Exploring Reproducible Nonaqueous Scanning Droplet Cell Electrochemistry in Model Battery Chemistries.

The discovery and optimization of new materials for energy storage are essential for a sustainable future. High-throughput experimentation (HTE) using a scanning droplet cell (SDC) is suitable for the rapid screening of prospective material candidates and effective variation of investigated parameters over a millimeter-scale area. Herein, we explore the transition and challenges for SDC electrochemistry from aqueous toward aprotic electrolytes and address pitfalls related to reproducibility in such high-throughput systems. Specifically, we explore whether reproducibilities comparable to those for millimeter half-cells are achievable on the millimeter half-cell level than for full cells. To study reproducibility in half-cells as a first screening step, this study explores the selection of appropriate cell components, such as reference electrodes (REs) and the use of masking techniques for working electrodes (WEs) to achieve consistent electrochemically active areas. Experimental results on a Li-Au model anode system show that SDC, coupled with a masking approach and subsequent optical microscopy, can mitigate issues related to electrolyte leakage and yield good reproducibility. The proposed methodologies and insights contribute to the advancement of high-throughput battery research, enabling the discovery and optimization of future battery materials with improved efficiency and efficacy.

Greener, Safer and Better Performing Aqueous Binder for Positive Electrode Manufacturing of Sodium Ion Batteries.

P2-type cobalt-free MnNi-based layered oxides are promising cathode materials for sodium-ion batteries (SIBs) due to their high reversible capacity and well chemical stability. However, the phase transformations during repeated (dis)charge steps lead to rapid capacity decay and deteriorated Na+ diffusion kinetics. Moreover, the electrode manufacturing based on polyvinylidene difluoride (PVDF) binder system has been reported with severely defluorination issue as well as the energy intensive and expensive process due to the use of toxic and volatile N-methyl-2-pyrrolidone (NMP) solvent. It calls for designing a sustainable, better performing, and cost-effective binder for positive electrode manufacturing. In this work, we investigated inorganic sodium metasilicate (SMS) as a viable binder in conjunction with P2-Na0.67Mn0.55Ni0.25Fe0.1Ti0.1O2 (NMNFT) cathode material for SIBs. The NMNFT-SMS electrode delivered a superior electrochemical performance compared to carboxy methylcellulose (CMC) and PVDF based electrodes with a reversible capacity of ~161 mAh/g and retaining ~83 % after 200 cycles. Lower cell impedance and faster Na+ diffusion was also observed in this binder system. Meanwhile, with the assistance of TEM technique, SMS is suggested to form a uniform and stable nanoscale layer over the cathode particle surface, protecting the particle from exfoliation/cracking due to electrolyte attack. It effectively maintained the electrode connectivity and suppressed early phase transitions during cycling as confirmed by operando XRD study. With these findings, SMS binder can be proposed as a powerful multifunctional binder to enable positive electrode manufacturing of SIBs and to overall reduce battery manufacturing costs.

Modification of Al Surface via Acidic Treatment and its Impact on Plating and Stripping.

Amorphous Al2 O3 film that naturally exists on any Al substrate is a critical bottleneck for the cyclic performance of metallic Al in rechargeable Al batteries. The so-called electron/ion insulator Al oxide slows down the anode's activation and hinders Al plating/stripping. The Al2 O3 film induces different surface properties (roughness and microstructure) on the metal. Al foils present two optically different sides (shiny and non-shiny), but their surface properties and influence on plating and stripping have not been studied so far. Compared to the shiny side, the non-shiny one has a higher (~28 %) surface roughness, and its greater concentration of active sites (for Al plating and stripping) yields higher current densities. Immersion pretreatments in Ionic-Liquid/AlCl3 -based electrolyte with various durations modify the surface properties of each side, forming an electrode-electrolyte interphase layer rich in Al, Cl, and N. The created interphase layer provides more tunneling paths for better Al diffusion upon plating and stripping. After 500 cycles, dendritic Al deposition, generated active sites, and the continuous removal of the Al metal and oxide cause accelerated local corrosion and electrode pulverization. We highlight the mechanical surface properties of cycled Al foil, considering the role of immersion pretreatment and the differences between the two sides.

