Importance of Site Diversity and Connectivity in Electrochemical CO Reduction on Cu.

Electrochemical CO2 reduction on Cu is a promising approach to produce value-added chemicals using renewable feedstocks, yet various Cu preparations have led to differences in activity and selectivity toward single and multicarbon products. Here, we find, surprisingly, that the effective catalytic activity toward ethylene improves when there is a larger fraction of less active sites acting as reservoirs of *CO on the surface of Cu nanoparticle electrocatalysts. In an adaptation of chemical transient kinetics to electrocatalysis, we measure the dynamic response of a gas diffusion electrode (GDE) cell when the feed gas is abruptly switched between Ar (inert) and CO. When switching from Ar to CO, CO reduction (COR) begins promptly, but when switching from CO to Ar, COR can be maintained for several seconds (delay time) despite the absence of the CO reactant in the gas phase. A three-site microkinetic model captures the observed dynamic behavior and shows that Cu catalysts exhibiting delay times have a less active *CO reservoir that exhibits fast diffusion to active sites. The observed delay times and the estimated *CO reservoir sizes are affected by catalyst preparation, applied potential, and microenvironment (electrolyte cation identity, electrolyte pH, and CO partial pressure). Notably, we estimate that the *CO reservoir surface coverage can be as high as 88 ± 7% on oxide-derived Cu (OD-Cu) at high overpotentials (-1.52 V vs SHE) and this increases in reservoir coverage coincide with increased turnover frequencies to ethylene. We also estimate that *CO can travel substantial distances (up to 10s of nm) prior to desorption or reaction. It appears that active C-C coupling sites by themselves do not control selectivity to C2+ products in electrochemical COR; the supply of CO to those sites is also a crucial factor. More generally, the overall activity of Cu electrocatalysts cannot be approximated from linear combinations of individual site activities. Future designs must consider the diversity of the catalyst network and account for intersite transportation pathways.

Sunlight-Driven Self-Cleaning Ultrafine Particulate Matter Filter with Antibacterial Activity.

Air pollution by particulate matter (PM) and airborne pathogens causes severe health problems in the human body. Presently, popular disposable air filters yield huge waste and have a fatal impact on the environment. Postuse cleaning of air filters also leads to secondary air and water pollution. Here, we report a sunlight-driven self-cleaning PM filter by coupling a full-solar-spectrum-active photocatalyst comprising up-conversion nanoparticles (UCNPs) decorated with semiconductor iron(III) oxide (UCNP@α-Fe2O3) shells stabilized upon graphene functionalized borosilicate fibrous membrane (rGO-BF). While rGO-BF ensures high PM adsorption, UCNP@α-Fe2O3 (NP) enables self-photodegradation of adsorbed PM under abundant sunlight and subsequent membrane regeneration, while preventing secondary air or water pollution. Rational surface chemistry and optimal microstructure enable our filters to remove >99% of PM2.5 under deplorable air-quality conditions. Moreover, our filter shows excellent antibacterial activity toward E. coli and S. aureus, demonstrating its potential for practical utilization in face masks, air filtering devices, and protective medical wear. This work successfully suggests an intriguing design platform for self-sustainable zero-waste air filter membranes.

Toward Advanced Superomniphobicity: Hierarchical Insights from Serif-T Nanostructures to Microscale Wrinkles.

Developing a superomniphobic surface that exceeds the static and dynamic repellency observed in nature's springtails for various liquids presents a significant challenge in the realm of surface and interface science. However, progress in this field has been particularly limited when dealing with low-surface-tension liquids. This is because dynamic repellency values are typically at least 2 orders of magnitude lower than those observed with water droplets. Our study introduces an innovative hierarchical topography demonstrating exceptional dynamic repellency to low-surface-tension liquids. Inspired by the structural advantages found in springtails, we achieve a static contact angle of >160° and the complete rebound of droplet impact with a Weber number (We) of ∼104 using ethanol. These results surpass all existing benchmarks that have been reported thus far, including those of natural surfaces. The key insight from our research is the vital role of the microscale air pocket size, governed by wrinkle wavelength, in both static and dynamic repellency. Additionally, nanoscale air pockets within serif-T nanostructures prove to be essential for achieving omniphobicity. Our investigations into the wetting dynamics of ethanol droplets further reveal aspects such as the reduction in contact time and the occurrence of a fragmentation phenomenon beyond We ∼ 350, which has not been previously observed.

