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Developing high‐performance, low‐cost, and robust bifunctional electrocatalysts for overall water splitting is extremely indispensable and challenging. It is a promising strategy to couple highly active precious metals with transition metals as efficient electrocatalysts, which can not only effectively reduce the cost of the preparation procedure, but also greatly improve the performance of catalysts through a synergistic effect. Herein, Ru and Ni nanoparticles embedded within nitrogen‐doped carbon nanofibers (RuNi‐NCNFs) are synthesized via a simple electrospinning technology with a subsequent carbonization process. The as‐formed RuNi‐NCNFs represent excellent Pt‐like electrocatalytic activity for the hydrogen evolution reaction (HER) in both alkaline and acidic conditions. Furthermore, the RuNi‐NCNFs also exhibit an outstanding oxygen evolution reaction (OER) activity with an overpotential of 290 mV to achieve a current density of 10 mA cm−2 in alkaline electrolyte. Strikingly, owing to both the HER and OER performance, an electrolyzer with RuNi‐NCNFs as both the anode and cathode catalysts requires only a cell voltage of 1.564 V to drive a current density of 10 mA cm−2 in an alkaline medium, which is lower than the benchmark of Pt/C||RuO2 electrodes. This study opens a novel avenue toward the exploration of high efficient but low‐cost electrocatalysts for overall water splitting. A facile strategy based on electrospinning and a postcarbonization process is demonstrated to prepare carbon nanofibers incorporating Ru and Ni nanoparticles, which exhibits admirable Pt‐like hydrogen evolution reaction activity and superior oxygen evolution reaction performance. The electrolyzer with this hybrid as both anode and cathode displays a remarkable electrocatalytic activity and outstanding long‐term durability, which outperforms the commercial Pt/C||RuO2 electrocatalyst.

The realization of large‐scale industrial application of alkaline water electrolysis for hydrogen generation is severely hampered by the cost of electricity. Therefore, it is currently necessary to synthesize highly efficient electrocatalysts with excellent stability and low overpotential under an industrial‐level current density. Herein, Ir‐incorporated in partially oxidized Ru aerogel has been designed and synthesized via a simple in situ reduction strategy and subsequent oxidation process. The electrochemical measurements demonstrate that the optimized Ru98Ir2‐350 electrocatalyst exhibits outstanding hydrogen evolution reaction (HER) performance in an alkaline environment (1 M KOH). Especially, at the large current density of 1000 mA cm−2, the overpotential is as low as 121 mV, far exceeding the benchmark Pt/C catalyst. Moreover, the Ru98Ir2‐350 catalyst also displays excellent stability over 1500 h at 1000 mA cm−2, denoting its industrial applicability. This work provides an efficient route for developing highly active and ultra‐stable electrocatalysts for hydrogen generation under industrial‐level current density. A facile strategy based on an in situ reduction and subsequent controllable oxidation process has been demonstrated to prepare Ir‐incorporated in partially oxidized Ru aerogel (Ru98Ir2‐350) with a 3D porous structure, which exhibits a remarkable HER performance in an alkaline solution. The as‐prepared Ru98Ir2‐350 catalyst shows highly active and ultra‐stable catalytic properties under industrial‐level current density, significantly superior to the commercial Pt/C and many previously reported catalysts.

PurposeAlgorithms are widely used to manage various activities in the gig economy. Online car-hailing platforms, such as Uber and Lyft, are exemplary embodiments of such algorithmic management, where drivers are managed by algorithms for task allocation, work monitoring and performance evaluation. Despite employing substantially, the platforms face the challenge of maintaining and fostering drivers' work engagement. Thus, this study aims to examine how the algorithmic management of online car-hailing platforms affects drivers' work engagement.Design/methodology/approachDrawing on the transactional theory of stress, the authors examined the effects of algorithmic monitoring and fairness on online car-hailing drivers' work engagement and revealed the mediation effects of challenge-hindrance appraisals. Based on survey data collected from 364 drivers, the authors' hypotheses were examined using partial least squares structural equation modeling (PLS-SEM). The authors also applied path comparison analyses to further compare the effects of algorithmic monitoring and fairness on the two types of appraisals.FindingsThis study finds that online car-hailing drivers' challenge-hindrance appraisals mediate the relationship between algorithmic management characteristics and work engagement. Algorithmic monitoring positively affects both challenge and hindrance appraisals in online car-hailing drivers. However, algorithmic fairness promotes challenge appraisal and reduces hindrance appraisal. Consequently, challenge and hindrance appraisals lead to higher and lower work engagement, respectively. Further, the additional path comparison analysis showed that the hindering effect of algorithmic monitoring exceeds its challenging effect, and the challenge-promoting effect of algorithmic fairness is greater than the algorithm's hindrance-reducing effect.Originality/valueThis paper reveals the underlying mechanisms concerning how algorithmic monitoring and fairness affect online car-hailing drivers' work engagement and fills the gap in the research on algorithmic management in the context of online car-hailing platforms. The authors' findings also provide practical guidance for online car-hailing platforms on how to improve the platforms' algorithmic management systems.