High-throughput, combinatorial synthesis of multimetallic nanoclusters.

Multimetallic nanoclusters (MMNCs) offer unique and tailorable surface chemistries that hold great potential for numerous catalytic applications. The efficient exploration of this vast chemical space necessitates an accelerated discovery pipeline that supersedes traditional "trial-and-error" experimentation while guaranteeing uniform microstructures despite compositional complexity. Herein, we report the high-throughput synthesis of an extensive series of ultrafine and homogeneous alloy MMNCs, achieved by 1) a flexible compositional design by formulation in the precursor solution phase and 2) the ultrafast synthesis of alloy MMNCs using thermal shock heating (i.e., ∼1,650 K, ∼500 ms). This approach is remarkably facile and easily accessible compared to conventional vapor-phase deposition, and the particle size and structural uniformity enable comparative studies across compositionally different MMNCs. Rapid electrochemical screening is demonstrated by using a scanning droplet cell, enabling us to discover two promising electrocatalysts, which we subsequently validated using a rotating disk setup. This demonstrated high-throughput material discovery pipeline presents a paradigm for facile and accelerated exploration of MMNCs for a broad range of applications.

Benchmarking the acceleration of materials discovery by sequential learning.

Sequential learning (SL) strategies, i.e. iteratively updating a machine learning model to guide experiments, have been proposed to significantly accelerate materials discovery and research. Applications on computational datasets and a handful of optimization experiments have demonstrated the promise of SL, motivating a quantitative evaluation of its ability to accelerate materials discovery, specifically in the case of physical experiments. The benchmarking effort in the present work quantifies the performance of SL algorithms with respect to a breadth of research goals: discovery of any "good" material, discovery of all "good" materials, and discovery of a model that accurately predicts the performance of new materials. To benchmark the effectiveness of different machine learning models against these goals, we use datasets in which the performance of all materials in the search space is known from high-throughput synthesis and electrochemistry experiments. Each dataset contains all pseudo-quaternary metal oxide combinations from a set of six elements (chemical space), the performance metric chosen is the electrocatalytic activity (overpotential) for the oxygen evolution reaction (OER). A diverse set of SL schemes is tested on four chemical spaces, each containing 2121 catalysts. The presented work suggests that research can be accelerated by up to a factor of 20 compared to random acquisition in specific scenarios. The results also show that certain choices of SL models are ill-suited for a given research goal resulting in substantial deceleration compared to random acquisition methods. The results provide quantitative guidance on how to tune an SL strategy for a given research goal and demonstrate the need for a new generation of materials-aware SL algorithms to further accelerate materials discovery.

Synthesis, optical imaging, and absorption spectroscopy data for 179072 metal oxides.

Optical absorption spectroscopy is an important materials characterization for applications such as solar energy generation. This data descriptor describes the to date (Dec 2018) largest publicly available curated materials science dataset for near infrared to near UV (UV-Vis) light absorbance, composition and processing properties of metal oxides. By supplying the complete synthesis and processing history of each of the 179072 samples from 99965 unique compositions we believe the dataset will enable the community to develop predictive models for materials, such as prediction of optical properties based on composition and processing, and ultimately serve as a benchmark dataset for continued integration of machine learning in materials science. The dataset is also a resource for identifying materials composition and synthesis to attain specific optical properties.

Machine learning of optical properties of materials - predicting spectra from images and images from spectra.

As the materials science community seeks to capitalize on recent advancements in computer science, the sparsity of well-labelled experimental data and limited throughput by which it can be generated have inhibited deployment of machine learning algorithms to date. Several successful examples in computational chemistry have inspired further adoption of machine learning algorithms, and in the present work we present autoencoding algorithms for measured optical properties of metal oxides, which can serve as an exemplar for the breadth and depth of data required for modern algorithms to learn the underlying structure of experimental materials science data. Our set of 178 994 distinct materials samples spans 78 distinct composition spaces, includes 45 elements, and contains more than 80 000 unique quinary oxide and 67 000 unique quaternary oxide compositions, making it the largest and most diverse experimental materials set utilized in machine learning studies. The extensive dataset enabled training and validation of 3 distinct models for mapping between sample images and absorption spectra, including a conditional variational autoencoder that generates images of hypothetical materials with tailored absorption properties. The absorption patterns auto-generated from sample images capture the salient features of ground truth spectra, and band gap energies extracted from these auto-generated patterns are quite accurate with a mean absolute error of 180 meV, which is the approximate uncertainty from traditional extraction of the band gap energy from measurements of the full transmission and reflection spectra. Optical properties of materials are not only ubiquitous in materials applications but also emblematic of the confluence of underlying physical phenomena yielding the type of complex data relationships that merit and benefit from neural network-type modelling.

