrices. This work established a relation between electron and spin structures for adsorption and photocatalysis, paving a new way to design modified carbon nanomaterials to control OMPs. Heteroatom ; Photocatalysis ; Adsorption ; Chemical engineering ; Catalysis ; Nanomaterials ; Materials science ; Carbon nitride ; Triazine ; Chemistry ; Doping ; Covalent organic framework ; Covalent bond ; Organic chemistry ; Nanotechnology Covalent Organic Framework Applications ; Advanced Photocatalysis Techniques ; Nanomaterials for catalytic reactions
Authors: Siavash Iravani | Rajender S. Varma
Source: Nano-Micro Letters (article, 2022-11-04)
Field: Materials Science — Materials Chemistry

MXene-Based Composites as Nanozymes in Biomedicine: A Perspective MXene-based nanozymes have garnered considerable attention because of their potential environmental and biomedical applications. These materials encompass alluring and manageable catalytic performances and physicochemical features, which make them suitable as (bio)sensors with high selectivity/sensitivity and efficiency. MXene-based structures with suitable electrical conductivity, biocompatibility, large surface area, optical/magnetic properties, and thermal/mechanical features can be applied in designing innovative nanozymes with area-dependent electrocatalytic performances. Despite the advances made, there is still a long way to deploy MXene-based nanozymes, especially in medical and healthcare applications; limitations pertaining the peroxidase-like activity and sensitivity/selectivity may restrict further practical applications of pristine MXenes. Thus, developing an efficient surface engineering tactic is still required to fabricate multifunctional MXene-based nanozymes with excellent activity. To obtain MXene-based nanozymes with unique physicochemical features and high stability, some crucial steps such as hybridization and modification ought to be performed. Notably, (nano)toxicological and long-term biosafety analyses along with clinical translation studies still need to be comprehensively addressed. Although very limited reports exist pertaining to the biomedical potentials of MXene-based nanozymes, the future explorations should transition toward the extensive research and detailed analyses to realize additional potentials of these structures in biomedicine with a focus on clinical and industrial aspects. In this perspective, therapeutic, diagnostic, and theranostic applications of MXene-based nanozymes are deliberated with a focus on future perspectives toward more successful clinical translational studies. The current state-of-the-art biomedical advances in the use of MXene-based nanozymes, as well as their developmental challenges and future prospects are also highlighted. In view of the fascinating properties of MXene-based nanozymes, these materials can open significant new opportunities in the future of bio- and nanomedicine. Biomedicine ; Perspective (graphical) ; Materials science ; Composite material ; Nanotechnology ; Computer science ; Artificial intelligence MXene and MAX Phase Materials ; Advanced Nanomaterials in Catalysis ; Graphene and Nanomaterials Applications
Authors: Yan Sun | Yue Liu | Ruichuan Li | Yanshuai Li | Shizheng Bai
Source: Frontiers in Chemistry (review, 2022-05-19)
Field: Materials Science — Materials Chemistry

Strategies to Improve the Thermoelectric Figure of Merit in Thermoelectric Functional Materials In recent years, thermoelectric functional materials have been widely concerned in temperature difference power generation, electric refrigeration and integrated circui, and so on. In this paper, the design and research progress of thermoelectric materials around lifting <i>ZT</i> value in recent years are reviewed. Optimizing the carrier concentration to improve the Seebeck coefficient, the steady improvement of carrier mobility and the influence of energy band engineering on thermoelectric performance are discussed. In addition, the impact of lattice thermal conductivity on <i>ZT</i> value is also significant. We discuss the general law that the synergistic effect of different dimensions, scales, and crystal structures can reduce lattice thermal conductivity, and introduce the new application of electro-acoustic decoupling in thermoelectric materials. Finally, the research of thermoelectric materials is summarized and prospected in the hope of providing practical ideas for expanding the application and scale industrialization of thermoelectric devices. Thermoelectric effect ; Thermoelectric materials ; Seebeck coefficient ; Engineering physics ; Materials science ; Decoupling (probability) ; Figure of merit ; Thermoelectric generator ; Thermal conductivity ; Thermoelectric cooling ; Mechanical engineering ; Optoelectronics ; Thermodynamics ; Physics ; Engineering ; Composite material Advanced Thermoelectric Materials and Devices ; Thermal Radiation and Cooling Technologies ; Advanced Thermodynamics and Statistical Mechanics
Authors: Zhijia Li | Feilong Jiang | Muxin Yu | Shengchang Li | Lian Chen | Maochun Hong
Source: Nature Communications (article, 2022-04-19)
Field: Materials Science — Materials Chemistry

Achieving gas pressure-dependent luminescence from an AIEgen-based metal-organic framework Abstract Materials exhibiting aggregation-induced emission (AIE) behaviour enable strong emission in solid state and can respond to various external stimuli, which may facilitate the development of materials for optical sensing, bioimaging or optoelectronic devices. Herein, we use an AIE luminogen 2’,5’-diphenyl-[1,1’:4’,1”-terphenyl]-4,4”-dicarboxylic acid as the ligand to prepare an AIEgen-based MOF (metal-organic framework) named FJI-H31. FJI-H31 exhibits bright luminescence under ambient conditions (under air and at room temperature), but almost no emission is observed under vacuum. Our investigation shows that the emission intensity displays a smooth and reversible enhancement with increased gas pressure, which may be attributed to the restriction of intramolecular motion brought by structural deformation under pressure stimulus. Unlike most pressure-responsive MOFs, the luminescence reverts to its original state once gas pressure recovers. By virtue of its unique optical properties, a luminescent MOF with sensing ability of gas-pressure is realized. Luminescence ; Intramolecular force ; Materials science ; Metal ; Metal-organic framework ; Ambient pressure ; Nanotechnology ; Photochemistry ; Optoelectronics ; Chemical physics ; Chemistry ; Stereochemistry ; Physical chemistry Luminescence and Fluorescent Materials ; Metal-Organic Frameworks: Synthesis and Applications ; Nanoplatforms for cancer theranostics
Authors: Yinjie Jiang | Yemin Yu | Ming Kong | Yu Mei | Luotian Yuan | Zhengxing Huang | Kun Kuang | Zhihua Wang | Huaxiu Yao | James Zou | Connor W. Coley | Ying Wei
Source: Engineering (article, 2022-08-20)
Field: Materials Science — Materials Chemistry

Artificial Intelligence for Retrosynthesis Prediction In recent years, there has been a dramatic rise in interest in retrosynthesis prediction with artificial intelligence (AI) techniques. Unlike conventional retrosynthesis prediction performed by chemists and by rule-based expert systems, AI-driven retrosynthesis prediction automatically learns chemistry knowledge from off-the-shelf experimental datasets to predict reactions and retrosynthesis routes. This provides an opportunity to address many conventional challenges, including heavy reliance on extensive expertise, the sub-optimality of routes, and prohibitive computational cost. This review describes the current landscape of AI-driven retrosynthesis prediction. We first discuss formal definitions of the retrosynthesis problem and review the outstanding research challenges therein. We then review the related AI techniques and recent progress that enable retrosynthesis prediction. Moreover, we propose a novel landscape that provides a comprehensive categorization of different retrosynthesis prediction components and survey how AI reshapes each component. We conclude by discussing promising areas for future research. Retrosynthetic analysis ; Artificial intelligence ; Categorization ; Computer science ; Engineering ; Machine learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions