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Triggering the lattice oxygen oxidation mechanism is crucial for improving oxygen evolution reaction (OER) performance, because it could bypass the scaling relation limitation associated with the conventional adsorbate evolution mechanism through the direct formation of oxygen–oxygen bond. High‐valence transition metal sites are favorable for activating the lattice oxygen, but the deep oxidation of pre‐catalysts suffers from a high thermodynamic barrier. Here, taking advantage of the Jahn–Teller (J–T) distortion induced structural instability, we incorporate high‐spin Mn3+ (t2g3eg1 ${{t}_{2g}^{3}{e}_{g}^{1}}$) dopant into Co4N. Mn dopants enable a surface structural transformation from Co4N to CoOOH, and finally to CoO2, as observed by various in situ spectroscopic investigations. Furthermore, the reconstructed surface on Mn‐doped Co4N triggers the lattice oxygen activation, as evidenced experimentally by pH‐dependent OER, tetramethylammonium cation adsorption and online electrochemical mass spectrometry measurements of 18O‐labelled catalysts. In general, this work not only offers the introducing J–T effect approach to regulate the structural transition, but also provides an understanding about the influence of the catalyst's electronic configuration on determining the reaction route, which may inspire the design of more efficient catalysts with activated lattice oxygen. [ABSTRACT FROM AUTHOR]

There are various indoor fingerprint localization techniques utilizing the similarity of received signal strength (RSS) to discriminate the similarity of positions. However, due to the varied states of different wireless access points (APs), each AP's contribution to RSS similarity varies, which affects the accuracy of localization. In our study, we analyzed several critical causes that affect APs' contribution, including APs' health states and APs' positions. Inspired by these insights, for a large-scale indoor space with ubiquitous APs, a threshold was set for all sample RSS to eliminate the abnormal APs dynamically, a correction quantity for each RSS was provided by the distance between the AP and the sample position to emphasize closer APs, and a priority weight was designed by RSS differences (RSSD) to further optimize the capability of fingerprint distances (FDs, the Euclidean distance of RSS) to discriminate physical distance (PDs, the Euclidean distance of positions). Integrating the above policies for the classical WKNN algorithm, a new indoor fingerprint localization technique is redefined, referred to as FDs' discrimination capability improvement WKNN (FDDC-WKNN). Our simulation results showed that the correlation and consistency between FDs and PDs are well improved, with the strong correlation increasing from 0 to 76% and the high consistency increasing from 26% to 99%, which confirms that the proposed policies can greatly enhance the discrimination capabilities of RSS similarity. We also found that abnormal APs can cause significant impact on FDs discrimination capability. Further, by implementing the FDDC-WKNN algorithm in experiments, we obtained the optimal K value in both the simulation scene and real library scene, under which the mean errors have been reduced from 2.2732 m to 1.2290 m and from 4.0489 m to 2.4320 m, respectively. In addition, compared to not using the FDDC-WKNN, the cumulative distribution function (CDF) of the localization errors curve converged faster and the error fluctuation was smaller, which demonstrates the FDDC-WKNN having stronger robustness and more stable localization performance. [ABSTRACT FROM AUTHOR]

The separation of CO activation and C─C coupling enables oxide–zeolite catalyzed tandem reaction toward direct conversion of syngas into light olefins to be a promising alternative for the traditional Fischer–Tropsch process with elevated selectivity. Utilizing controlled mass transfer to match the activation of reactants and the coupling of intermediates presents an appealing strategy to break the limitation on space‐time yield in this tandem process. In this work, ZnCrOx hollow multi‐shelled structures (HoMS) are fabricated to enhance the adsorption and enrichment ability of syngas, thereby strengthening the rate‐determining activation step of reactants and resulting in an accelerated generation of intermediates. When coupled with SAPO zeolite, quintuple‐shelled ZnCrOx HoMS‐based catalysts exhibit a record high C2= ‐C4= space‐time yield of 559 mg·gcat−1·h−1 with a selectivity of 90%. This work demonstrates the advantages of precise nanostructure sculpturing on the mass transfer behavior in the microenvironment and provides a new strategy for highly selective direct conversion of syngas to light olefins, based on efficient activation of reactants and sufficient feeding of intermediates through enrichment effects within HoMS. [ABSTRACT FROM AUTHOR]

