Strategies to Modulate the Copper Oxidation State Toward Selective C2+ Production in the Electrochemical CO2 Reduction Reaction.

The electrochemical reduction of CO2 to form value-added chemicals receives considerable attention in recent years. Copper (Cu) is recognized as the only element capable of electro-reducing CO2 into hydrocarbons with two or more carbon atoms (C2+), but the low product selectivity of the Cu-based catalyst remains a major technological challenge to overcome. Therefore, identification of the structural features of Cu-based catalysts is of great importance for the highly selective production of C2+ products (ethylene, ethanol, n-propanol, etc.), and the oxidation state of Cu species in the catalysts is found critical to the catalyst performance. This review introduces recent efforts to fine-tune the oxidation state of Cu to increase carbon capture and produce specific C2+ compounds, with the intention of greatly expediting the advance in the catalyst designs. It also points to the remaining challenges and fruitful research directions for the development of Cu-based catalysts that can shape the practical CO2 reduction technology.

Stable 1T-MoS2 by Facile Phase Transition Synthesis for Efficient Electrocatalytic Oxygen Evolution Reaction.

The 1T phase of MoS2 exhibits much higher electrocatalytic activity and better stability than the 2H phase. However, the harsh conditions of 1T phase synthesis remain a significant challenge for various extensions and applications of MoS2. In this work, a simple hydrothermal-based synthesis method for the phase transition of MoS2 is being developed. For this, the NH2-MIL-125(Ti) (Ti MOF) is successfully utilized to induce the phase transition of MoS2 from 2H to 1T, achieving a high conversion ratio of ≈78.3%. The optimum phase-induced MoS2/Ti MOF heterostructure demonstrates enhanced oxygen evolution reaction (OER) performance, showing an overpotential of 290 mV at a current density of 10 mA cm-2. The density functional theory (DFT) calculations are demonstrating the benefits of this phase transition, determining the electronic properties and OER performance of MoS2.

An Amiable Design of Cobalt Single Atoms as the Active Sites for Oxygen Evolution Reaction in Desalinated Seawater.

Green fuel from water splitting is hardcore for future generations, and the limited source of fresh water (<1%) is a bottleneck. Seawater cannot be used directly as a feedstock in current electrolyzer techniques. Until now single atom catalysts were reported by many synthetic strategies using notorious chemicals and harsh conditions. A cobalt single-atom (CoSA) intruding cobalt oxide ultrasmall nanoparticle (Co3 O4 USNP)-intercalated porous carbon (PC) (CoSA-Co3 O4 @PC) electrocatalyst was synthesized from the waste orange peel as a single feedstock (solvent/template). The extended X-ray absorption fine structure spectroscopy (EXAFS) and theoretical fitting reveal a clear picture of the coordination environment of the CoSA sites (CoSA-Co3 O4 and CoSA-N4 in PC). To impede the direct seawater corrosion and chlorine evolution the seawater has been desalinated (Dseawater) with minimal cost and the obtained PC is used as an adsorbent in this process. CoSA-Co3 O4 @PC shows high oxygen evolution reaction (OER) activity in transitional metal impurity-free (TMIF) 1 M KOH and alkaline Dseawater. CoSA-Co3 O4 @PC exhibits mass activity that is 15 times higher than the commercial RuO2 . Theoretical interpretations suggest that the optimized CoSA sites in Co3 O4 USNPs reduce the energy barrier for alkaline water dissociation and simultaneously trigger an excellent OER followed by an adsorbate evolution mechanism (AEM).

Deterministic Synthesis of Pd Nanocrystals Enclosed by High-Index Facets and Their Enhanced Activity toward Formic Acid Oxidation.

