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Graphical abstract Abstract The Materials Genome Initiative (MGI) has heralded a sea change in the philosophy of materials design. In an increasing number of applications, the successful deployment of novel materials has benefited from the use of computational methodologies, data descriptors, and machine learning. Polymers have long suffered from a lack of data on electronic, mechanical, and dielectric properties across large chemical spaces, causing a stagnation in the set of suitable candidates for various applications. Extensive efforts over the last few years have seen the fruitful application of MGI principles toward the accelerated discovery of attractive polymer dielectrics for capacitive energy storage. Here, we review these efforts, highlighting the importance of computational data generation and screening, targeted synthesis and characterization, polymer fingerprinting and machine-learning prediction models, and the creation of an online knowledgebase to guide ongoing and future polymer discovery and design. We lay special emphasis on the fingerprinting of polymers in terms of their genome or constituent atomic and molecular fragments, an idea that pays homage to the pioneers of the human genome project who identified the basic building blocks of the human DNA. By scoping the polymer genome, we present an essential roadmap for the design of polymer dielectrics, and provide future perspectives and directions for expansions to other polymer subclasses and properties. [ABSTRACT FROM AUTHOR]

Highlights: [•] Few-layer graphene with a low oxygen content was synthesized by a two-step process. [•] Expanded graphite was subjected to solvothermal treatment, followed by microwave radiation. [•] The as-fabricated graphene sheets exhibited a high electrical conductivity. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) based approaches are beginning to impact several domains of human life, science and technology. Polymer informatics is one such domain where AI and machine learning (ML) tools are being used in the efficient development, design and discovery of polymers. Surrogate models are trained on available polymer data for instant property prediction, allowing screening of promising polymer candidates with specific target property requirements. Questions regarding synthesizability, and potential (retro)synthesis steps to create a target polymer, are being explored using statistical means. Data-driven strategies to tackle unique challenges resulting from the extraordinary chemical and physical diversity of polymers at small and large scales are being explored. Other major hurdles for polymer informatics are the lack of widespread availability of curated and organized data, and approaches to create machine-readable representations that capture not just the structure of complex polymeric situations but also synthesis and processing conditions. Methods to solve inverse problems, wherein polymer recommendations are made using advanced AI algorithms that meet application targets, are being investigated. As various parts of the burgeoning polymer informatics ecosystem mature and become integrated, efficiency improvements, accelerated discoveries and increased productivity can result. Here, we review emergent components of this polymer informatics ecosystem and discuss imminent challenges and opportunities. [ABSTRACT FROM AUTHOR]

The fidelity of data is of paramount importance in the construction of reliable and accurate machine learning (ML) models. Low-fidelity data, although noisy, can usually be obtained for a large number of material samples. High-fidelity data, on the other hand, is time-consuming and oftentimes, only available for a limited number of target samples. While the former can provide useful information to help generalize the ML models over large materials space, the latter is useful to build more accurate surrogate models. Information fusion schemes that utilize the data available at multiple levels of fidelity can outperform traditional single fidelity based ML methods, such as Gaussian process regression. In this work, a variant of the multi-fidelity information fusion scheme, namely multi-fidelity co-kriging, is used to build powerful prediction models of polymer bandgaps. To benchmark this strategy, we utilize a bandgap dataset of 382 polymers, obtained at two levels of fidelity: using the Perdew-Burke-Ernzerhof (PBE) exchange-correlational functional ("low-fidelity") and the Heyd-Scuseria-Ernzerhof (HSE06) functional("high-fidelity") of density functional theory. The multi-fidelity model, trained on both PBE and HSE06 data, outperforms a single-fidelity Gaussian process regression model trained on just HSE06 band-gaps in a number of scenarios and is also able to generalize better to a more diverse chemical space. [ABSTRACT FROM AUTHOR]

Metal organic frameworks (MOFs) are attractive materials to generate multifunctional catalysts for the electrocatalytic reduction of CO 2 to hydrocarbons. Here we report the synthesis of Cu and Zn modified Al-fumarate (Al-fum) MOFs, in which Zn promotes the selective reduction of CO 2 to CO and Cu promotes CO reduction to oxygenates and hydrocarbons in an electrocatalytic cascade. Cu and Zn nanoparticles (NPs) were introduced to the Al-fum MOF by a double solvent method to promote in-pore metal deposition, and the resulting reduced Cu-Zn@Al-fum drop-cast on a hydrophobic gas diffusion electrode for electrochemical study. Cu-Zn@Al-fum is active for CO 2 electroreduction, with the Cu and Zn loading influencing the product yields. The highest faradaic efficiency (FE) of 62% is achieved at -1.0 V vs. RHE for the conversion of CO 2 into CO, HCOOH, CH 4 , C 2 H 4 and C 2 H 5 OH, with a FE of 28% to CH 4 , C 2 H 4 and C 2 H 5 OH at pH 6.8. Al-fum MOF is a chemically robust matrix to disperse Cu and Zn NPs, improving electrocatalyst lifetime during CO 2 reduction by minimizing transition metal aggregation during electrode operation., Competing Interests: There are no conflicts of interest to declare., (This journal is © The Royal Society of Chemistry.)

Developing a large database of polymers structures and properties, for which suitable polymer structural models are a prerequisite, is critical for polymer informatics. We present a simple strategy, referred to as polymer structure predictor (PSP), for predicting the crystal structural models of linear polymers, given their chain-level atomic connectivity information. The PSP, which was designed specifically for polymers, relies on properly defining and sampling the configuration space. Using this approach, we have successfully recovered eight known crystal structures of six common linear polymers, including polyethylene, polyvinylidene fluoride, poly(vinyl chloride), poly( p -phenylene sulfide), polyacrylonitrile, and poly-2,5-benzoxazole, while discovering some new stable structures of three of them, i.e., polyvinylidene fluoride, polyacrylonitrile, and poly( p -phenylene sulfide). The PSP is very simple, highly scalable, suitable for automatic workflows, and comparable to the best major structure prediction method in terms of efficiency in polymer crystal structure prediction. Although challenges in comprehensively accounting for possible chain-level conformations remain, the PSP will be very useful in efficiently generating polymer data and strengthening the emerging polymer informatics ecosystems.

Electroreduction of CO 2 to CO is one of the simplest ways to valorise CO 2 as a source of carbon. Herein, a cheap, robust, Cu-based hybrid catalyst consisting of a polymer of Cu phthalocyanine coated on carbon nanotubes, which proved to be selective for CO production (80 % faradaic yield) at relatively low overpotentials, was developed. Polymerisation of Cu phthalocyanine was shown to have a drastic effect on the selectivity of the reaction because molecular Cu phthalocyanine was instead selective for proton reduction under the same conditions. Although the material only showed isolated Cu sites in phthalocyanine-like CuN 4 coordination, in situ and operando X-ray absorption spectroscopy showed that, under operating conditions, the Cu atoms were fully converted to Cu nanoparticles, which were likely the catalytically active species. Interestingly, this restructuring of the metal sites was reversible., (© 2020 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Within materials informatics, a rapidly developing subfield of materials research, past (curated) data are mined and learned for either discovering new materials or identifying new functionalities of known materials. This paper provides an example of this process. Starting from a recently developed (very diverse) dataset which includes 1346 hybrid organic-inorganic perovskites (HOIPs), we downselect a subset of 350 three dimensional HOIPs to a final set of four lead-free HOIPs, including CH 3 NH 3 SnI 3 , HC(NH 2 ) 2 SnI 3 , NH 2 NH 3 SnI 3 , and NH 2 (CH 2 ) 3 SnI 3 , in which the first two were experimentally synthesized and the others remain hypothetical. Using first-principles based computational methods, we show that these HOIPs have preferable electronic band structures and carrier effective mass, good optical properties, and high spectroscopic limited maximum efficiency. Compared to the experimental data, we find that state-of-the-art numerical methods can predict the electronic and optical properties fairly well, while the current model for the spectroscopic limited maximum efficiency is inadequate for capturing the power conversion efficiency of a solar absorber. We suggest that the HOIP dataset should be expanded to include larger structures of HOIPs, thereby being more useful for future data-mining and machine-learning approaches.

Single Ni 1- x Cu x TiO 3 (0.05 ≤ x ≤ 0.2) Ilmenite-type phases were successfully prepared through a solid-state reaction route using divalent metal nitrates as precursors and characterized. Their electrocatalytic performance for oxygen evolution reaction (OER) in alkaline media is presented. The Cu content was determined (0.05 ≤ x ≤ 0.2) by X-ray diffraction. A thorough powder neutron diffraction study was carried out to identify the subtle changes caused by copper substitution in the structure of NiTiO 3 . The evolution of the optical and magnetic properties with the Cu content was also investigated on the raw micrometer-sized particles. A reduction in particle size down to ≈15 nm was achieved by ball-milling the raw powder prepared by the solid-state reaction. The best catalytic activity for OER was obtained for nanometer-sized particles of Ni 0.8 Cu 0.2 TiO 3 drop-casted on the Cu plate. For this electrode, a current density of 10 mA cm -2 for oxygen production was generated at 345 and 470 mV applied overpotentials with 1 and 0.1 M NaOH solutions as electrolytes, respectively. The catalyst retained this OER activity at 10 mA cm -2 for long-term electrolysis with a faradic efficiency of 90% for O 2 production in a 0.1 M NaOH electrolyte.

Dielectric polymers are widely used in electronics and energy technologies, but their performance is severely limited by the electrical breakdown under a high electric field. Dielectric breakdown is commonly understood as an avalanche of processes such as carrier multiplication and defect generation that are triggered by field-accelerated hot electrons and holes. However, how these processes are initiated remains elusive. Here, nonadiabatic quantum molecular dynamics simulations reveal microscopic processes induced by hot electrons and holes in a slab of an archetypal dielectric polymer, polyethylene, under an electric field of 600 MV/m. We found that electronic-excitation energy is rapidly dissipated within tens of femtoseconds because of strong electron-phonon scattering, which is consistent with quantum-mechanical perturbation calculations. This in turn excites other electron-hole pairs to cause carrier multiplication. We also found that the key to chemical damage is localization of holes that travel to a slab surface and weaken carbon-carbon bonds on the surface. Such quantitative information can be incorporated into first-principles-informed, predictive modeling of dielectric breakdown.

The photochemical reduction of CO 2 to formic acid catalyzed by a series of [Rh(4,4'-R-bpy)(Cp*)Cl] + and [Rh(5,5'-COOH-bpy)(Cp*)Cl] + complexes (Cp* = pentamethylcyclopentadienyl, bpy = 2,2'-bipyridine, and R = OCH 3 , CH 3 , H, COOC 2 H 5 , CF 3 , NH 2 , or COOH) was studied to assess how modifications in the electronic structure of the catalyst affect its selectivity, defined as the HCOOH:H 2 product ratio. A direct molecular-level influence of the functional group on the initial reaction rate for CO 2 versus proton reduction reactions was established. Density functional theory computations elucidated for the first time the respective role of the [RhH] and [Cp*H] tautomers, recognizing rhodium hydride as the key player for both reactions. In particular, our calculations explain the observed tendency of electron-donating substituents to favor CO 2 reduction by means of decreasing the hydricity of the Rh-H bond, resulting in a lower hydride transfer barrier toward formic acid production as compared to substituents with an electron-withdrawing nature that favor more strongly the reduction of protons to hydrogen.

Conversion of carbon dioxide into hydrocarbons using solar energy is an attractive strategy for storing such a renewable source of energy into the form of chemical energy (a fuel). This can be achieved in a system coupling a photovoltaic (PV) cell to an electrochemical cell (EC) for CO 2 reduction. To be beneficial and applicable, such a system should use low-cost and easily processable photovoltaic cells and display minimal energy losses associated with the catalysts at the anode and cathode and with the electrolyzer device. In this work, we have considered all of these parameters altogether to set up a reference PV-EC system for CO 2 reduction to hydrocarbons. By using the same original and efficient Cu-based catalysts at both electrodes of the electrolyzer, and by minimizing all possible energy losses associated with the electrolyzer device, we have achieved CO 2 reduction to ethylene and ethane with a 21% energy efficiency. Coupled with a state-of-the-art, low-cost perovskite photovoltaic minimodule, this system reaches a 2.3% solar-to-hydrocarbon efficiency, setting a benchmark for an inexpensive all-earth-abundant PV-EC system., Competing Interests: The authors declare no conflict of interest., (Copyright © 2019 the Author(s). Published by PNAS.)

Bornite materials are naturally occurring systems composed of earth-abundant constituents. Bournonite, a representative of this class of materials, is of interest for thermoelectric applications due to its inherently low thermal conductivity, which has been attributed to the lattice distortions due to stereochemically active electron lone pair distributions. In this computational and experimental study, we present analyses of the lattice structure, electron and phonon dynamics, and charge localization and transfer properties for undoped and Ni and Zn doped bournonites. The results from our simulations reveal complex relations between bond length and bond angle characteristics, chemical bonding, and charge transfer upon doping. Analysis of the experimental results indicate that a microscopic description for bournonite and its doped compositions is necessary for a complete understanding of these materials, as well as for effective control of the transport properties for targeted applications., (© 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.)