PurposeIn online user innovation communities (UICs), firms adopt external innovations beyond their internal resources and capabilities. However, little is known about the influences of organizational adoption or detailed adoption patterns on subsequent user innovation. This study aims to examine the influence of organizational adoption, including its level and timing, on users' subsequent innovation behavior and performance.Design/methodology/approachThis research model was validated using a secondary dataset of 17,661 user–innovation pairs from an online UIC. The effect of organizational adoption on users' subsequent innovation likelihood was measured by conducting a panel logistic regression. Furthermore, the effects of organizational adoption on subsequent innovation’ quality and homogeneity and those of the adoption level and timing on subsequent innovation likelihood were tested using Heckman's two-step approach.FindingsThe authors found that organizational adoption negatively affects the likelihood of subsequent innovation and its homogeneity but positively affects its quality. Moreover, more timely and lower-level adoption can increase the likelihood of users' subsequent innovation.Originality/valueThis study comprehensively explores organizational adoption's effects on users' subsequent innovation behavior and performance, contributing to the literature on UICs and user innovation adoption. It also provides valuable practical implications for firms on how to optimize their adoption decisions to maintain the quantity, quality, and diversity of user innovations.

Anodic oxygen evolution reaction (OER) is essential to participate in diverse renewable energy conversion and storage processes, while most OER electrocatalysts present satisfactory catalytic performance in only alkaline or acidic medium, limiting their practical applications in many aspects. Herein, we have designed and prepared Ir-CeO 2 -C nanofibers (NFs) via an electrospinning and a relatively low-temperature calcination strategy for OER application in both alkaline and acidic conditions. Density functional theory (DFT) simulations demonstrate the high catalytic active sites of Ir atoms for OER, and that the formation of Ir—O bonds at the interface between Ir and CeO 2 can modulate the electron density of the relevant Ir atoms to promote the OER activity. In addition, the unique nanofibrous heterostructure increases the exposed active sites and promotes the electrical conductivity. Therefore, the prepared Ir-CeO 2 -C nanofibrous catalyst delivers an excellent OER property in both alkaline and acidic solutions. Impressively, the overpotentials to reach 10 mA·cm −2 are only 279 and 283 mV in the alkaline and acidic electrolyte, respectively, with favorable long-term stabilities. In addition, the two-electrode overall water splitting set-ups equipped with Ir-CeO 2 -C NFs as anode and commercial Pt/C as cathode provide a cell voltage of 1.54 and 1.53 V to drive 10 mA·cm −2 in the alkaline and acidic electrolyte, respectively, which are much lower than Pt/C||IrO 2 and lots of transition metal oxides-based electrolyzers. This research presents an efficient means to design OER catalysts with superior properties in both alkaline and acidic solutions.

The arrival of the era of artificial intelligence has promoted the development of smart libraries and endowed them with new features such as autonomy, autonomous learning, context perception, and multi-function. Analyzing how these new features affect users’ behavior is of great significance to the application of artificial intelligence and the optimization of services. Based on the TTF model, the four dimensions of product intelligence (autonomy, adaptability, reactivity, and multifunctionality) are introduced to represent the technology characteristics, and the research model is constructed. And the method of SEM is used to conduct empirical analysis. The results show that technology characteristics and individual characteristics are the main factors affecting the task-technology fit (TTF), while the effect of task characteristics on TTF is not significant. Among them, the impact of technology characteristics is greater than the individual characteristics. The relative importance of the four dimensions of technology characteristics is “Autonomy >Multifunctionality, Reactivity> Adaptability”. The TTF significantly positively affects users’ intention to use smart libraries. Therefore, the suggestions on optimizing smart services are put forward.