Noble-metal nanocrystals enclosed by high-index facets are of growing interest due to their enhanced catalytic performance in a variety of reactions. Herein, we report the deterministic synthesis of Pd nanocrystals encased by high-index facets by controlling the rate of deposition (Vdeposition) relative to that of surface diffusion (Vdiffusion). For octahedral seeds with truncated corners, a reduction rate (and thus deposition rate) faster than that of surface diffusion (i.e., Vdeposition/Vdiffusion > 1) led to the formation of concave trisoctahedra (TOH) with high-index facets. When the reduction was slowed down, in contrast, surface diffusion dominated the growth pathway. In the case of Vdeposition/Vdiffusion ≈ 1, truncated octahedra with enlarged sizes were produced. When the reduction rate was between these two extremes, we obtained concave tetrahexahedra (THH) without or with truncation. Similar growth patterns were also observed for the cuboctahedral seeds. When the Pd octahedra, concave TOH, and concave THH were tested for electrocatalyzing the formic acid oxidation (FAO) reaction, those with high-index facets were advantageous over the conventional Pd octahedra enclosed by {111} facets. This work not only contributes to the understanding of surface diffusion and its role in nanocrystal growth but also offers a general protocol for the synthesis of nanocrystals enclosed by high-index facets.

Smartphone Authentication System Using Personal Gaits and a Deep Learning Model.

In a society centered on hyper-connectivity, information sharing is crucial, but it must be ensured that each piece of information is viewed only by legitimate users; for this purpose, the medium that connects information and users must be able to identify illegal users. In this paper, we propose a smartphone authentication system based on human gait, breaking away from the traditional authentication method of using the smartphone as the medium. After learning human gait features with a convolutional neural network deep learning model, it is mounted on a smartphone to determine whether the user is a legitimate user by walking for 1.8 s while carrying the smartphone. The accuracy, precision, recall, and F1-score were measured as evaluation indicators of the proposed model. These measures all achieved an average of at least 90%. The analysis results show that the proposed system has high reliability. Therefore, this study demonstrates the possibility of using human gait as a new user authentication method. In addition, compared to our previous studies, the gait data collection time for user authentication of the proposed model was reduced from 7 to 1.8 s. This reduction signifies an approximately four-fold performance enhancement through the implementation of filtering techniques and confirms that gait data collected over a short period of time can be used for user authentication.

Electrochemically Activatable Liquid Organic Hydrogen Carriers and Their Applications.

Hydrogen has been chosen as an environmentally benign energy source to replace fossil-fuel-based energy systems. Since hydrogen is difficult to store and transport in its gaseous phase, thermochemical liquid organic hydrogen carriers (LOHCs) have been developed as one of the alternative technologies. However, the high temperature and pressure requirements of thermochemical LOHC systems result in huge energy waste and impracticality. This Perspective proposes electrochemical (EC)-LOHCs capable of more efficient, safer, and lower temperature and pressure hydrogen storage/utilization. To enable this technology, several EC-LOHC candidates such as isopropanol, phenolic compounds, and organic acids are described, and the latest research trends and design concepts of related homo/hetero-based electrocatalysts are discussed. In addition, we propose efficient fuel-cell-based systems that implement electrochemical (de)hydrogenation of EC-LOHCs and present prospects for relevant technologies.

Surface distortion of FeRu nanoparticles improves the hydrogen evolution reaction performance in alkaline media.

Rational design of electrocatalysts, including an increased catalytic surface area, a unique surface structure, and improved conductivity, for facilitating the hydrogen evolution reaction (HER) is emerging as an important issue. In this work, we consider the engineering of catalyst surfaces as an effective and feasible way to accelerate the HER kinetics. By etching the surface Fe of FeRu alloy nanoparticles (NPs) using hydrofluoric acid (HF), a distorted catalytic surface of FeRu NPs was formed. The distorted surface of the HF-treated FeRu NPs was successfully analyzed by X-ray absorption spectroscopy, high-resolution photoemission spectroscopy, and electrochemical absorption/desorption experiments. The electrocatalytic HER activity of the HF-treated FeRu NPs demonstrated that surface distortion enhances the water dissociation reaction and the electron transfer rate. As a result, the surface-distorted FeRu NPs improved HER performances in alkaline media compared to the pristine FeRu alloy NP/C, commercial Ru/C, and the state-of-the-art Pt/C catalysts.