Long-Range Uniform Deposition of Ag Nanoseed on Cu Current Collector for High-Performance Lithium Metal Batteries.

Uniform lithium deposition is essential to hinder dendritic growth. Achieving this demands even seed material distribution across the electrode, posing challenges in correlating the electrode's surface structure with the uniformity of seed material distribution. In this study, the effect of periodic surface and facet orientation on seed distribution is investigated using a model system consisting of a wrinkled copper (Cu)/graphene structure with a [100] facet orientation. A new methodology is developed for uniformly distributed silver (Ag) nanoparticles over a large area by controlling the surface features of Cu substrates. The regularly arranged Ag nanoparticles, with a diameter of 26.4 nm, are fabricated by controlling the Cu surface condition as [100]-oriented wrinkled Cu. The wrinkled Cu guides a deposition site for spherical Ag nanoparticles, the [100] facet determines the Ag morphology, and the presence of graphene leads to spacings of Ag seeds. This patterned surface and high lithiophilicity, with homogeneously distributed Ag nanoparticles, lead to uniform Li+ flux and reduced nucleation energy barrier, resulting in excellent battery performance. The electrochemical measurements exhibit improved cyclic stability over 260 cycles at 0.5 mA cm-2 and 100 cycles at 1.0 mA cm-2 and enhanced kinetics even under a high current density of 5.0 mA cm-2 .

Highly Sensitive and Selective Acetylene CuO/ZnO Heterostructure Sensors through Electrospinning at Lean O2 Concentration for Transformer Diagnosis.

Acetylene (C2H2) is a gas that can cause explosions in transformers even at low concentrations. Gas chromatography (GC) or photoacoustic spectroscopy (PAS) have been used to detect C2H2 during dissolved gas analysis (DGA), but they are not suitable for monitoring numerous transformers at substations. Even though metal oxide semiconductor (MOS) based C2H2 sensors have drawn much attention as a potential solution, existing MOS-based C2H2 sensors have low sensitivity toward C2H2 in the transformer environment (<2% O2 concentrations). This study develops high-performance C2H2 gas sensors for DGA using a heterostructure of CuO/ZnO (CZ) via the electrospinning process. Performance of various ratios of CZ composite nanofibers are compared in a transformer-like environment, and the optimal composition of CZ nanofibers for detection of C2H2 at 2% O2 concentration is proposed. The CuO:ZnO = 8:2 (CZ2) sensor achieves the highest response (Rg/Ra = 7.6 against 10 ppm of C2H2) toward low concentration of C2H2 at 200 °C with good stability (>10 h). In addition, the CZ2 sensor also shows a high selectivity (>5 times) to coexisting transformer oil gases which are H2, CH4, C2H4, C2H6, CO, and CO2. Overall, this study is the first to demonstrate a high performing DGA sensor under 2% O2 concentration that can provide a practical solution to monitoring the low concentration of C2H2 in transformers effectively.

Effect of Feature Shape and Dimension of a Confinement Geometry on Selectivity of Electrocatalytic CO2 Reduction.

The local confinement effect, which can generate a high concentration of hydroxide ions and reaction intermediates near the catalyst surface, is an important strategy for converting CO2 into multi-carbon products in electrocatalytic CO2 reduction. Therefore, understanding how the shape and dimension of the confinement geometry affect the product selectivity is crucial. In this study, we report for the first time the effect of the shape (degree of confinement) and dimension of the confined space on the product selectivity without changing the intrinsic property of Cu. We demonstrate that geometry influences the outcomes of products, such as CH4 , C2 H4 , and EtOH, in different ways: the selectivity of CH4 and EtOH is affected by shape, while the selectivity of C2 H4 is influenced by dimension of geometry predominantly. These phenomena are demonstrated, both experimentally and through simulation, to be induced by the local confinement effect within the confined structure. Our geometry model could serve as basis for designing the confined structures tailored for the production of specific products.

Remote Assessment of ADHD Symptoms Based on Mobile Game Performance in Children with ADHD: A Proof of Concept.

The use of game-based digital medicine is gaining increasing interest in helping children with ADHD to improve their attention outside the clinical setting. In this process, it is important to continue monitoring children's responses to the use of digital medicine. In this work, we introduce novel digital markers and an analytic pipeline to estimate ADHD-related symptomatic levels during the self-administration of attention games. The digital markers, capturing the children's characteristics of attention and inattention spans, were extracted and translated into clinically-accepted measures of ADHD symptoms, specifically the ADHD-Rating Scale (ADHD-RS) and Child Behavior Checklist (CBCL). To validate the feasibility of our approach, we collected game-specific performance data from 15 children with ADHD, which was used to train machine learning-based regression models to estimate their corresponding ADHD-RS and CBCL scores. Our experiment results showed mean absolute errors of 5.14 and 4.05 points between the actual and estimated ADHD-RS and CBCL scores respectively. This study enables new clinical and research opportunities for accurate longitudinal assessment of symptomatic levels of ADHD via an interactive means of playing mobile games.

Investigating the Impact of Control Modes to Competency and Engagement in Serious Game Play for Children with ADHD.

Serious games are designed to reduce symptoms of attention deficit among children with Attention Deficit/Hyperactivity Disorder (ADHD). These games predominantly use touchscreen interfaces that children with ADHD play in a sitting position, which contributes to already-problematic sedentary behaviors. In an effort to mitigate negative impacts of sedentary behavior, we set out to investigate the use of Dance Dance Revolution (DDR) mats with serious games to induce gross movements. To that end, we designed and implemented Breathtaking Glass Bridge to investigate the impact of interface (DDR vs. touchscreen) on competency and engagement in children with ADHD. Our investigation with 30 children with ADHD reveal that the DDR mat was as engaging as the conventional touchscreen interface despite relatively lower competence shown by our participants. Our study opens new research opportunities for developing and strategically using movement-based control interfaces to encourage gross movements while reducing attention deficit symptoms among children with ADHD.

Density Multiplication of Highly Periodic Sub-5 nm Supramolecular Dendrimer Cylinders on Block Copolymer Lamellar-Assisted High-Resolution Patterns.

Density multiplication in nanopatterning is one of the most efficient techniques for increasing the resolution of the inherent patterns. Thus far, most of the density multiplication techniques integrate bottom-up (or top-down) patterning onto guide patterns prepared by the top-down approach. Although the bottom-up approach exhibits several advantages of cost-effectiveness and high resolution, very few studies have reported bottom-up patterning within a bottom-up template. In this study, the density multiplication of supramolecular cylinders into a block copolymer (BCP)-based guide lamellar pattern is demonstrated by the directed self-assembly (DSA) of a dendrimer and BCPs for the first time. Supramolecular cylinders of sub-5 nm scale are confined into trenches based on 50 and 100 nm scales of a lamellar polystyrene (PS)-poly(methyl methacrylate) (PMMA) BCP, which led to 10×-level to 20×-level density multiplication. Moreover, the orientation of the dendrimer is dependent on the dendrimer film thickness, and the corresponding mechanism is revealed. Notably, the strong guiding effect from the high-resolution guide patterns improved the ordering behavior in the highly curved pattern. Graphoepitaxy via the confinement of an ultrahigh-resolution dendrimer into the guide pattern based on BCP demonstrates promise as a density multiplication method for generating highly ordered nanostructures and complex structures.

Gas Sensors with Two-Dimensional rGO@COF Composite Materials for Fast NO2 Detection under Room Temperature.

Covalent organic frameworks (COFs) are attracting increasing interest in various applications due to their ability to capture molecules originating from their highly crystallized porous structures. However, most types of COFs are non-conductive and cannot be directly applied to electronic devices. Herein, we utilize non-conductive COFs in chemiresistor sensors by forming composite structures with conductive reduced graphene oxide (rGO). The composites rGO@COF exhibit low-enough resistance to be measured as chemiresistors, demonstrating enhanced gas sensing performance than pristine rGO. In particular, rGO@COF sensors achieve 2.7 times higher sensitivity toward NO2 and a dramatically reduced response time from 234 to 32 s compared to rGO, which can be attributed to increased surface area and NO2 adsorption energy. Our strategy provides new perspectives for utilizing non-conductive COFs in various electronic applications.

Simple Approach to Enhance Long-Term Environmental Stability of MXene Using Initiated Chemical Vapor Deposition Surface Coating.

Developing a methodology to enhance long-term stability is one of the most important issues in MXene research, since they are prone to oxidation in the ambient environment. Although various approaches have been suggested to improve the stability of MXene, they have suffered from complicated processes and limited applicability to various types of MXene nanostructures. Herein, we report a simple and versatile technique to enhance the environmental stability of MXenes. Ti3C2Tx MXene films were decorated with a highly hydrophobic polymer, 1H,1H,2H,2H-perfluorodecyl methacrylate (PFDMA), using initiated chemical vapor deposition (iCVD) where iCVD allows the facile postdeposition of polymer films of desired thickness on MXene films. The oxidation resistance was evaluated by fabricating MXene gas sensors and measuring the change in signal-to-noise ratio (SNR) of volatile organic compound (VOC) gases under harsh conditions (RH 100% at 50 °C) for several weeks where the performance in the absence and presence of PFDMA was compared. The results show that while the SNR of PFDMA-Ti3C2Tx sensors was retained, a dramatic increase of the noise level and a decrease in the SNR were observed in pristine Ti3C2Tx. We believe that this simple and nondestructive method will offer great potential to enhance the stability of a wide range of MXenes.

Exploring Optimal Water Splitting Bifunctional Alloy Catalyst by Pareto Active Learning.

Design of bifunctional multimetallic alloy catalysts, which are one of the most promising candidates for water splitting, is a significant issue for the efficient production of renewable energy. Owing to large dimensions of the components and composition of multimetallic alloys, as well as the trade-off behavior in terms of the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) overpotentials for bifunctional catalysts, it is difficult to search for high-performance bifunctional catalysts with multimetallic alloys using conventional trial-and-error experiments. Here, an optimal bifunctional catalyst for water splitting is obtained by combining Pareto active learning and experiments, where 110 experimental data points out of 77946 possible points lead to effective model development. The as-obtained bifunctional catalysts for HER and OER exhibit high performance, which is revealed by model development using Pareto active learning; among the catalysts, an optimal catalyst (Pt0.15 Pd0.30 Ru0.30 Cu0.25 ) exhibits a water splitting behavior of 1.56 V at a current density of 10 mA cm-2 . This study opens avenues for the efficient exploration of multimetallic alloys, which can be applied in multifunctional catalysts as well as in other applications.

Rapid and Reversible Sensing Performance of Hydrogen-Substituted Graphdiyne.

The design of new nanomaterials for rapid and reversible detection of molecules in existence is critical for real-world sensing applications. Current nanomaterial libraries such as carbon nanotubes, graphene, MoS2, and MXene are fundamentally limited by their slow detection speed and small signals; thus, the atomic-level material design of molecular transport pathways and active binding sites must be accompanied. Herein, we fully explore the chemical and physical properties of a hydrogen-substituted graphdiyne (HsGDY) for its molecular sensing properties. This new carbon framework comprises reactive sp carbons in acetylenic linkages throughout the 16.3 Å nanopores and allows for detecting target molecules (e.g., H2) with an exceptionally high sensitivity (ΔR/Rb = 542%) and fast response/recovery time (τ90 = 8 s and τ10 = 38 s) even without any postmodification process. It possesses 2 orders of magnitude higher sensing ability than that of existing nanomaterial libraries. We demonstrate that rapid and reversible molecular binding is attributed to the cooperative interaction with adjacent double sp carbon in the layered nanoporous structure of HsGDY. This new class of carbon framework provides fundamental solutions for nanomaterials in reliable sensor applications that accelerate real-world interfacing.

