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The design of efficient and robust non-precious metal electrocatalysts towards oxygen evolution reaction (OER) is of great value for developing green energy technologies. The in-situ formed high-valence (oxy)hydroxides species during the reconstruction process of pre-catalysts are recognized as the real contributing sites for OER. However, pre-catalysts generally undergo a slow and inadequate self-reconstruction. Herein, we reported a PO43− optimized CoFe-based OER catalysts with amorphous structure, which enables a fast and deep reconstruction during the OER process. The amorphous structure induced by ligands PO43− is prone to evolution and further form active species for OER. The electron interaction between metal sites can be modulated by electron-rich PO43−, which promotes generation of high active CoOOH. Simultaneously, the etching of PO43− from the pre-catalysts during the catalytic process is in favor of accelerating the self-reconstruction. As a result, as-prepared pre-catalyst can generate high active CoOOH at a low potential of 1.4 V and achieve an in-depth reconstructed nanosheet structure with abundant OER active sites. Our work provides a promising design of pre-catalysts for realizing efficient catalysis of water oxidation.

The ecosystems and human health were seriously threatened by hexavalent chromium (Cr(VI)) in wastewater. In this article, using the idea of the highly matched energy band structure between indium sulfide (In2S3) and MIL-53(Fe), a Type-II heterojunction has been constructed by loading In2S3 on MIL-53(Fe) microrod to overcome the fault like high recombination rates of photogenerated electron-holes of In2S3. The composite with 20:1 mass ratio of In2S3 to MIL-53(Fe) (IM-2) was adopted as an optimal sample for efficient photocatalytic Cr(VI) reduction under visible light. Various characterization techniques were used to verify the characteristics of composites and delved into the structure-effect relationship between this heterojunction and its activity. Results showed that the reaction rate constants of the photoreduction process over IM-2 was ~ 4 and 26 times higher than those of pure In2S3 and MIL-53(Fe), respectively, and the catalyst could maintain superior removal efficiency (88.6%) and steady crystal structure after four cycles. First-principles calculations further illustrated that the heterostructure formed between In2S3 and MIL-53(Fe) could effectively accelerate the separation of photogenerated electrons and holes, thus improving the photocatalytic reduction performance. Moreover, the active species analyses revealed that the superoxide radicals and electrons were mainly involved in the reduction of Cr(VI).

Axillary strategy decisions have become more complex and controversial in considering minimally traumatic therapy instead of sentinel lymph node biopsy, axillary lymph node dissection or regional nodal irradiation for people with breast cancer. The purpose of this study was to noninvasively predict sentinel lymph node (SLN) and non-sentinel lymph node (NSLN) status based on pre-operative sonographic and clinicopathologic features to determine optimal decisions regarding axillary therapy. In total, 701 patients with breast cancer from two independent centers were retrospectively analyzed. The SLN model (SLNM) for predicting SLN status and the NSLN model (NSLNM) for predicting NSLN status were trained based on a training set using the random-forest algorithm, and their performance was validated using an independent external test set. A receiver operating characteristic curve was drawn to obtain the area under the curve, which was used to assess performance. The area under the curve for the SLNM in the training and test, respectively, was 94.2% and 83.0%, and for the NSLNM, 99.5% and 92.7%. The SLNM and NSLNM accurately predicted that 61.46% (319/519) and 17.53% (91/519), respectively, of our participants were non-metastatic. The overall benefit of the three models was 78.99% in our participants. The two models for predicting SLN and NSLN status showed excellent application potential in optimizing axillary strategies.

Adversarial examples can easily fool existing powerful deep neural networks. However, we find that the attack ability of most existing adversarial attack methods is significantly degraded once the generated adversarial examples are shown on screen devices and are further captured. This is mainly attributed to two challenges: (1) Extra noises and variance during the capturing process, such as lens distortion and diverse capturing angle. (2) They get stuck in a self-contradictory problem between visual quality and attack ability. Inspired by the properties of the human visual system (HVS), this paper dedicatedly designs the first color decomposition-based adversarial example method CDAE for screen devices. Specifically, it decomposes one regular screen frame into two symmetric adversarial frames with maximum modifications while theoretically guaranteeing the visual quality perceived by human observers. Thanks to the powerful generalization ability of the proposed method, we can combine it with most adversarial example generation methods and achieve state-of-the-art attack ability. Additionally, it can also be used to protect important information from leakage and attack existing video action recognition networks .

Control of production operations is one of the most cost-effective methods to improve energy efficiency in production lines. Although many research efforts have been devoted to utilizing production control to boost production energy efficiency, it is not clear how production dynamics change in reaction to the energy saving operations, which is important to capture energy saving opportunities. In this paper, an optimal energy saving control method based on N-policy is introduced to reduce the system energy consumption while only slightly decreasing productivity. A Markov chain model is established to interpret the dynamics of serial production lines with N-policy. The analytical formulas of production rate and energy consumption are derived. Then, an energy-efficient control model is formulated to balance the productivity and energy consumption. For two-machine production lines, the monotonicity property of the model is proved, and the analytical N-policy is derived. For longer production lines, an adaptive genetic algorithm is utilized to solve the energy-efficient control model. Case studies validate the effectiveness of the proposed optimal energy saving control method.

The exact control of pore structure in carbon is desirable for offering a leap forward in a wide range of industrial applications, but the intractable hurdle relating to the anisotropic etching of carbon remains. Here, metal nano-drills are developed by which controllable pore size and adjustable hole depth can be rationally designed in both SP2- and SP2-SP3 hybridized carbon. A new catalytic drilling behavior is demonstrated, whereby the catalytic metal nanoparticles (Ru, Ni, Au, Pt, etc.) as drilling forces, accompanied by chemical etching, thermodynamically catalyze the decomposition of the surface barrier layer (K2CO3) and dynamically accelerate the anisotropic carbon etching rates. Subsequently, metal nano-drills open the enclosed internal spaces of activation carbonized microtubes (RCMT). Benefiting from the drilling structure featured by outer-inter connecting, RCMT exhibits 2.5 times higher Li2O2 accommodation capacity as the lithium-oxygen battery cathode and 2.8 times higher specific capacitance as the electric double-layer supercapacitor electrode than the carbonized microtube without the drilling process.