Explainable gait recognition with prototyping encoder-decoder.

Human gait is a unique behavioral characteristic that can be used to recognize individuals. Collecting gait information widely by the means of wearable devices and recognizing people by the data has become a topic of research. While most prior studies collected gait information using inertial measurement units, we gather the data from 40 people using insoles, including pressure sensors, and precisely identify the gait phases from the long time series using the pressure data. In terms of recognizing people, there have been a few recent studies on neural network-based approaches for solving the open set gait recognition problem using wearable devices. Typically, these approaches determine decision boundaries in the latent space with a limited number of samples. Motivated by the fact that such methods are sensitive to the values of hyper-parameters, as our first contribution, we propose a new network model that is less sensitive to changes in the values using a new prototyping encoder-decoder network architecture. As our second contribution, to overcome the inherent limitations due to the lack of transparency and interpretability of neural networks, we propose a new module that enables us to analyze which part of the input is relevant to the overall recognition performance using explainable tools such as sensitivity analysis (SA) and layer-wise relevance propagation (LRP).

Achieving complete electrooxidation of ethanol by single atomic Rh decoration of Pt nanocubes.

SignificanceDirect ethanol fuel cells are attracting growing attention as portable power sources due to their advantages such as higher mass-energy density than hydrogen and less toxicity than methanol. However, it is challenging to achieve the complete electrooxidation to generate 12 electrons per ethanol, resulting in a low fuel utilization efficiency. This manuscript reports the complete ethanol electrooxidation by engineering efficient catalysts via single-atom modification. The combined electrochemical measurements, in situ characterization, and density functional theory calculations unravel synergistic effects of single Rh atoms and Pt nanocubes and identify reaction pathways leading to the selective C-C bond cleavage to oxidize ethanol to CO2. This study provides a unique single-atom approach to tune the activity and selectivity toward complicated electrocatalytic reactions.

Crystal Phase Transition Creates a Highly Active and Stable RuCX Nanosurface for Hydrogen Evolution Reaction in Alkaline Media.

Although metastable crystal structures have received much attention owing to their utilization in various fields, their phase-transition to a thermodynamic structure has attracted comparably little interest. In the case of nanoscale crystals, such an exothermic phase-transition releases high energy within a confined surface area and reconstructs surface atomic arrangement in a short time. Thus, this high-energy nanosurface may create novel crystal structures when some elements are supplied. In this work, the creation of a ruthenium carbide (RuCX , X < 1) phase on the surface of the Ru nanocrystal is discovered during phase-transition from cubic-close-packed to hexagonal-close-packed structure. When the electrocatalytic hydrogen evolution reaction (HER) is tested in alkaline media, the RuCX exhibits a much lower overpotential and good stability relative to the counterpart Ru-based catalysts and the state-of-the-art Pt/C catalyst. Density functional theory calculations predict that the local heterogeneity of the outermost RuCX surface promotes the bifunctional HER mechanism by providing catalytic sites for both H adsorption and facile water dissociation.

Reliability of Machine and Human Examiners for Detection of Laryngeal Penetration or Aspiration in Videofluoroscopic Swallowing Studies.

Computer-assisted analysis is expected to improve the reliability of videofluoroscopic swallowing studies (VFSSs), but its usefulness is limited. Previously, we proposed a deep learning model that can detect laryngeal penetration or aspiration fully automatically in VFSS video images, but the evidence for its reliability was insufficient. This study aims to compare the intra- and inter-rater reliability of the computer model and human raters. The test dataset consisted of 173 video files from which the existence of laryngeal penetration or aspiration was judged by the computer and three physicians in two sessions separated by a one-month interval. Intra- and inter-rater reliability were calculated using Cohen's kappa coefficient, the positive reliability ratio (PRR) and the negative reliability ratio (NRR). Intrarater reliability was almost perfect for the computer and two experienced physicians. Interrater reliability was moderate to substantial between the model and each human rater and between the human raters. The average PRR and NRR between the model and the human raters were similar to those between the human raters. The results demonstrate that the deep learning model can detect laryngeal penetration or aspiration from VFSS video as reliably as human examiners.