Hierarchical Topography with Tunable Micro- and Nanoarchitectonics for Highly Enhanced Cardiomyocyte Maturation via Multi-Scale Mechanotransduction.

Enhancing cardiomyocyte (CM) maturation by topographical cues is a critical issue in cardiac tissue engineering. Thus far, single-scale topographies with a broad range of feature shapes and dimensions have been utilized including grooves, pillars, and fibers. This study reports for the first time a hierarchical structure composed of nano-pillars (nPs) on micro-wrinkles (µWs) for effective maturation of CMs. Through capillary force lithography followed by a wrinkling process, vast size ranges of topographies are fabricated, and the responses of CMs are systematically investigated. Maturation of CMs on the hierarchical structures is highly enhanced compared to a single-scale topography: cardiac differentiation of H9C2s (rat cardiomyocytes) on the hierarchical topography is ≈ 2.8 and ≈ 1.9 times higher than those consisting of single-scale µWs and nPs. Both nPs and µWs have important roles in cardiac maturation, and the aspect ratio (height/diameter) of the nPs and the wavelength of the µWs are important in CM maturation. This enhancement is caused by strong focal adhesion and nucleus mediated mechanotransduction of CMs from the confinement effects of the different wavelengths of µWs and the cellular membrane protrusion on the nPs. This study demonstrates how a large family of hierarchical structures is used for cardiac maturation.

Green and Facile Synthesis of Hybrid Composites with Ultralow Dielectric Properties from Water-Soluble Polyimide and Dual-Porous Silica Nanoparticles.

Here, we proposed an eco-friendly synthetic method for synthesizing hybrid composites with ultralow dielectric properties at high frequencies up to 28 GHz for true 5G communication from aqueous aromatic polyimide (PI) polymers and dual-porous silica nanoparticles (DPS). The "one-step" water-based emulsion template method was used to synthesize the macroporous silica nanoparticles (MPS). A substantially negative ζ potential was produced along the surface of MPS by the poly(vinylpyrrolidone)-based chemical functionalization, enabling excellent aqueous dispersion stability. The water-soluble poly(amic acid) (PAA), as a precursor to PI, was also "one-step" polymerized in an aqueous solution. The MPS were dispersed in a water-soluble PAA matrix to create the hybrid composite films using an entirely water-based approach. The compatibility between the PAA matrix and MPS was elucidated by investigating relatively diverse end-terminated PAAs (with either amine or carboxyl group). It was also discovered that, during a thermally activated imidization reaction, the MPS are in situ converted into the DPS with macro- and microporous structures (with a surface area of 1522.4 m2/g). The thermal, dielectric, mechanical, and morphological characteristics of each composite film were examined, while the amount of DPS in the PI matrix varied from 1 to 20 wt %. With the addition of 5 wt % DPS as an optimum condition, it showed ultralow dielectric properties, with the Dk and Df being 1.615 and 0.003 at a frequency of 28 GHz, respectively, and compatible mechanical properties, with the tensile strength and elastic modulus being 78.2 MPa and 0.32 GPa, respectively. These results can comprehensively satisfy various physical properties required as a substrate material for 5G communication devices.

Envisioning the use of in-situ arm movement data in stroke rehabilitation: Stroke survivors' and occupational therapists' perspectives.

