Strong Interaction Effects Research Articles

Short‐Time and High‐Performance Recovery of Critical Metal Elements From Spent Ternary Lithium‐Ion Batteries by Selective Synergic Coordination Effect

AbstractRecycling of spent ternary lithium‐ion batteries (LIBs) is crucial for both resource recovery and environmental sustainability. Deep eutectic solvents (DESs), as an eco‐friendly leaching agent, offer a promising alternative to mineral acids for the metal recovery. However, current DES‐based systems primarily focus on binary LiCoO2 batteries and suffer from complex leaching conditions, including high operation temperature, long reaction time. Herein, a dual‐function green solution is synthesized using choline chloride and tartaric acid, enabling direct precipitation of nickel (Ni) and separation of cobalt (Co), manganese (Mn), and lithium (Li) within 50 min at 100 °C, without requiring additional reducing agents throughout the process. The recovery efficiency of Li, Ni, Co, and Mn reached 99.2%, 97.3%, 94.4%, and 94.1%, respectively, while the purity of Ni products approached 99.9%. The used system is suitable for recycling various types of ternary batteries. Additionally, both theoretical and experimental analyses reveal that Ni ions exhibit superior binding energy and coordination with DESs compared to other ions, attributed to the synergistic effect of strong interaction and stable chelation. This research offers a promising method for the replenishment of essential metal resources.

Data-driven optimization of additive composite manufacturing using automated fibre placement: A study on process parameters effects and interactions

The influence of process parameters and their interactions on the interlaminar shear strength (ILSS) of additively manufactured CF-PEEK composites was investigated. The composites were made using a hot gas torch (HGT)-based automated fibre placement (AFP) process. Experimental short beam shear (SBS) tests and FEA numerical simulations with bonding models were conducted to generate data. Various process parameter combinations were analyzed using full-factorial statistical assessment. Results show that ILSS increases with decreasing deposition speeds and increasing HGT temperatures, with consolidation forces having a milder effect. A strong interaction effect between parameters was observed, and the effect of HGT temperature on ILSS depends on material deposition speed. The optimum ILSS were at low deposition speeds (76 mm/s) with high HGT temperatures (900 °C or 950 °C), also evident in the fracture surfaces. This database can aid smart production planning in additive manufacturing of composites by optimizing process parameters for maximum interlaminar strength.

Metabolic status is a key factor influencing proteomic changes in ewe granulosa cells induced by chronic BPS exposure

BackgroundBisphenol S (BPS) is the main substitute for bisphenol A (BPA), a well-known plasticiser and endocrine disruptor. BPS disrupts ovarian function in several species. Moreover, a few studies have reported that the effects of BPS might be modulated by the metabolic status, and none have characterised the granulosa cell (GC) proteome after chronic BPS exposure.ObjectivesThis study aimed to decipher the mechanisms of action of chronic BPS exposure on the proteome of ewe GCs while considering the interaction between a deliberate contrasted metabolism and reproductive function.MethodsForty ewes were split into two groups with contrasted diets: restricted (R, n = 20) and well-fed (WF, n = 20). The R and WF ewes were subdivided according to the dose of BPS administered through the diet (0–50 µg/kg/day), forming four groups: R0, R50, WF0 and WF50. After 3-month BPS daily exposure, GCs were recovered during the pre-ovulatory stage and proteins were analysed by nano-liquid chromatography coupled with tandem mass spectrometry.ResultsChronic exposure to BPS affected the GC proteome differently according to the ewe metabolic status. Fifty-nine out of 958 quantified proteins were differentially abundant between groups and are mainly involved in carbohydrate and lipid pathways. Unsupervised hierarchical clustering of differentially abundant proteins (DAPs) identified four clusters of 34, 6, 5 and 14 proteins according to the BPS exposure and diet interaction. Pairwise comparisons between groups also revealed a strong effect of BPS exposure and diet interaction. Functional analysis of DAPs highlighted that BPS upregulated β-glucuronidase (GUSB; p = 0.002), a protein especially able to deconjugate bisphenol glucuronides (BP-g). Moreover, among unexposed ewes, GUSB was detected only in well-fed ewes.DiscussionConjugation of glucuronides inhibits the oestrogenic activity of bisphenols. Upregulation of GUSB in ewes dosed with BPS would prolong the oestrogenic effects of BPS by deconjugating BPS-g into free BPS. In addition, literature has reported an up-regulation of GUSB in people suffering from obesity. Therefore, people suffering from obesity could be subjected to prolonged and aggravated exposure to BPS. These data highlighted the deleterious effects of BPS and its interaction with metabolic status.

Controlling Metal-Support Interactions to Engineer Highly Active and Stable Catalysts for COx Hydrogenation.

This perspective focuses on the modulation of metal-support interaction (MSI) in catalysts for COx hydrogenation, highlighting their profound impact on catalytic performance. Firstly, it outlines different strategies, including the use of highly reducible oxides and moderate reduction treatments, which induce the classical strong metal-support interaction (SMSI) effect and the electronic metal-support interaction (EMSI) effect. Morphology engineering and crystalline phase manipulation of oxides presented as effective methods to control EMSI are also discussed. The discrimination of SMSI and EMSI can be achieved using oxides with low encapsulation tendencies, such as ZrO2, which supports electronic modifications without or minimizing the overgrowth issues, optimizing the catalytic performance for methanation. Then, the synergy between Cu and ZnO in methanol synthesis, enhanced by SMSI, is emphasized inside. Optimizing support oxides to control oxygen vacancies enhances the catalytic performance of CO2 hydrogenation to methanol. Perspectives for the future research on the fundamental understanding of structure-MSI-performance relationship for catalyst design is discussed.

Strong metal-support interaction induced excellent performance for photo-thermal catalysis methane dry reforming over Ru-cluster-ceria catalyst

The utilization of solar energy in driving methane dry reforming (MDR) reaction through photo-thermal synergetic catalysis could simultaneously realize the reduction of carbon footprint and the fixation of solar energy, which is an environmentally and economically beneficial route. However, the currently developed catalysts still require further optimization in catalytic performance and stability. Here, we prepared RuNC, a Ru nanoclusters catalyst anchored on CeO2 with strong metal-support interaction (SMSI) effect via a facile preparation method. The formation rates of H2 and CO over RuNC catalyst were 1.41 and 2.16 mol·gcat−1·h−1 respectively under photo-thermal catalysis (PTC) at 500 °C, which is up to one order of magnitude higher than existing PTC-MDR catalysts. Moreover, the prepared catalyst did not show significant deactivation under 100 hour test. The in-depth structure-function relationship between catalytic activity/stability and Ru nanocluster sites/induced SMSI effect were determined by systematic structural characterization. The advantages of RuNC catalyst structure under PTC catalytic conditions were demonstrated by optical characterization. Besides, the excitation-migration path of photo-electron under PTC conditions was determined by ISI-XPS experiments. Finally, the mechanism of PTC-MDR reaction and the specific reaction steps enhanced by light irradiation were determined by operando experiments. This work provided theoretical basis and excellent catalyst for the industrial application of PTC-MDR route.

Power performance optimization of twin vertical axis wind turbine for farm applications

Twin Vertical Axis Wind Turbines (VAWTs) appear to be the potential candidates for enhancing the power density of wind farms. Earlier twin-VAWT studies were mainly focused on carrying out the parametric study of one or two twin-VAWT variables together while treating the other variables as fixed. Since twin-VAWT variables strongly influence each other, therefore, this approach led to reporting conflicting optimal combinations of twin-VAWT variables for optimizing its power performance. Furthermore, previous studies considered higher rotor solidities, which is not desirable from a power generation perspective and it also reduces turbine lifespan. Using L16 (45) Taguchi orthogonal array along with a modified superposition model, this research optimized the power performance of twin-VAWT considering together, solidity ratio (σ), pitch angle (β), wind direction angle (δ), turbine spacing ratio (S/D) and rotational direction (φ) of the rotor as design variables. Unsteady CFD was employed to ascertain the power performance of orthogonal array designs followed by the orthogonal analysis for the optimum tip-speed ratios (TSRs) and power coefficients. Based upon the relative impact of the design variables on the mean optimum TSR, the variables were ranked as: (σ) > (δ) > (φ) > (β) and (S/D). On the other hand, a different trend for the relative impact of the design variables on the power performance was found: (β) > (δ) > (σ) > (φ) > (S/D). The orthogonal analysis optimum twin-VAWT configuration showed a positive synergistic interaction at TSRs > 2.5 and negative synergistic interaction at TSRs ≤ 2.5 in comparison to its standalone counterpart. Moreover, the orthogonal analysis’s optimum twin-VAWT underperformed by 4.7 % when compared with the best orthogonal array design at their respective optimum TSRs, indicating a sub-optimal design resulting from Taguchi orthogonal analysis. The incapability of orthogonal analysis to include the interaction effects among variables resulted in the sub-optimal twin-VAWT design. Linear graph method highlighted the presence of strong interaction effects among the design variables. For five design variables with four levels each (45), a full factorial Design of Experiments (DoE) of 1024 cases was constructed to include the interaction effects. Signal-to-noise ratios of full factorial DoE cases were predicted by the modified superposition model. The highest predicted signal-to-noise ratio among 1024 cases identified the best orthogonal array design as the actual optimum twin-VAWT configuration. The increase in power efficiency of twin-VAWT in comparison to its standalone counterpart was observed as the TSR was increased with the maximum power enhancement by 23 % at TSR 4.25. Furthermore, the optimal twin-VAWT configuration exhibited an optimal TSR of 3.5, contrasting with the standalone VAWT's optimal TSR of 3.0. This shift extended the power performance envelope of the optimum twin-VAWT, allowing for energy extraction across a broader range of TSR values. The undertaken study highlights the prospects of twin-VAWTs for increasing the farm power density.