Tuning the electrocatalytic selectivity and long‐term stability for industrially significant, yet reactively unfavorable products, remains a challenge in oxygen reduction. Herein, the Au/TiO2 catalysts with strong metal–support interactions (SMSI) are designed and synthesized through an in situ auto‐reduction of Au‐modified Ti‐MOF. Highly dispersed ultra‐low loading of Au NPs strongly capsulated in porous TiO2 substrate, together with conductive carbon derivated from MOFs, enables Au/TiO2 electrocatalysts to achieve excellent H2O2 electrosynthesis through the two‐electron oxygen reduction reaction (2e ORR). Au(1.0)/TiO2 (0.52 wt% Au loading) exhibited a high selectivity of 90%, a remarkable Faradaic efficiency of 98%, and 72.2 mg L−1 h−1 H2O2 production. Highly dispersed Au NPs promote active site exposure, while the conductive carbon and the porous superstructure enhance mass diffusion. Notably, the SMSI between Au NPs and TiO2 substrate leads to superb stability (over 168 h) of the electrocatalysts, as it ensures continuous electronic interactions. Experimental characterizations and density functional theory (DFT) simulations further revealed that the SMSI effect medicates the activation of the *OOH intermediate formation and the d‐band center, which are conductive to 2e ORR. Moreover, this strategy provides a potential way toward high‐performance electrocatalysts in flow system devices for continuously purifying sewage and chemical bleaching in extreme environments. [ABSTRACT FROM AUTHOR]

N‐doping is an effective technique for enhancing the exploitation of TiO2 under visible light, thanks to the level of doping introduced. It is also important to explore N‐doping in the metastable polymorph TiO2(B), which is renowned for its applications in energy materials. In order to investigate the impact of N‐doping on the optical properties of TiO2(B), a systematic comparison of the electronic structural and optical properties of pure and N‐doped TiO2(B) was conducted using density function theory (DFT) calculations. The results indicate that N‐doping is more thermodynamically favorable at the O site with four coordinated atoms. Upon N‐doping, impurity states emerged within the bandgap of TiO2(B), leading to a significant reduction in the energy gap. Consequently, N‐doping primarily enhances the absorbance of visible light, which is crucial for photocatalysis. Furthermore, the adsorption energy of H at the (0 0 1) surface of N‐doped TiO2(B) decreased by 2.75 eV, providing valuable insight for the design of TiO2(B) with exceptional photo‐ and electro‐catalytic performance. [ABSTRACT FROM AUTHOR]

Photocatalytic CO2 reduction holds great promise to solve energy shortage and global warming. Nb2O5 has demonstrated superiority in CO2 activation and selectivity regulation, but suffers from poor light absorption ability and fast carrier recombination. To tackle these problems, we designed and synthesized ultrathin Nb2O5 nanosheets which were decorated with tiny NiP nanoparticles as cocatalyst. In this way, light absorption range broadened, carrier migration distance reduced, charge transfer resistance decreased, and surface reaction kinetics promoted. Consequently, the as‐prepared NiP/Nb2O5 showed notably enhanced activity (148.7 μmol ⋅ g−1) and selectivity (82.1 %) towards CO, as well as remarkable stability. This work paves the way for cheap and earth abundant alternatives as cocatalysts and deepens the understanding of their functions in CO2 photoreduction. [ABSTRACT FROM AUTHOR]

Recently, the development of new materials and devices has become the main research focus in the field of energy. Supercapacitors (SCs) have attracted significant attention due to their high power density, fast charge/discharge rate, and excellent cycling stability. With a lamellar structure, 2D transition metal dichalcogenides (2D TMDs) emerge as electrode materials for SCs. Although many 2D TMDs with excellent energy storage capability have been reported, further optimization of electrode materials and devices is still needed for competitive electrochemical performance. Previous reviews have focused on the performance of 2D TMDs as electrode materials in SCs, especially on their modification. Herein, the effects of element doping, morphology, structure and phase, composite, hybrid configuration, and electrolyte are emphatically discussed on the overall performance of 2D TMDs‐based SCs from the perspective of device optimization. Finally, the opportunities and challenges of 2D TMDs‐based SCs in the field are highlighted, and personal perspectives on methods and ideas for high‐performance energy storage devices are provided. [ABSTRACT FROM AUTHOR]

Forest fire risk prediction is essential for building a forest fire defense system. Ensemble learning methods can avoid the problem of difficult model selection for disaster susceptibility prediction and can significantly improve modeling accuracy. This study introduces a stacking ensemble learning model for predicting forest fire risks in Yunnan Province by integrating various data types, such as meteorological, topographic, vegetation, and human activity factors. A total of 70,274 fire points and an equal number of randomly selected nonfire points were used to develop the model, with 70% of the data allocated for training and the remaining 30% for testing. The stacking model combined four diverse machine learning methods: random forest (RF), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and multilayer perceptron (MLP). We evaluated the model's predictive performance using metrics like accuracy, area under the characteristic curve (AUC), and fire density (FD). The results demonstrated that the stacking fusion model exhibited remarkable accuracy with an AUC of 0.970 on the test set, significantly surpassing the performance of individual machine learning models, which had AUC values ranging from 0.935 to 0.953. Furthermore, the stacking fusion model effectively captured the maximum fire density in extremely high susceptibility areas, demonstrating enhanced generalization capabilities. [ABSTRACT FROM AUTHOR]