BACKGROUND: The key for successful stroke upper-limb rehabilitation includes the personalization of therapeutic interventions based on patients' functional ability and performance level. However, therapists often encounter challenges in supporting personalized rehabilitation due to the lack of information about how stroke survivors use their stroke-affected arm outside the clinic. Wearable technologies have been considered as an effective, objective solution to monitor patients' arm use patterns in their naturalistic environments. However, these technologies have remained a proof of concept and have not been adopted as mainstream therapeutic products, and we lack understanding of how key stakeholders perceive the use of wearable technologies in their practice. OBJECTIVE: We aim to understand how stroke survivors and therapists perceive and envision the use of wearable sensors and arm activity data in practical settings and how we could design a wearable-based performance monitoring system to better support the needs of the stakeholders. METHODS: We conducted semi-structured interviews with four stroke survivors and 15 occupational therapists (OTs) based on real-world arm use data that we collected for contextualization. To situate our participants, we leveraged a pair of finger-worn accelerometers to collect stroke survivors' arm use data in real-world settings, which we used to create study probes for stroke survivors and OTs, respectively. The interview data was analyzed using the thematic approach. RESULTS: Our study unveiled a detailed account of (1) the receptiveness of stroke survivors and OTs for using wearable sensors in clinical practice, (2) OTs' envisioned strategies to utilize patient-generated sensor data in the light of providing patients with personalized therapy programs, and (3) practical challenges and design considerations to address for the accelerated integration of wearable systems into their practice. CONCLUSIONS: These findings offer promising directions for the design of a wearable solution that supports OTs to develop individually-tailored therapy programs for stroke survivors to improve their affected arm use.

Top-Down Approaches for 10 nm-Scale Nanochannel: Toward Exceptional H2S Detection.

Metal oxide semiconductors (MOS) have proven to be most powerful sensing materials to detect hydrogen sulfide (H2S), achieving part per billion (ppb) level sensitivity and selectivity. However, there has not been a way of extending this approach to the top-down H2S sensor fabrication process, completely limiting their commercial-level productions. In this study, we developed a top-down lithographic process of a 10 nm-scale SnO2 nanochannel for H2S sensor production. Due to high-resolution (15 nm thickness) and high aspect ratio (>20) structures, the nanochannel exhibited highly sensitive H2S detection performances (Ra/Rg = 116.62, τres = 31 s at 0.5 ppm) with selectivity (RH2S/Racetone = 23 against 5 ppm acetone). In addition, we demonstrated that the nanochannel could be efficiently sensitized with the p-n heterojunction without any postmodification or an additional process during one-step lithography. As an example, we demonstrated that the H2S sensor performance can be drastically enhanced with the NiO nanoheterojunction (Ra/Rg = 166.2, τres = 21 s at 0.5 ppm), showing the highest range of sensitivity demonstrated to date for state-of-the-art H2S sensors. These results in total constitute a high-throughput fabrication platform to commercialize the H2S sensor that can accelerate the development time and interface in real-life situations.

Atomic-Scale Homogeneous RuCu Alloy Nanoparticles for Highly Efficient Electrocatalytic Nitrogen Reduction.

Ruthenium (Ru) is the most widely used metal as an electrocatalyst for nitrogen (N2 ) reduction reaction (NRR) because of the relatively high N2 adsorption strength for successive reaction. Recently, it has been well reported that the homogeneous Ru-based metal alloys such as RuRh, RuPt, and RuCo significantly enhance the selectivity and formation rate of ammonia (NH3 ). However, the metal combinations for NRR have been limited to several miscible combinations of metals with Ru, although various immiscible combinations have immense potential to show high NRR performance. In this study, an immiscible combination of Ru and copper (Cu) is first utilized, and homogeneous alloy nanoparticles (RuCu NPs) are fabricated by the carbothermal shock method. The RuCu homogeneous NP alloys on cellulose/carbon nanotube sponge exhibit the highest selectivity and NH3 formation rate of ≈31% and -73 µmol h-1 cm-2 , respectively. These are the highest values of the selectivity and NH3 formation rates among existing Ru-based alloy metal combinations.