Enhanced Selectivity to Methanol in CO2 Hydrogenation on CuO/ZrO2 Catalysts by Alkali Metal Modification

AbstractIn order to alleviate the influence of greenhouse effect on global climate change, the effective utilization of CO2 to prepare fine chemicals should be paid more attention to, however, which is greatly blocked by the catalyst with low efficiency. Here, alkali metal (Li, Na, or K) are employed as a modification aid to prepare CuO/ZrO2 catalyst for CO2 hydrogenation to methanol. The effects of alkali metal on physicochemical properties and catalytic activities of CuO/ZrO2 catalyst were studied in detail by the XRD, N2‐physisorption, ICP‐OES, SEM/EDS, H2/N2O/CO2/NH3‐chemisorption, and evaluation test. The results verified that the use of complex combustion method enabled the uniform combination of all components in precursor. High‐temperature calcination (700 °C) further enhanced the strong interaction and synergistic effect between Cu and ZrO2. Most importantly, the introduction of alkali metal effectively altered the structure and catalytic activity of CuO/ZrO2 catalysts. However, the selectivity to methanol increased while the CO2 conversion decreased regardless of different kinds of alkali metal being introduced to the CuO/ZrO2 catalysts. For example, CuO/ZrO2 catalyst modified by K exhibited excellent performance for methanol production that 98.9% selectivity of methanol based on 8.8% conversion of CO2 after 48 h online reaction.

Atomic Level Understanding of the Structural Stability and Catalytic Activity of Nanoporous Gold/Titania Cluster Inverse Catalysts at Ambient and High Temperatures.

Nanoporous gold (NPG) exhibits exceptional catalytic performance at low temperatures, but its activity declines at elevated temperatures due to structural coarsening. Loading metal oxide nanoparticles onto NPG can enhance its catalytic activity at high temperatures. In this work, we used NPG-supported titania nanoparticles as a model system (denoted as Ti2O4/NPG) to study their catalytic activity at ambient and high temperatures with CO oxidation as a probe reaction by density functional theory (DFT) calculation and ab initio molecular dynamics (AIMD) simulations. The possible factors that may affect the CO oxidation reaction pathways and energy profiles on the Ti2O4/NPG, such as oxygen vacancies; silver impurities; Mars-van Krevelen (MvK), Eley-Rideal (ER), or trimolecular Eley-Rideal (TER) mechanisms; and catalytic active sites, were comprehensively investigated. The results showed that reaction energy barriers on Ti2O4/NPG were not significantly decreased compared to the pristine NPG, indicating that their catalytic activities at ambient temperature were comparable. At the evaluated temperature (400 °C), the Ti2O4/NPG exhibited superior thermal stability and maintained its active sites, while the NPG reduced active sites due to surface coarsening. The strong oxide-metal interaction (SOMI) effect between the NPG and Ti2O4 nanoparticles is found to be a main factor for the high structural stability and catalytic activity at high temperatures.