Progress and prospects for accelerating materials science with automated and autonomous workflows.

Accelerating materials research by integrating automation with artificial intelligence is increasingly recognized as a grand scientific challenge to discover and develop materials for emerging and future technologies. While the solid state materials science community has demonstrated a broad range of high throughput methods and effectively leveraged computational techniques to accelerate individual research tasks, revolutionary acceleration of materials discovery has yet to be fully realized. This perspective review presents a framework and ontology to outline a materials experiment lifecycle and visualize materials discovery workflows, providing a context for mapping the realized levels of automation and the next generation of autonomous loops in terms of scientific and automation complexity. Expanding autonomous loops to encompass larger portions of complex workflows will require integration of a range of experimental techniques as well as automation of expert decisions, including subtle reasoning about data quality, responses to unexpected data, and model design. Recent demonstrations of workflows that integrate multiple techniques and include autonomous loops, combined with emerging advancements in artificial intelligence and high throughput experimentation, signal the imminence of a revolution in materials discovery.

Charge Carrier Lifetimes in Cr-Fe-Al-O Thin Films.

The effect of compositional variation on charge carrier lifetimes of Cr1Fe0.84Al0.16O3, a promising material for solar water splitting recently identified using combinatorial materials science, is explored using ultrafast time-resolved optical reflectance. The transient signal can be described by a biexponential decay, where the shorter time constant varies over 1 order of magnitude with changing Cr content while the longer one stays constant. Intrinsic performance limitations such as a low charge carrier mobility on the order of 10-3 cm2/(Vs) are identified. Charge carrier lifetime and mobility are discussed as screening criteria for solar water splitting materials.

Alkaline-stable nickel manganese oxides with ideal band gap for solar fuel photoanodes.

Combinatorial (photo)electrochemical studies of the (Ni-Mn)Ox system reveal a range of promising materials for oxygen evolution photoanodes. X-ray diffraction, quantum efficiency, and optical spectroscopy mapping reveal stable photoactivity of NiMnO3 in alkaline conditions with photocurrent onset commensurate with its 1.9 eV direct band gap. The photoactivity increases upon mixture with 10-60% Ni6MnO8 providing an example of enhanced charge separation via heterojunction formation in mixed-phase thin film photoelectrodes. Density functional theory-based hybrid functional calculations of the band edge energies in this oxide reveal that a somewhat smaller than typical fraction of exact exchange is required to explain the favorable valence band alignment for water oxidation.

Fe-Cr-Al containing oxide semiconductors as potential solar water-splitting materials.

A high-throughput thin film materials library for Fe-Cr-Al-O was obtained by reactive magnetron cosputtering and analyzed with automated EDX and XRD to elucidate compositional and structural properties. An automated optical scanning droplet cell was then used to perform photoelectrochemical measurements of 289 compositions on the library, including electrochemical stability, potentiodynamic photocurrents and photocurrent spectroscopy. The photocurrent onset and open circuit potentials of two semiconductor compositions (n-type semiconducting: Fe51Cr47Al2Ox, p-type semiconducting Fe36.5Cr55.5Al8Ox) are favorable for water splitting. Cathodic photocurrents are observed at 1.0 V vs RHE for the p-type material exhibiting an open circuit potential of 0.85 V vs RHE. The n-type material shows an onset of photocurrents at 0.75 V and an open circuit potential of 0.6 V. The p-type material showed a bandgap of 1.55 eV, while the n-type material showed a bandgap of 1.97 eV.