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Electrocatalysis plays a central role in clean energy conversion, enabling a number of sustainable processes for future technologies. This review discusses design strategies for state-of-the-art heterogeneous electrocatalysts and associated materials for several different electrochemical transformations involving water, hydrogen, and oxygen, using theory as a means to rationalize catalyst performance. By examining the common principles that govern catalysis for different electrochemical reactions, we describe a systematic framework that clarifies trends in catalyzing these reactions, serving as a guide to new catalyst development while highlighting key gaps that need to be addressed. We conclude by extending this framework to emerging clean energy reactions such as hydrogen peroxide production, carbon dioxide reduction, and nitrogen reduction, where the development of improved catalysts could allow for the sustainable production of a broad range of fuels and chemicals.

Today's best perovskite solar cells use a mixture of formamidinium and methylammonium as the monovalent cations. With the addition of inorganic cesium, the resulting triple cation perovskite compositions are thermally more stable, contain less phase impurities and are less sensitive to processing conditions. This enables more reproducible device performances to reach a stabilized power output of 21.1% and ∼18% after 250 hours under operational conditions. These properties are key for the industrialization of perovskite photovoltaics.

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Despite the dedicated search for novel catalysts for fuel cell applications, the intrinsic oxygen reduction reaction (ORR) activity of materials has not improved significantly over the past decade. Here, we review the role of theory in understanding the ORR mechanism and highlight the descriptor-based approaches that have been used to identify catalysts with increased activity. Specifically, by showing that the performance of the commonly studied materials (e.g., metals, alloys, carbons, etc.) is limited by unfavorable scaling relationships (for binding energies of reaction intermediates), we present a number of alternative strategies that may lead to the design and discovery of more promising materials for ORR.

Highly active catalysts for the oxygen evolution reaction (OER) are required for the development of photoelectrochemical devices that generate hydrogen efficiently from water using solar energy. Here, we identify the origin of a 500-fold OER activity enhancement that can be achieved with mixed (Ni,Fe)oxyhydroxides (Ni1–xFexOOH) over their pure Ni and Fe parent compounds, resulting in one of the most active currently known OER catalysts in alkaline electrolyte. Operando X-ray absorption spectroscopy (XAS) using high energy resolution fluorescence detection (HERFD) reveals that Fe3+ in Ni1–xFexOOH occupies octahedral sites with unusually short Fe–O bond distances, induced by edge-sharing with surrounding [NiO6] octahedra. Using computational methods, we establish that this structural motif results in near optimal adsorption energies of OER intermediates and low overpotentials at Fe sites. By contrast, Ni sites in Ni1–xFexOOH are not active sites for the oxidation of water.

Mechanisms in Methanol Catalysis The industrial production of methanol from hydrogen and carbon monoxide depends on the use of copper and zinc oxide nanoparticles on alumina oxide supports. This catalyst is “structure sensitive”; its activity can vary by orders of magnitude, depending on how it is prepared. Behrens et al. (p. 893 , published online 19 April; see the Perspective by Greeley ) used a combination of bulk and surface-sensitive analysis and imaging methods—along with insights from density functional theory calculations—to study several catalysts, including the one similar to that used industrially. High activity depended on the presence of steps on the copper nanoparticles stabilized by defects such as stacking faults. Partial coverage of the copper nanoparticles with zinc oxide was critical for stabilizing surface intermediates such as HCO and lowering energetic barriers to the methanol product.

Earth-abundant first-row (3d) transition metal-based catalysts have been developed for the oxygen-evolution reaction (OER); however, they operate at overpotentials substantially above thermodynamic requirements. Density functional theory suggested that non-3d high-valency metals such as tungsten can modulate 3d metal oxides, providing near-optimal adsorption energies for OER intermediates. We developed a room-temperature synthesis to produce gelled oxyhydroxides materials with an atomically homogeneous metal distribution. These gelled FeCoW oxyhydroxides exhibit the lowest overpotential (191 millivolts) reported at 10 milliamperes per square centimeter in alkaline electrolyte. The catalyst shows no evidence of degradation after more than 500 hours of operation. X-ray absorption and computational studies reveal a synergistic interplay between tungsten, iron, and cobalt in producing a favorable local coordination environment and electronic structure that enhance the energetics for OER.

We describe an augmented reality conferencing system which uses the overlay of virtual images on the real world. Remote collaborators are represented on virtual monitors which can be freely positioned about a user in space. Users can collaboratively view and interact with virtual objects using a shared virtual whiteboard. This is possible through precise virtual image registration using fast and accurate computer vision techniques and head mounted display (HMD) calibration. We propose a method for tracking fiducial markers and a calibration method for optical see-through HMD based on the marker tracking.