Deciphering promotion of MoP over MoC in Pt catalysts for methanol-assisted water splitting reaction

Molybdenum-based compounds show promising promotion effects on Pt catalysts for energy-relevant catalysis reactions. Herein, a more effective promotion effect of MoP than MoC was found in assisting Pt nanoparticles for methanol-assisted hydrogen generation in light of the strong metal-support interaction and synergistic effect between Pt and MoP/C nanospheres. Electrochemical analyses and theoretical calculations demonstrated that Pt-MoP/C facilitated the oxidation and removal of CO intermediates more effectively than Pt-MoC/C. This enhanced performance was attributed to the distinct 6-coordination environment of hexagonal MoP and the elevated electron density of Mo induced by phosphorus. These structural and electronic features significantly enhanced electron transfer to Pt, thereby creating strong metal-support interaction and synergistic effect to improve the overall catalytic efficiency. Especially, the unique activities of Moδ+ and Moδ- in the MoP modified the surface structure of Pt, lowered the Pt d-band center, and optimized the local chemical state of Pt atoms, which resulted in more optimized adsorption energy and charge transfer capabilities of intermediates. The Pt-MoP/C electrolyzer thus showed both lower cell voltage than that of Pt-MoC/C and Pt/C electrolyzers in water splitting and methanol-assisted water splitting for hydrogen generation. This study offers insightful information about the promotion effect of molybdenum-based compounds in Pt catalyst systems in energy-relevant catalysis reactions.

Comparative Transcriptomic Analyses Reveal Differences in the Responses of Diploid and Triploid Eastern Oysters to Environmental Stress.

Triploid oysters are commonly used as the basis for production in the aquaculture of eastern oysters along the USA East and Gulf of Mexico coasts. While they are valued for their rapid growth, incidents of triploid mortality during summer months have been well documented in eastern oysters, especially at low salinity sites. We compared global transcriptomic responses of diploid and triploid oysters bred from the same three maternal source populations at two different hatcheries and outplanted to a high (annual mean salinity = 19.4 ± 6.7) and low (annual mean salinity = 9.3 ± 5.0) salinity site. Oysters were sampled for gene expression at the onset of a mortality event in the summer of 2021 to identify triploid-specific gene expression patterns associated with low salinity sites, which ultimately experienced greater triploid mortality. We also examined chromosome-specific gene expression to test for instances of aneuploidy in experimental triploid oyster lines, another possible contributor to elevated mortality in triploids. We observed a strong effect of hatchery conditions (cohort) on triploid-specific mortality (field data) and a strong interactive effect of hatchery, ploidy, and outplant site on gene expression. At the low salinity site where triploid oysters experienced high mortality, we observed downregulation of transcripts related to calcium signaling, ciliary activity, and cell cycle checkpoints in triploids relative to diploids. These transcripts suggest dampening of the salinity stress response and problems during cell division as key cellular processes associated with elevated mortality risk in triploid oysters. No instances of aneuploidy were detected in our triploid oyster lines. Our results suggest that triploid oysters may be fundamentally less tolerant of rapid decreases in salinity, indicating that oyster farmers may need to limit the use of triploid oysters to sites with more stable salinity conditions.

Salinization and low-dose levels of pesticides alter brain shape of larval amphibians

Wetland communities are increasingly threatened by multiple stressors simultaneously, such as pesticides and salinization. We examined the effects of ecologically-relevant exposures to broad-spectrum insecticides and salinization on amphibian neurodevelopment, which is strongly linked to how organisms respond behaviorally to environmental change. Prior research showed that exposure to trace concentrations of an organophosphate pesticide (chlorpyrifos) altered the brain shape and behavior of larval and metamorphic amphibians. It is unknown whether brain shape is altered by additional pesticides and road salt. Using outdoor mesocosms, we tested whether salt (NaCl) and representatives from three pesticide families (organophosphates, pyrethroids, and neonicotinoids) altered tadpole (Lithobates pipiens) brain shape. Of the two organophosphates, chlorpyrifos induced relatively longer telencephalon lengths relative to body mass, consistent with previous studies, but malathion had no effect on brain shape. Of the two pyrethroids, permethrin, but not cypermethrin, increased telencephalon length. For the neonicotinoids, there were marginally significant effects of imidacloprid and thiamethoxam on telencephalon length. Thus, the impacts of pesticides on brain shape was not dictated by pesticide family. Exposure to relatively high concentrations of salt resulted in brains that were less wide but had longer optic tecta. Although we failed to find strong interactive effect of salt with pesticides, there was some weak, nonsignificant, evidence that exposure to salt masked responses to pesticides. Together, our results indicate that environmentally realistic levels of pesticides and salinization can alter larval brain shape. Our study highlights the importance of studying the impacts of naturally-occurring levels of pesticides and salinization on vertebrate neural development.