Hyoid Bone Tracking in a Videofluoroscopic Swallowing Study Using a Deep-Learning-Based Segmentation Network.

Kinematic analysis of the hyoid bone in a videofluorosopic swallowing study (VFSS) is important for assessing dysphagia. However, calibrating the hyoid bone movement is time-consuming, and its reliability shows wide variation. Computer-assisted analysis has been studied to improve the efficiency and accuracy of hyoid bone identification and tracking, but its performance is limited. In this study, we aimed to design a robust network that can track hyoid bone movement automatically without human intervention. Using 69,389 frames from 197 VFSS files as the data set, a deep learning model for detection and trajectory prediction was constructed and trained by the BiFPN-U-Net(T) network. The present model showed improved performance when compared with the previous models: an area under the curve (AUC) of 0.998 for pixelwise accuracy, an accuracy of object detection of 99.5%, and a Dice similarity of 90.9%. The bounding box detection performance for the hyoid bone and reference objects was superior to that of other models, with a mean average precision of 95.9%. The estimation of the distance of hyoid bone movement also showed higher accuracy. The deep learning model proposed in this study could be used to detect and track the hyoid bone more efficiently and accurately in VFSS analysis.

Method for Diagnosing the Bone Marrow Edema of Sacroiliac Joint in Patients with Axial Spondyloarthritis Using Magnetic Resonance Image Analysis Based on Deep Learning.

Axial spondyloarthritis (axSpA) is a chronic inflammatory disease of the sacroiliac joints. In this study, we develop a method for detecting bone marrow edema by magnetic resonance (MR) imaging of the sacroiliac joints and a deep-learning network. A total of 815 MR images of the sacroiliac joints were obtained from 60 patients diagnosed with axSpA and 19 healthy subjects. Gadolinium-enhanced fat-suppressed T1-weighted oblique coronal images were used for deep learning. Active sacroiliitis was defined as bone marrow edema, and the following processes were performed: setting the region of interest (ROI) and normalizing it to a size suitable for input to a deep-learning network, determining bone marrow edema using a convolutional-neural-network-based deep-learning network for individual MR images, and determining sacroiliac arthritis in subject examinations based on the classification results of individual MR images. About 70% of the patients and normal subjects were randomly selected for the training dataset, and the remaining 30% formed the test dataset. This process was repeated five times to calculate the average classification rate of the five-fold sets. The gradient-weighted class activation mapping method was used to validate the classification results. In the performance analysis of the ResNet18-based classification network for individual MR images, use of the ROI showed excellent detection performance of bone marrow edema with 93.55 ± 2.19% accuracy, 92.87 ± 1.27% recall, and 94.69 ± 3.03% precision. The overall performance was additionally improved using a median filter to reflect the context information. Finally, active sacroiliitis was diagnosed in individual subjects with 96.06 ± 2.83% accuracy, 100% recall, and 94.84 ± 3.73% precision. This is a pilot study to diagnose bone marrow edema by deep learning based on MR images, and the results suggest that MR analysis using deep learning can be a useful complementary means for clinicians to diagnose bone marrow edema.

Template-Directed Rapid Synthesis of Pd-Based Ultrathin Porous Intermetallic Nanosheets for Efficient Oxygen Reduction.