Oxygen electrochemistry plays a key role in renewable energy technologies such as fuel cells and electrolyzers, but the slow kinetics of the oxygen evolution reaction (OER) limit the performance and commercialization of such devices. Here we report an iridium oxide/strontium iridium oxide (IrO x /SrIrO 3 ) catalyst formed during electrochemical testing by strontium leaching from surface layers of thin films of SrIrO 3 . This catalyst has demonstrated specific activity at 10 milliamps per square centimeter of oxide catalyst (OER current normalized to catalyst surface area), with only 270 to 290 millivolts of overpotential for 30 hours of continuous testing in acidic electrolyte. Density functional theory calculations suggest the formation of highly active surface layers during strontium leaching with IrO 3 or anatase IrO 2 motifs. The IrO x /SrIrO 3 catalyst outperforms known IrO x and ruthenium oxide (RuO x ) systems, the only other OER catalysts that have reasonable activity in acidic electrolyte.

At a time of increased social usage of net and collaborative applications, a robust and detailed theory of social presence could contribute to our understanding of social behavior in mediated environments, allow researchers to predict and measure differences among media interfaces, and guide the design of new social environments and interfaces. A broader theory of social presence can guide more valid and reliable measures. The article reviews, classifies, and critiques existing theories and measures of social presence. A set of criteria and scope conditions is proposed to help remedy limitations in past theories and measures and to provide a contribution to a more robust theory and measure of social presence.

The electrochemical reduction of CO2 into hydrocarbons and alcohols would allow renewable energy sources to be converted into fuels and chemicals. However, no electrode catalysts have been developed that can perform this transformation with a low overpotential at reasonable current densities. In this work, we compare trends in binding energies for the intermediates in CO2 electrochemical reduction and present an activity “volcano” based on this analysis. This analysis describes the experimentally observed variations in transition-metal catalysts, including why copper is the best-known metal electrocatalyst. The protonation of adsorbed CO is singled out as the most important step dictating the overpotential. New strategies are presented for the discovery of catalysts that can operate with a reduced overpotential.

OBJECTIVE: Electroencephalography (EEG) analysis has been an important tool in neuroscience with applications in neuroscience, neural engineering (e.g. Brain-computer interfaces, BCI's), and even commercial applications. Many of the analytical tools used in EEG studies have used machine learning to uncover relevant information for neural classification and neuroimaging. Recently, the availability of large EEG data sets and advances in machine learning have both led to the deployment of deep learning architectures, especially in the analysis of EEG signals and in understanding the information it may contain for brain functionality. The robust automatic classification of these signals is an important step towards making the use of EEG more practical in many applications and less reliant on trained professionals. Towards this goal, a systematic review of the literature on deep learning applications to EEG classification was performed to address the following critical questions: (1) Which EEG classification tasks have been explored with deep learning? (2) What input formulations have been used for training the deep networks? (3) Are there specific deep learning network structures suitable for specific types of tasks? APPROACH: A systematic literature review of EEG classification using deep learning was performed on Web of Science and PubMed databases, resulting in 90 identified studies. Those studies were analyzed based on type of task, EEG preprocessing methods, input type, and deep learning architecture. MAIN RESULTS: For EEG classification tasks, convolutional neural networks, recurrent neural networks, deep belief networks outperform stacked auto-encoders and multi-layer perceptron neural networks in classification accuracy. The tasks that used deep learning fell into five general groups: emotion recognition, motor imagery, mental workload, seizure detection, event related potential detection, and sleep scoring. For each type of task, we describe the specific input formulation, major characteristics, and end classifier recommendations found through this review. SIGNIFICANCE: This review summarizes the current practices and performance outcomes in the use of deep learning for EEG classification. Practical suggestions on the selection of many hyperparameters are provided in the hope that they will promote or guide the deployment of deep learning to EEG datasets in future research.

Theoretical studies of the possibility of forming ammonia electrochemically at ambient temperature and pressure are presented. Density functional theory calculations were used in combination with the computational standard hydrogen electrode to calculate the free energy profile for the reduction of N(2) admolecules and N adatoms on several close-packed and stepped transition metal surfaces in contact with an acidic electrolyte. Trends in the catalytic activity were calculated for a range of transition metal surfaces and applied potentials under the assumption that the activation energy barrier scales with the free energy difference in each elementary step. The most active surfaces, on top of the volcano diagrams, are Mo, Fe, Rh, and Ru, but hydrogen gas formation will be a competing reaction reducing the faradaic efficiency for ammonia production. Since the early transition metal surfaces such as Sc, Y, Ti, and Zr bind N-adatoms more strongly than H-adatoms, a significant production of ammonia compared with hydrogen gas can be expected on those metal electrodes when a bias of -1 V to -1.5 V vs. SHE is applied. Defect-free surfaces of the early transition metals are catalytically more active than their stepped counterparts.