The presence of oxygen vacancy sites fundamentally affects physical and chemical properties of materials. In this study, a dipole-containing interaction between poly(diallyldimethylammonium chloride) PDDA and α-MoO 3 is found to enable high-concentrations of surface oxygen vacancies. Thermal annealing under Ar resulted in negligible reduction of MoO 3 to MoO 3- x with x = 0.03 at 600 °C. In contrast, we show that the thermochemical reaction with PDDA polyelectrolyte under Ar can significantly reduce MoO 3 to MoO 3- x with x = 0.36 (MoO 2.64 ) at 600 °C. Thermal annealing under H 2 gas enhanced the substoichiometry of MoO 3- x from x = 0.62 to 0.98 by using PDDA at the same conditions. Density functional theory calculations, supported by experimental analysis, suggest that the vacancy sites are created through absorption of terminal site oxygen (O t ) upon decomposition of the N-C bond in the pentagonal ring of PDDA during the thermal treatment. O t atoms are absorbed as ionic O - and neutral O 2- , creating Mo 5+ -v O · and Mo 4+ -v O ·· vacancy bipolarons and polarons, respectively. X-ray photoemission spectroscopy peak analysis indicates the ratio of charged to neutral molybdenum ions in the PDDA-processed samples increased from Mo 4+ /Mo 6+ = 1.0 and Mo 5+ /Mo 6+ = 3.3 when reduced at 400 °C to Mo 4+ /Mo 6+ = 3.7 and Mo 5+ /Mo 6+ = 2.6 when reduced at 600 °C. This is consistent with our ab initio calculation where the Mo 4+ -v O ·· formation energy is 0.22 eV higher than that for Mo 5+ -v O · in the bulk of the material and 0.02 eV higher on the surface. This study reveals a new paradigm for effective enhancement of surface oxygen vacancy concentrations essential for a variety of technologies including advanced energy conversion applications such as electrochemical energy storage, catalysis, and low-temperature thermochemical water splitting.

The electronic structure of an insulator encodes essential signatures of its short-term electrical performance and long-term reliability. A critical long-standing challenge though is that key features of the electronic structure of an insulator (and its evolution) under realistic conditions have not been entirely accessible, either via experimental or computational approaches, due to the inherent complexities involved. In this comprehensive study, we reveal the role of chemical and morphological imperfections that inevitably exist within the technologically important prototypical and pervasive insulator, polyethylene (PE), and at electrode/PE interfaces. Large-scale density functional theory computations and long-time molecular dynamics simulations were employed to accurately recover, explain and unravel a wide variety of experimental data obtained during the electrical degradation of PE. This scheme has allowed us to directly and realistically address the role of chemical, morphological and interfacial complexity in determining electronic structure. These efforts take us a step closer to understanding and potentially controlling dielectric degradation and breakdown.

Hybrid organic-inorganic perovskites (HOIPs) have been attracting a great deal of attention due to their versatility of electronic properties and fabrication methods. We prepare a dataset of 1,346 HOIPs, which features 16 organic cations, 3 group-IV cations and 4 halide anions. Using a combination of an atomic structure search method and density functional theory calculations, the optimized structures, the bandgap, the dielectric constant, and the relative energies of the HOIPs are uniformly prepared and validated by comparing with relevant experimental and/or theoretical data. We make the dataset available at Dryad Digital Repository, NoMaD Repository, and Khazana Repository (http://khazana.uconn.edu/), hoping that it could be useful for future data-mining efforts that can explore possible structure-property relationships and phenomenological models. Progressive extension of the dataset is expected as new organic cations become appropriate within the HOIP framework, and as additional properties are calculated for the new compounds found.

To use water as the source of electrons for proton or CO 2 reduction within electrocatalytic devices, catalysts are required for facilitating the proton-coupled multi-electron oxygen evolution reaction (OER, 2 H 2 O→O 2 +4 H + +4 e - ). These catalysts, ideally based on cheap and earth abundant metals, have to display high activity at low overpotential and good stability and selectivity. While numerous examples of Co, Mn, and Ni catalysts were recently reported for water oxidation, only few examples were reported using copper, despite promising efficiencies. A rationally designed nanostructured copper/copper oxide electrocatalyst for OER is presented. This material derives from conductive copper foam passivated by a copper oxide layer and further nanostructured by electrodeposition of CuO nanoparticles. The generated electrodes are highly efficient for catalyzing selective water oxidation to dioxygen with an overpotential of 290 mV at 10 mA cm -2 in 1 m NaOH solution., (© 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Copper is currently extensively studied because it provides promising electrodes for carbon dioxide electroreduction. The original combination, reported here, of a nanostructured porous dendritic Cu-based material, characterized by electron microcopy (SEM, TEM) and X-ray diffraction methods, and a water/ionic liquid mixture as the solvent, contributing to CO 2 solubilization and activation, results in a remarkably efficient (large current densities at low overpotentials), stable and selective (large faradic yields) electrocatalytic system for the conversion of CO 2 into formic acid, a product with a variety of uses. These results provide new directions for the further improvement of Cu electrodes.

