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High catalytic efficiency and long‐term stability are two main components for the performance assessment of an electrocatalyst. Previous attention has been paid more to efficiency other than stability. The present work is focused on the study of the stability processed on the FeCoNiRu high‐entropy alloy (HEA) in correlation with its catalytic efficiency. This catalyst has demonstrated not only performing the simultaneous hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) with high efficiency but also sustaining long‐term stability upon HER and OER. The study reveals that the outstanding stability is attributed to the spinel oxide surface layer developed during evolution reactions. The spinel structure preserves the active sites that are inherited from the HEA's intrinsic structure. This work will provide an insightful direction/pathway for the design and manufacturing activities of other metallic electrocatalysts and a benchmark for the assessment of their efficiency–stability relationship. The stability of electrocatalysts is correlated with their surface structures that are self‐reconstructed responding to the hydrogen evolution reaction (HER) or oxygen evolution reaction (OER). The spinel oxide, inherited from the original high‐entropy alloy catalyst and developed over the surface of FeCoNiRu when subject to the polarization, provides the hollow active sites enabling the long‐term stabilities of HER and OER.

The surface of the human cerebral cortex is a highly folded sheet with the majority of its surface area buried within folds. As such, it is a difficult domain for computational as well as visualization purposes. We have therefore designed a set of procedures for modifying the representation of the cortical surface to (i) inflate it so that activity buried inside sulci may be visualized, (ii) cut and flatten an entire hemisphere, and (iii) transform a hemisphere into a simple parameterizable surface such as a sphere for the purpose of establishing a surface-based coordinate system.

Clearly understanding elementary growth processes that depend on surface reconstruction is essential to controlling vapor-phase epitaxy more precisely. In this study, ammonia chemical adsorption on GaN(0001) reconstructed surfaces under metalorganic vapor phase epitaxy (MOVPE) conditions (3Ga-H and Nad-H + Ga-H on a 2 × 2 unit cell) is investigated using steepest-entropy-ascent quantum thermodynamics (SEAQT). SEAQT is a thermodynamic-ensemble based, first-principles framework that can predict the behavior of non-equilibrium processes, even those far from equilibrium where the state evolution is a combination of reversible and irreversible dynamics. SEAQT is an ideal choice to handle this problem on a first-principles basis since the chemical adsorption process starts from a highly non-equilibrium state. A result of the analysis shows that the probability of adsorption on 3Ga-H is significantly higher than that on Nad-H + Ga-H. Additionally, the growth temperature dependence of these adsorption probabilities and the temperature increase due to the heat of reaction is determined. The non-equilibrium thermodynamic modeling applied can lead to better control of the MOVPE process through the selection of preferable reconstructed surfaces. The modeling also demonstrates the efficacy of DFT-SEAQT coupling for determining detailed non-equilibrium process characteristics with a much smaller computational burden than would be entailed with mechanics-based, microscopic-mesoscopic approaches.

The arc-based additive manufacturing process offers high deposition rates with a drawback of creating irregular surfaces necessitating post-machining efforts. These post-processing activities use up vital raw materials and considerable energy resources. Developing predictive capabilities for surface topography becomes essential to counter these challenges. Such predictions rely on key process parameters, including wire feed and travel speeds. In this study, we develop a surface topography model that reconstructs surfaces using wire feed speed, travel speed, and interpass temperature as input parameters. The initial phase is to identify the relevant surface features that together describe the surface. From the several features scrutinized, eight representative attributes have been discerned and used in developing the surface reconstruction model, including spatial average roughness, spatial peak height, spatial maximum valley depth, spatial skewness, spatial kurtosis, maximum flatness, and waviness. The surface reconstruction model leverages the discrete cosine transform (DCT) and requires at least 30 DCT parameters for accurate surface reconstruction. Moreover, an ANN model is introduced to predict the DCT parameters based on the wire feed speed, travel speed, and interpass temperature inputs. Validation with the 309L stainless steel test material highlights the model’s commendable accuracy in predicting the DCT parameters, paving the way for precise projections of overall surface topography and machining allowances. This model sets the stage for simulation-based additive-subtractive process design, pinpointing optimal deposition conditions and matching machining parameters. Additionally, it facilitates the seamless integration of realistic surfaces into computational models for additive process simulations, holding immense potential to refine additive manufacturing processes.

The ability to develop highly active and low-cost electrocatalysts represents an important endeavor toward accelerating sluggish water-oxidation kinetics. Herein, we report the implementation and unraveling of the photothermal effect of spinel nanoparticles (NPs) on promoting dynamic active-sites generation to markedly enhance their oxygen evolution reaction (OER) activity via an integrated operando Raman and density functional theory (DFT) study. Specifically, NiFe₂O₄ (NFO) NPs are first synthesized by capitalizing on amphiphilic star-like diblock copolymers as nanoreactors. Upon the near-infrared light irradiation, the photothermal heating of the NFO-based electrode progressively raises the temperature, accompanied by a marked decrease of overpotential. Accordingly, only an overpotential of 309 mV is required to yield a high current density of 100 mA cm−2, greatly lower than recently reported earth-abundant electrocatalysts. More importantly, the photothermal effect of NFO NPs facilitates surface reconstruction into high-active oxyhydroxides at lower potential (1.36 V) under OER conditions, as revealed by operando Raman spectroelectrochemistry. The DFT calculation corroborates that these reconstructed (Ni,Fe)oxyhydroxides are electrocatalytically active sites as the kinetics barrier is largely reduced over pure NFO without surface reconstruction. Given the diversity of materials (metal oxides, sulfides, phosphides, etc.) possessing the photo-to-thermal conversion, this effect may thus provide a unique and robust platform to boost highly active surface species in nanomaterials for a fundamental understanding of enhanced performance that may underpin future advances in electrocatalysis, photocatalysis, solar-energy conversion, and renewable-energy production.