A methodology for semiempirical density functional optimization, using regularization and cross-validation methods from machine learning, is developed. We demonstrate that such methods enable well-behaved exchange-correlation approximations in very flexible model spaces, thus avoiding the overfitting found when standard least-squares methods are applied to high-order polynomial expansions. A general-purpose density functional for surface science and catalysis studies should accurately describe bond breaking and formation in chemistry, solid state physics, and surface chemistry, and should preferably also include van der Waals dispersion interactions. Such a functional necessarily compromises between describing fundamentally different types of interactions, making transferability of the density functional approximation a key issue. We investigate this trade-off between describing the energetics of intramolecular and intermolecular, bulk solid, and surface chemical bonding, and the developed optimization method explicitly handles making the compromise based on the directions in model space favored by different materials properties. The approach is applied to designing the Bayesian error estimation functional with van der Waals correlation (BEEF--vdW), a semilocal approximation with an additional nonlocal correlation term. Furthermore, an ensemble of functionals around BEEF--vdW comes out naturally, offering an estimate of the computational error. An extensive assessment on a range of data sets validates the applicability of BEEF--vdW to studies in chemistry and condensed matter physics. Applications of the approximation and its Bayesian ensemble error estimate to two intricate surface science problems support this.

Using cell-based aptamer selection, we have developed a strategy to use the differences at the molecular level between any two types of cells for the identification of molecular signatures on the surface of targeted cells. A group of aptamers have been generated for the specific recognition of leukemia cells. The selected aptamers can bind to target cells with an equilibrium dissociation constant (K(d)) in the nanomolar-to-picomolar range. The cell-based selection process is simple, fast, straightforward, and reproducible, and, most importantly, can be done without prior knowledge of target molecules. The selected aptamers can specifically recognize target leukemia cells mixed with normal human bone marrow aspirates and can also identify cancer cells closely related to the target cell line in real clinical specimens. The cell-based aptamer selection holds a great promise in developing specific molecular probes for cancer diagnosis and cancer biomarker discovery.

This review presents the state-of-the-art organohalide lead perovskites, which are currently making an immense impact across the photovoltaic community.

ADVERTISEMENT RETURN TO ISSUEPREVArticleNEXTSpecific Ion Effects at the Air/Water InterfacePavel Jungwirth and Douglas J. TobiasView Author Information Institute of Organic Chemistry and Biochemistry, Academy of Sciences of the Czech Republic, and Center for Biomolecules and Complex Molecular Systems, Flemingovo nám. 2, 16610 Prague 6, Czech Republic, and Department of Chemistry and Institute for Surface and Interface Science, University of California, Irvine, California 92697-2025 Cite this: Chem. Rev. 2006, 106, 4, 1259–1281Publication Date (Web):December 23, 2005Publication History Received9 June 2005Published online23 December 2005Published inissue 1 April 2006https://pubs.acs.org/doi/10.1021/cr0403741https://doi.org/10.1021/cr0403741research-articleACS PublicationsCopyright © 2006 American Chemical SocietyRequest reuse permissionsArticle Views12455Altmetric-Citations1160LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail Other access optionsGet e-Alertsclose SUBJECTS:Anions,Interfaces,Ions,Salts,Solution chemistry Get e-Alerts

The presence of layered cobalt oxides has been identified experimentally in Co-based anodes under oxygen-evolving conditions. In this work, we report the results of theoretical investigations of the relative stability of layered and spinel bulk phases of Co oxides, as well as the stability of selected surfaces as a function of applied potential and pH. We then study the oxygen evolution reaction (OER) on these surfaces and obtain activity trends at experimentally relevant electro-chemical conditions. Our calculated volume Pourbaix diagram shows that β-CoOOH is the active phase where the OER occurs in alkaline media. We calculate relative surface stabilities and adsorbate coverages of the most stable low-index surfaces of β-CoOOH: (0001), (0112), and (1014). We find that at low applied potentials, the (1014) surface is the most stable, while the (0112) surface is the more stable at higher potentials. Next, we compare the theoretical overpotentials for all three surfaces and find that the (1014) surface is the most active one as characterized by an overpotential of η = 0.48 V. The high activity of the (1014) surface can be attributed to the observation that the resting state of Co in the active site is Co(3+) during the OER, whereas Co is in the Co(4+) state in the less active surfaces. Lastly, we demonstrate that the overpotential of the (1014) surface can be lowered further by surface substitution of Co by Ni. This finding could explain the experimentally observed enhancement in the OER activity of Ni(y)Co(1-y)O(x) thin films with increasing Ni content. All energetics in this work were obtained from density functional theory using the Hubbard-U correction.

The electrochemical production of NH3 under ambient conditions represents an attractive prospect for sustainable agriculture, but electrocatalysts that selectively reduce N2 to NH3 remain elusive. In this work, we present insights from DFT calculations that describe limitations on the low-temperature electrocatalytic production of NH3 from N2 . In particular, we highlight the linear scaling relations of the adsorption energies of intermediates that can be used to model the overpotential requirements in this process. By using a two-variable description of the theoretical overpotential, we identify fundamental limitations on N2 reduction analogous to those present in processes such as oxygen evolution. Using these trends, we propose new strategies for catalyst design that may help guide the search for an electrocatalyst that can achieve selective N2 reduction.

Ionic displacement modifying the electric field in the device is found as most likely reason for the hysteresis which is examined by separating fast and slow processes and comparing devices with and without blocking layer.