Hollow spherical structures of ternary bismuth molybdenum oxide doped with samarium (Bi 2-x Sm x MoO 6 ) were successfully synthesized via development of a Pluronic P123 (PEO 20 -PPO 70 -PEO 20 )-assisted solvothermal technique. Density functional theory calculations have been performed to improve our understanding of the effects of Sm doping on the electronic band structure, density of states, and band gap of the material. The calculations for 0 ≤ x ≤ 0.3 revealed a considerably flattened conduction band minimum near the Γ point, suggesting that the material can be considered to possess a quasi-direct band gap. In contrast, for x = 0.5, the conduction band minimum is deflected toward the U point, making it a distinctly indirect band gap material. The effects of a hollow structure as well as Sm substitution on the absorbance and fluorescence properties of the materials produced increased emission intensities at low Sm concentrations (x = 0.1 and 0.3), with x = 0.1 displaying a peak photoluminescence intensity 13.2 times higher than for the undoped bulk sample. Subsequent increases in the Sm concentration resulted in quenching of the emission intensity, indicative of the onset of a quasi-direct-to-indirect electronic band transition. These results indicate that both mesoscale structuring and Sm doping will be promising routes for tuning optoelectronic properties for future applications such as catalysis and photocatalysis.

Copper/copper oxide (Cu/Cu 2 O) electrodes are known to display interesting electrocatalytic performances for the reduction of CO 2 , and thus, deserve further investigation for optimization. Here, we show that the addition of nitrogen-based organic additives greatly improves the activity of these electrodes (higher current densities, greater selectivity, and higher faradaic yields). The best effector is found to be tetramethyl cyclam. For example, electrolysis at -2.0 V versus Fc + /Fc in CO 2 -saturated DMF/H 2 O (99:1, v/v) in the presence of this effector results in formic acid with almost 90 % faradaic yield. SEM and XPS analysis of the electrode surface reveals that the organic additive promotes the formation of active Cu 0 nanoparticles from Cu 2 O during electrolysis. This simple approach provides a straightforward strategy toward the optimization of Cu/Cu 2 O electrodes., (© 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

A gold-based nanostructure has been demonstrated as promising materials for the selective electroreduction of CO2 to CO in aqueous conditions. In this work, we present a carbon nanotube-gold nanohybrid as a selective and efficient electrocatalyst for the reduction of CO2 in 0.5 m NaHCO3 . The hybrid material exhibits remarkable activity with a current density of 10 mA cm(-2) at -0.55 V versus standard hydrogen electrode with a stable CO production rate (0.52 μmol s(-1) ) after 4 h electrolysis. Monodispersed gold nanoparticles anchored on carbon nanotubes through a layer-by-layer method allows very little Au loading and thus minimization of the cost of electrode fabrication with a mass activity up to 100 A g(-1) at -0.55 V versus reversible hydrogen electrode. It is 33 times higher than a previous report for monodisperse Au nanoparticles (3 A g(-1) ) while ensuring selectivity (70 % faradaic yield of CO) at comparable reduction potential., (© 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Authors report a retrospective study of all cases of indoramin-only poisoning notified to the Paris poison Centre from 1986 to 2010. Fifty five cases of indoramin self-poisoning were included: 40 adults and 15 children. The mean supposed ingested dose was about 701mg±464mg. ECG showed a prolonged QTc interval (equal to or greater than 0.50s) in 30% of patients. The lowest observed dose for prolonged QTc was 625mg. This series includes two cases of seizures occurring around two hours after ingestion of 900 and 2 250mg of indoramin. A review of the literature showed cardiac disorders, with a delayed mechanism of action up until 18hours after ingestion. Therefore, rapid medical resuscitation and prolonged cardiac monitoring for at least 24hours after ingestion of 625mg are recommended., (Copyright © 2012 Société Française de Pharmacologie et de Thérapeutique. Publié par Elsevier Masson SAS.)