Mapping is a crucial task in robotics and a fundamental building block of most mobile systems deployed in the real world. Robots use different environment representations depending on their task and sensor setup. This paper showcases a practical approach to volumetric surface reconstruction based on truncated signed distance functions, also called TSDFs. We revisit the basics of this mapping technique and offer an approach for building effective and efficient real-world mapping systems. In contrast to most state-of-the-art SLAM and mapping approaches, we are making no assumptions on the size of the environment nor the employed range sensor. Unlike most other approaches, we introduce an effective system that works in multiple domains using different sensors. To achieve this, we build upon the Academy-Award-winning OpenVDB library used in filmmaking to realize an effective 3D map representation. Based on this, our proposed system is flexible and highly effective and, in the end, capable of integrating point clouds from a 64-beam LiDAR sensor at 20 frames per second using a single-core CPU. Along with this publication comes an easy-to-use C++ and Python library to quickly and efficiently solve volumetric mapping problems with TSDFs.

The Developing Human Connectome Project (dHCP) seeks to create the first 4-dimensional connectome of early life. Understanding this connectome in detail may provide insights into normal as well as abnormal patterns of brain development. Following established best practices adopted by the WU-MINN Human Connectome Project (HCP), and pioneered by FreeSurfer, the project utilises cortical surface-based processing pipelines. In this paper, we propose a fully automated processing pipeline for the structural Magnetic Resonance Imaging (MRI) of the developing neonatal brain. This proposed pipeline consists of a refined framework for cortical and sub-cortical volume segmentation, cortical surface extraction, and cortical surface inflation, which has been specifically designed to address considerable differences between adult and neonatal brains, as imaged using MRI. Using the proposed pipeline our results demonstrate that images collected from 465 subjects ranging from 28 to 45 weeks post-menstrual age (PMA) can be processed fully automatically; generating cortical surface models that are topologically correct, and correspond well with manual evaluations of tissue boundaries in 85% of cases. Results improve on state-of-the-art neonatal tissue segmentation models and significant errors were found in only 2% of cases, where these corresponded to subjects with high motion. Downstream, these surfaces will enhance comparisons of functional and diffusion MRI datasets, supporting the modelling of emerging patterns of brain connectivity.

HighlightsDouble-cation etching induces abundant vacancies serving as active sites and accelerates the surface reconstruction.NMO-30M with cation deficiencies and oxygen vacancies exhibits outstanding OER performance and remarkable stability.In situ Raman spectroscopy directly captures the surface reconstruction process.Rational reconstruction of oxygen evolution reaction (OER) pre-catalysts and performance index of OER catalysts are crucial but still challenging for universal water electrolysis. Herein, we develop a double-cation etching strategy to tailor the electronic structure of NiMoO4, where the prepared NiMoO4 nanorods etched by H2O2 reconstruct their surface with abundant cation deficiencies and lattice distortion. Calculation results reveal that the double cation deficiencies can make the upshift of d-band center for Ni atoms and the active sites with better oxygen adsorption capacity. As a result, the optimized sample (NMO-30M) possesses an overpotential of 260 mV at 10 mA cm−2 and excellent long-term durability of 162 h. Importantly, in situ Raman test reveals the rapid formation of high-oxidation-state transition metal hydroxide species, which can further help to improve the catalytic activity of NiMoO4 in OER. This work highlights the influence of surface remodification and shed some light on activating catalysts.

360 degree cameras capture the whole scene around a photographer in a single shot. Cheap 360 cameras are a new paradigm in photogrammetry. The camera can be pointed to any direction, and the large field of view reduces the number of photographs. This paper aims to show that accurate metric reconstructions can be achieved with affordable sensors (less than 300 euro). The camera used in this work is the Xiaomi Mijia Mi Sphere 360, which has a cost of about 300 USD (January 2018). Experiments demonstrate that millimeter-level accuracy can be obtained during the image orientation and surface reconstruction steps, in which the solution from 360° images was compared to check points measured with a total station and laser scanning point clouds. The paper will summarize some practical rules for image acquisition as well as the importance of ground control points to remove possible deformations of the network during bundle adjustment, especially for long sequences with unfavorable geometry. The generation of orthophotos from images having a 360° field of view (that captures the entire scene around the camera) is discussed. Finally, the paper illustrates some case studies where the use of a 360° camera could be a better choice than a project based on central perspective cameras. Basically, 360° cameras become very useful in the survey of long and narrow spaces, as well as interior areas like small rooms.

Surface reconstruction engineering is an effective strategy to promote the catalytic activities of electrocatalysts, especially for water oxidation. Taking advantage of the physicochemical properties of precatalysts by manipulating their structural self-reconstruction levels provide a promising methodology for achieving suitable catalysts. In this review, we focus on recent advances in research related to the rational control of the process and level of surface transformation ultimately to design advanced oxygen evolution electrocatalysts. We start by discussing the original contributions to surface changes during electrochemical reactions and related factors that can influence the electrocatalytic properties of materials. We then present an overview of current developments and a summary of recently proposed strategies to boost electrochemical performance outcomes by the controlling structural self-reconstruction process. By conveying these insights, processes, general trends, and challenges, this review will further our understanding of surface reconstruction processes and facilitate the development of high-performance electrocatalysts beyond water oxidation.