Atomically ordered intermetallic nanoparticles exhibit improved catalytic activity and durability relative to random alloy counterparts. However, conventional methods with time-consuming and high-temperature syntheses only have rudimentary capability in controlling the structure of intermetallic nanoparticles, hindering advances of intermetallic nanocatalysts. We report a template-directed strategy for rapid synthesis of Pd-based (PdM, M=Pb, Sn and Cd) ultrathin porous intermetallic nanosheets (UPINs) with tunable sizes. This strategy uses preformed seeds, which act as the template to control the deposition of foreign atoms and the subsequent interatomic diffusion. Using the oxygen reduction reaction (ORR) as a model reaction, the as-synthesized Pd3 Pb UPINs exhibit superior activity, durability, and methanol tolerance. The favored geometrical structure and interatomic interaction between Pd and Pb in Pd3 Pb UPINs are concluded to account for the enhanced ORR performance.

Boosting Activity and Selectivity of CO2 Electroreduction by Pre-Hydridizing Pd Nanocubes.

The electrochemical CO2 reduction reaction (CO2 RR) to syngas represents a promising solution to mitigate CO2 emissions and manufacture value-added chemicals. Palladium (Pd) has been identified as a potential candidate for syngas production via CO2 RR due to its transformation to Pd hydride under CO2 RR conditions, however, the pre-hydridized effect on the catalytic properties of Pd-based electrocatalysts has not been investigated. Herein, pre-hydridized Pd nanocubes (PdH0.40 ) supported on carbon black (PdH0.40 NCs/C) are directly prepared from a chemical reduction method. Compared with Pd nanocubes (Pd NCs/C), PdH0.40 NCs/C presented an enhanced CO2 RR performance due to its less cathodic phase transformation revealed by the in situ X-ray absorption spectroscopy. Density functional theory calculations revealed different binding energies of key reaction intermediates on PdH0.40 NCs/C and Pd NCs/C. Study of the size effect further suggests that NCs of smaller sizes show higher activity due to their more abundant active sites (edge and corner sites) for CO2 RR. The pre-hydridization and reduced NC size together lead to significantly improved activity and selectivity of CO2 RR.

Can Ensemble Deep Learning Identify People by Their Gait Using Data Collected from Multi-Modal Sensors in Their Insole?

Gait is a characteristic that has been utilized for identifying individuals. As human gait information is now able to be captured by several types of devices, many studies have proposed biometric identification methods using gait information. As research continues, the performance of this technology in terms of identification accuracy has been improved by gathering information from multi-modal sensors. However, in past studies, gait information was collected using ancillary devices while the identification accuracy was not high enough for biometric identification. In this study, we propose a deep learning-based biometric model to identify people by their gait information collected through a wearable device, namely an insole. The identification accuracy of the proposed model when utilizing multi-modal sensing is over 99%.

Seomae mugwort and jaceosidin attenuate osteoarthritic cartilage damage by blocking IκB degradation in mice.

Seomae mugwort, a Korean native variety of Artemisia argyi, exhibits physiological effects against various diseases. However, its effects on osteoarthritis (OA) are unclear. In this study, a Seomae mugwort extract prevented cartilage destruction in an OA mouse model. In vitro and ex vivo analyses revealed that the extract suppressed MMP3, MMP13, ADAMTS4 and ADAMTS5 expression induced by IL-1ß, IL-6 and TNF-α and inhibited the loss of extracellular sulphated proteoglycans. In vivo analysis revealed that oral administration of the extract suppressed DMM-induced cartilage destruction. We identified jaceosidin in Seomae mugwort and showed that this compound decreased MMP3, MMP13, ADAMTS4 and ADAMTS5 expression levels, similar to the action of the Seomae mugwort extract in cultured chondrocytes. Interestingly, jaceosidin and eupatilin combined had similar effects to Seomae mugwort in the DMM-induced OA model. Induction of IκB degradation by IL-1ß was blocked by the extract and jaceosidin, whereas JNK phosphorylation was only suppressed by the extract. These results suggest that the Seomae mugwort extract and jaceosidin can attenuate cartilage destruction by suppressing MMPs, ADAMTS4/5 and the nuclear factor-κB signalling pathway by blocking IκB degradation. Thus, the findings support the potential application of Seomae mugwort, and particularly jaceosidin, as natural therapeutics for OA.