The design of a hierarchical heterostructure as a cost-effective and high-efficiency catalyst to realize electronic and interfacial engineering for the oxygen evolution reaction (OER) is a meaningful option in energy storage and conversion. In this work, amorphous NiFeS nanosheets supported on carbon nanofibers embedded with cobalt nanoparticles (Co-C/NiFeS nanofibers) catalysts are fabricated via the electro-spinning-carbonization-electrodeposition strategy. The optimized catalyst possesses a superior OER activity with a low overpotential of 233 mV at 10 mA cm −2 and a Tafel slope of 53.1 mV dec −1 in 1 mol L −1 KOH solution, together with a favorable hydrogen evolution reaction activity. Moreover, an alkaline Pt/C‖Co-C/NiFeS electrolyzer constructed with Co-C/NiFeS nanofibers as the anode and commercial Pt/C as the cathode achieves a low cell voltage of 1.48 V at 10 mA cm −2 , which is superior to those of the benchmark Pt/C‖RuO 2 cell and many other reported electrolyzers. As a bifunctional electrocatalyst, the Co-C/NiFeS‖Co-C/NiFeS electrolyzer can be assembled, exhibiting outstanding long-term stability of 70 h, which significantly outperforms that of the Pt/C‖RuO 2 electrolyzer. The remarkable OER performance of the catalyst benefits from the distinct hierarchical heterostructure with Co-C nanofibers core and amorphous NiFeS nanosheets sheath and the generated highly conductive fibrous carbon substrate, endowing it with a large number of exposed active sites, great electrical conductivity and impregnable structural stability. Thus, this work demonstrates a facile and efficient approach to fabricate non-noble metal-based catalysts with superior electrocatalytic performance for practical energy conversion and storage.

Iridium (Ir)-incorporated cobalt (Co) nanofibers (Co-Ir-600) are fabricated via an electrospinning-calcination- in situ H 2 reduction-galvanic replacement process. Benefitting from the unique one-dimensional nanofibrous heterostructure that allows rapid electron/mass transfer as well as the synergy between Ir and Co components, the Co-Ir catalyst shows a remarkable electrocatalytic activity for oxygen evolution reaction (OER) with an extremely low overpotential of 169 mV at 10 mA cm −2 in an alkaline electrolyte. Furthermore, the catalyst also presents a high hydrogen evolution reaction (HER) performance. Therefore, an alkaline electrolyzer is constructed with the Co-Ir-600 nanofibrous catalyst as both the anode and cathode electrodes, and only a small cell voltage of 1.51 V is needed to achieve 10 mA cm −2 with outstanding durability. This performance is superior to that of benchmark Pt/C∥IrO 2 electrodes and many other reported water electrolysis cells. This study supplies a general and efficient way to prepare cost-effective and high-performance metal-based overall water splitting electrocatalysts.

Land-atmosphere (LA) interactions, through the turbulent exchange of water, heat and CO2 fluxes, strongly influence regional micro-climates, water cycles, energy budgets, and ecosystem dynamics. The Tibetan Plateau (TP), characterized by its vast extent, high elevation, strong solar radiation and extreme weather variability, remains underexplored due to the scarcity of LA observation sites, particularly in its western and northern regions. This study introduces a newly established research and observation platform, comprising 16 planetary boundary layer towers that span diverse landscapes and dynamic meteorological conditions. Across these sites, mean annual air temperature, wind speed, and liquid precipitation range from −3.5 to 18.5 °C, 0.6 to 5.6 m s−1, and 43 to 2164 mm, respectively. Elevation exhibits significant correlations with all meteorological variables, highlighting the pronounced spatial heterogeneity of land–atmosphere coupling across the region. The turbulent fluxes of water and heat exhibit distinct seasonal patterns, with maximum sensible heat flux (SH) in April–May and latent heat flux (LE) in July–August. Most stations act as carbon sinks, with net ecosystem exchange (NEE; the net CO2 exchange between the ecosystem and the atmosphere, where negative values indicate net ecosystem CO2 uptake) ranging from −3.2 to −174.3 gCm-2a-1, except the Medog station, which behaves as a carbon source likely linked to vegetation disturbance and human activity. LE is significantly correlated with SH, NEE and ecosystem respiration, revealing a strong coupling among water, heat and carbon fluxes. This high-resolution, quality-controlled dataset provides critical in situ observations for studying water–heat–carbon coupling, validating models and satellite algorithms, and improving understanding of climate-ecosystem interactions over the TP. The whole datasets are freely available at the National Tibetan Plateau Data Center (https://doi.org/10.11888/Atmos.tpdc.302428; Wang and Ma, 2025).