Origin and fate of the pseudogap in the doped Hubbard model

The relationship between the pseudogap and underlying ground-state phases has not yet been rigorously established. We investigated the doped two-dimensional Hubbard model at finite temperature using controlled diagrammatic Monte Carlo calculations, allowing for the computation of spectral properties in the infinite-size limit and with arbitrary momentum resolution. We found three distinct regimes as a function of doping and interaction strength: a weakly correlated metal, a correlated metal with strong interaction effects, and a pseudogap regime at low doping. We show that the pseudogap forms both at weak coupling, when the magnetic correlation length is large, and at strong coupling, when it is shorter. As the temperature goes to zero, the pseudogap regime extrapolates precisely to the ordered stripe phase found by ground-state methods.

Non-platinum-based electrocatalysts for high performance acidic hydrogen evolution reactions in proton exchange membrane water electrolysis

To replace Pt for economical and scalable H2O to H2 conversion in proton exchange membranes (PEM) water electrolysis, efforts are being made to find cost-effective catalysts that delicately co-ordinate key parameters such as high mass activity and long durability. We found that by engineering the small size (<5 nm) of RuMo bimetallic nanoparticles (NPs) on carbon nanotube (CNT) with a strong metal-support interaction (SMSI) effect, we could obtain Mo2.8-Ru@CNT-2.8 catalysts with optimal activity and stability in acidic HER (Mo amount is 2.8 wt% and NPs size is about 2.8 nm). Experimental and theoretical studies reveal that reducing the size to about 2.8 nm can simultaneously obtain the optimal Gibbs free energy of H intermediate adsorption and the bonding energy between Ru-Ru/Ru-Mo, which contribute to the improvement of the catalytic activity and stability of Mo2.8-Ru@CNT-2.8. In addition, Mo doping can greatly increase the formation energy of Ru vacancies, which contribute to the improvement of the catalytic stability of Mo2.8-Ru@CNT-2.8. Specially, the optimized Mo2.8-Ru@CNT-2.8 catalyst represent an advance in mass activity (4.4 A mg−1Ru) compared to commercial Pt/C catalysts (3.1 A mg−1Ru) and high per-site activity (32.7 s−1) under acidic conditions while remaining the excellent stability. At the same time, Mo2.8-Ru@CNT-2.8 as a cathode in the proton exchange membrane (PEM) water electrolyser exhibits excellent cell voltages with a Ru loading of 0.06 mgRu cm−2 (1.7 V to 1 A cm−2), 0.12 times that of commercial Pt/C with a Pt loading of 0.5 mgPt cm−2 (1.74 V up to 1 A cm−2), and maintains long-term stability for 700 h at 1 A cm−2.

Fluid–acoustic–structure resonance mechanism of a plane cascade via a low-speed wind tunnel test

Acoustic resonance is an important factor that contributes to aeroengine compressor failure. In this study, a plane cascade of compressor blades was designed to reproduce acoustic resonance via a low-speed wind tunnel test. A high-frequency hot-wire, microphone and strain gauge were used to synchronously measure the fluid, acoustic and structural parameters. We analysed the variation in the amplitude and frequency of the multi-field parameters with increasing mean flow velocity and explored the multi-field interaction mechanism that induces the acoustic resonance of the plane cascade. The plane cascade effectively reproduced the acoustic resonance phenomenon. The first-order acoustic-mode frequency of the plane cascade flow duct, second-order torsional vibration mode frequency of the blade and shedding mode frequency of the tip clearance leakage vortex were equal under acoustic resonance. The fluid, acoustic and structural fields showed a strong interaction effect, achieving the maximum blade vibration amplitude and causing fatigue cracks of torsional vibration at the blade root. The frequency lock-in region of the compressor plane cascade was divided into an ‘acoustic–structure’ interaction region, a ‘fluid–acoustic–structure’ interaction region and a first-order acoustic-mode dominant region with increasing mean flow velocity, which demonstrates an interesting phenomenon in which the fluid–acoustic–structure modes compete: acoustic mode &gt; blade vibration mode &gt; vortex shedding mode. The results demonstrate a unique approach to the study of acoustic resonance that provides insight into the acoustic resonance mechanism in a cascade of compressor blades.