3'-Sialyllactose prebiotics prevents skin inflammation via regulatory T cell differentiation in atopic dermatitis mouse models.

3'-Sialyllactose (3'-SL), a natural prebiotic, maintains immune homeostasis and exerts anti-inflammatory and anti-arthritic effects. Although regulatory T cells (Tregs) prevent excessive inflammation and maintain immune tolerance, the effect of 3'-SL on Treg regulation is unclear. This study aimed to investigate the effect of 3'-SL on Treg responses in atopic dermatitis (AD) pathogenesis. Oral administration of 3'-SL reduced AD-like symptoms such as ear, epidermal, and dermal thickness in repeated topical application of house dust mites (HDM) and 2,4-dinitrochlorobenzene (DNCB). 3'-SL inhibited IgE, IL-1ß, IL-6, and TNF-α secretion and markedly downregulated AD-related cytokines including IL-4, IL-5, IL-6, IL-13, IL-17, IFN-γ, TNF-α, and Tslp through regulation of NF-κB in ear tissue. Additionally, in vitro assessment of Treg differentiation revealed that 3'-SL directly induced TGF-ß-mediated Treg differentiation. Furthermore, 3'-SL administration also ameliorated sensitization and elicitation of AD pathogenesis by suppressing mast cell infiltration and production of IgE and pro-inflammatory cytokines in mouse serum by mediating the Treg response. Furthermore, Bifidobacterium population was also increased by 3'-SL administration as prebiotics. Our data collectively show that 3'-SL has therapeutic effects against AD progression by inducing Treg differentiation, downregulating AD-related cytokines, and increasing the Bifidobacterium population.

Synthesis of Pd-Pt Ultrathin Assembled Nanosheets as Highly Efficient Electrocatalysts for Ethanol Oxidation.

Control over composition and morphology of nanocrystals (NCs) is significant to develop advanced catalysts applicable to polymer electrolyte membrane fuel cells and further overcome the performance limitations. Here, we present a facile synthesis of Pd-Pt alloy ultrathin assembled nanosheets (UANs) by regulating the growth behavior of Pd-Pt nanostructures. Iodide ions supplied from KI play as capping agents for the {111} plane to promote 2-dimensional (2D) growth of Pd and Pt, and the optimal concentrations of cetyltrimethylammonium chloride and ascorbic acid result in the generation of Pd-Pt alloy UANs in high yield. The prepared Pd-Pt alloy UANs exhibited the remarkable enhancement of the catalytic activity and stability toward ethanol oxidation reaction compared to irregular-shaped Pd-Pt alloy NCs, commercial Pd/C, and commercial Pt/C. Our results confirm that the Pd-Pt alloy composition and ultrathin 2D morphology offer high accessible active sites and favorable electronic structure for enhancing electrocatalytic activity.

Theoretical and Experimental Understanding of Hydrogen Evolution Reaction Kinetics in Alkaline Electrolytes with Pt-Based Core-Shell Nanocrystals.

The free energy of H adsorption (ΔGH) on a metallic catalyst has been taken as a descriptor to predict the hydrogen evolution reaction (HER) kinetics but has not been well applied in alkaline media. To assess this, we prepare Pd@Pt and PdH@Pt core-shell octahedra enclosed by Pt(111) facets as model catalysts for controlling the ΔGH affected by the ligand, the strain, and their ensemble effects. The Pt shell thickness is adjusted from 1 to 5 atomic layers by varying the amount of Pt precursor added during synthesis. In an alkaline electrolyte, the HER activity of core-shell models is improved either by the construction of core-shell structures or by the increased number of Pt shells. These experimental results are in good agreement with the ΔGH values calculated by the first-principles density functional theory with a complex surface strained core-shell slab model. However, enhanced HER activities of Pd@Pt and PdH@Pt core-shell nanocrystals over the Pt catalyst are inconsistent with the thermodynamic ΔGH scaling relationship only but can be explained by the work function and apparent ΔGH models that predict the interfacial electric field for the HER.