Efficient hydrogen production from formic acid dehydrogenation over ultrasmall PdIr nanoparticles on amine-functionalized yolk-shell mesoporous silica

Developing heterogeneous catalysts with exceptional catalytic activity over formic acid (HCOOH, FA) dehydrogenation is imperative to employ FA as an effective hydrogen (H2) carrier. In this work, ultrasmall (1.4 nm) and well-dispersed PdIr nanoparticles (NPs) immobilized on amine-functionalized yolk-shell mesoporous silica nanospheres (YSMSNs) with radially oriented mesoporous channels have been synthesized by a co-reduction strategy. The optimized catalyst Pd4Ir1/YSMSNs-NH2 (Pd/Ir molar ratio = 4:1) exhibited a remarkable turnover frequency (TOF) of 5818 h−1 and remarkable stability at 50 °C with the addition of sodium formate (SF), resulting in complete FA conversion and H2 selectivity, exceeding most of the solid heterogeneous catalysts in previous reports under similar circumstances. Kinetic isotope effect (KIE) exploration indicates the cleavage of the CH bond is regarded as the rate-determining step (RDS) during the FA dehydrogenation process. Such excellent catalytic properties arise from the ultrafine and well-dispersed PdIr NPs supported on the nanosphere support YSMSNs-NH2, the electronic synergistic effect of PdIr alloy NPs, and the strong metal-support interaction (MSI) effect between the introduced PdIr NPs and YSMSNs-NH2 support. This work offers a new paradigm for exploiting the highly effective silica-supported Pd-based heterogeneous catalysts over the dehydrogenation of FA.

Electronic interaction and band alignment between Cu sub-nanometric clusters and ZnIn2S4 nanosheets towards selective photoreduction of CO2 to CO

Engineering the electronic properties of heterogeneous photocatalysts with charge transfer regulated is an effective strategy to boost their CO2 reduction activity. Here, we drew inspiration from the strong metal-support interaction effect, and fabricated ZnIn2S4 supported-Cu sub-nanometric clusters photocatalyst. Within this catalyst, the formed CuS bond would redistribute interfacial charge, resulting in diverse chemical states of Cu atoms and the establishment of Ohmic contact between ZIS and Cu. The built-in electric field of such Ohmic contact benefited the migration of photoexcited electrons from ZIS to Cu. Further mechanistic studies unveiled the crucial role of positively charged Cu atom near the interface in facilitating H2O dissociation, and its adjacent Cu atom at the top-surface adopting a distinct chemical state could activate linear CO2 molecule into a bent configuration. These features substantially lowered energy barriers for CO2 adsorption and subsequent *COOH formation, and Cu-ZIS, accordingly, exhibited a remarkable CO production rate of 60.2 μmol g-1h−1, approximately 20.8-fold enhancement compared to ZIS counterpart.

Nanoscale Optical Conductivity Imaging of Double-Moiré Twisted Bilayer Graphene.

A central paradigm of moiré materials relies on the formation of superlattices that yield enlarged effective crystal unit cells. While a critical consequence of this phenomenon is the celebrated flat electronic bands that foster strong interaction effects, the presence of superlattices has further implications. Here we explore the advantages of moiré superlattices in twisted bilayer graphene (TBG) aligned with hexagonal boron nitride (hBN) for passively enhancing optical conductivity in the low-energy regime. To probe the local optical response of TBG/hBN double-moiré lattices, we use infrared (IR) nano-imaging in conjunction with nanocurrent imaging to examine local optical conductivity over a wide range of TBG twist angles. We show that interband transitions associated with the multiple moiré flat and dispersive bands produce tunable transparent IR responses even at finite carrier densities, which is in stark contrast to the previously limited metallic near transparency observed only in undoped pristine graphene.

Skeuomorphic or flat? The effects of icon style on visual search and recognition performance

Although there have been many previous studies on icon visual search and recognition performance, only a few have considered the effects of both the internal and external characteristics of icons. In this behavioral study, we employed a visual search task and a semantic recognition task to explore the effects of icon style, semantic distance (SD), and task difficulty on users’ performance in perceiving and identifying icons. First, we created and filtered 64 new icons, which were divided into four different groups (flat design & close SD, flat design & far SD, skeuomorphic design & close SD, skeuomorphic design & far SD) through expert evaluation. A total of 40 participants (13 men and 27 women, ages ranging from 19 to 25 years, mean age = 21.9 years, SD=1.93) were asked to perform an icon visual search task and an icon recognition task after a round of learning. Participants’ accuracy and response time were measured as a function of the following independent variables: two icon styles (flat or skeuomorphic style), two levels of SD (close or far), and two levels of task difficulty (easy or difficult). The results showed that flat icons had better visual search performance than skeuomorphic icons; this beneficial effect increased as the task difficulty increased. However, in the icon recognition task, participants’ performance in recalling skeuomorphic icons was significantly better than that in recalling flat icons. Furthermore, a strong interaction effect between icon style and task difficulty was observed for response time. As the task difficulty decreased, the difference in recognition performance between these two different icon styles increased significantly. These findings provide valuable guidance for the design of icons in human–computer interaction interfaces.

Dual confinement of RuOx nanoparticle using polar MnNiO and armored carbon for boosting water electrolysis

The poor stability of nanoparticle catalysts with catalytic activity is a significant obstacle to their industrial application. The establishment of rational nanoparticle structures to elucidate the relationship between catalyst structure and its catalytic activity and stability is crucial for constructing nanoparticle catalysts that are both highly active and stable. We propose a strategy to construct a dual-confinement effect of the nanoparticle, specifically by regulating the polarization of the MnNiO support to enhance strong oxide-support interactions (SOSI) and encapsulating the outer layer of nanoparticles with a carbon shell, which has been proven effective in improving the activity and stability of nanoparticle-based oxygen evolution reaction (OER) electrocatalysts. At a current density of 100 mA cm−2, the armor C@RuOx@MnNiO catalyst displays an overpotential of 260 mV for the OER. After the OER test for 100 h, the current density of C@RuOx@MnNiO shows no significant decay, whereas that of RuOx@MnNiO and RuOx@MnO rapidly decreases, indicating significant catalytic activity and stability of the catalyst. The assembled C@RuOx@MnNiO||Pt/C electrode demonstrates excellent alkaline water electrolysis performance in an MEA electrolyzer, requiring only a low cell voltage of 1.76 V to achieve an ampere-level current density of 1 A cm−2. In-situ electrochemical Raman spectroscopy reveals the significant interaction between nanoparticles and the polar support. The reduction in Gibbs free energy, which establishes the rate-determining step (RDS) of OER, is caused by the charge redistribution caused by polar Mn doping in RuOx@MnNiO and the coordination structure modifications, as shown by density functional theory calculations. This work provides an approach to designing efficient and stable nanoparticle electrocatalysts through the dual-confinement effect of SOSI-induced strong interactions and armor carbon layers.

Soundscapes and airborne laser scanning identify vegetation density and its interaction with elevation as main driver of bird diversity and community composition

AbstractAimMountain ecosystems are hotspots of biodiversity due to their high variation in climate and habitats. Yet, above average rates of climate change and enhanced forest disturbance regimes alter local climatic conditions and vegetation structure, which should impact biodiversity. We here investigated the impact of vegetation and elevation as well as their interactions on bird communities to improve our ability to predict climate change effects on bird communities.LocationEuropean Alps, Germany.MethodsWe studied patterns and drivers of bird communities at 213 plots along gradients in vegetation density and elevation using autonomous sound recorders. Bird species were identified from soundscapes by Convolutional Neural Networks (BirdNET) and taxonomists.ResultsBird diversity and community metrics were moderately to strongly correlated for data based on either identification by BirdNET or taxonomists (Pearson's r = .47–.94), and ecological findings were overall similar for both datasets. Vegetation density 1–2 m and &gt;2 m above ground strongly affected bird diversity and community composition and mediated effects of elevation. Community composition changed with elevation more strongly in habitats with low than high vegetation density &gt;2 m. Species numbers decreased with elevation in habitats with low vegetation density 1–2 m and &gt;2 m above ground, but increased in habitats with high vegetation density. Overall, functional and phylogenetic diversity increased with elevation indicating lower habitat filtering, but patterns were also mediated by vegetation density.Main ConclusionsOur results indicate that bird communities in the German Alps are determined by strong interactive effects of elevation and vegetation, underlining the importance to consider variation in vegetation in studies of biodiversity patterns along elevational gradients and under climate change. Combining remote sensing data and biodiversity monitoring based on autonomous sampling and AI‐based species identification opens new avenues for bird monitoring and research in remote areas.