Amine Coordinated Electron-Rich Palladium Nanoparticles for Electrochemical Hydrogenation of Benzaldehyde

Electrocatalytic hydrogenation (ECH) is a burgeoning strategy for the sustainable utilization of hydrogen. However, how to effectively suppress the competitive hydrogen evolution reaction (HER) is a big challenge to ECH catalysis. In this study, amine (NH₂ R)-coordinated Pd nanoparticles loaded on carbon felt (Pd@CF) as a catalyst is successfully synthesized by a one-step solvothermal reduction method using oleylamine as the reducing agent. An exceptional ECH reactivity on benzaldehyde is achieved on the optimal Pd@CF catalyst in terms of a high conversion (89.7%) and selectivity toward benzyl alcohol (89.8%) at −0.4 V in 60 min. Notably, the Faradaic efficiency for producing benzyl alcohol is up to 90.2%, much higher than that catalyzed by Pd@CF-without N-group (41.1%) and thecommercial Pd/C (20.9%). The excellent ECH performance of Pd@CF can be attributed to the enriched electrons on Pd surface resulted from the introduction of NH₂ R groups, which strengthens both the adsorption of benzaldehyde and the adsorbed hydrogen (H_ads) on Pd, preventing the combination of H_ads to form H₂, that is, inhibiting the HER. This study gives a new insight into design principles of highly efficient electrocatalysts for the hydrogenation of unsaturated aldehydes molecules.     

Ultrafast Switchable Passive Radiative Cooling Smart Windows with Synergistic Optical Modulation

Switchable passive radiative cooling (PRC) smart windows can modulate sunlight transmission and spontaneously emit heat to outer space through atmospheric transparent window, presenting great potential in building energy conservation. However, realizing stable and on-demand control of the cooling efficiency for PRC materials is still challenging. Herein, an electro-controlled polymer-dispersed liquid crystal (PDLC) smart window showing PRC property is designed and prepared by adding mid-infrared emitting reactive monomers into the conventional PDLC matrix. It is found that not only the electro-optical properties but also the PRC efficiency of PRC PDLC film are tunable by regulating the content of the mid-infrared emitting components, film thickness, and micromorphology. This advanced PRC PDLC material achieves a near/sub-ambient temperature when the solar irradiance is below 400 W m⁻² and can dynamically manage daytime cooling efficiency. Importantly, its PRC efficiency is capable of being tuned in an on-demand and ultrafast millisecond-scale way, whose controllable transparency enables multistage heat regulation. This study is hoped to provide new inspiration in the preparation of advanced optical devices and energy-efficient equipment.     

In Situ Polymerizing Internal Encapsulation Strategy Enables Stable Perovskite Solar Cells toward Lead Leakage Suppression

Despite the outstanding power conversion efficiency (PCE) of perovskite solar cells (PSCs) achieved over the years, unsatisfactory stability and lead toxicity remain obstacles that limit their competitiveness and large-scale practical deployment. In this study, in situ polymerizing internal encapsulation (IPIE) is developed as a holistic approach to overcome these challenges. The uniform polymer internal package layer constructed by thermally triggered cross-linkable monomers not only solidifies the ionic perovskite crystalline by strong electron-withdrawing/donating chemical sites, but also acts as a water penetration and ion migration barrier to prolong shelf life under harsh environments. The optimized MAPbI₃ and FAPbI₃ devices with IPIE treatment yield impressive efficiencies of 22.29% and 24.12%, respectively, accompanied by remarkably enhanced environmental and mechanical stabilities. In addition, toxic water-soluble lead leakage is minimized by the synergetic effect of the physical encapsulation wall and chemical chelation conferred by the IPIE. Hence, this strategy provides a feasible route for preparing efficient, stable, and eco-friendly PSCs.     

Lightweight deep learning methods for panoramic dental X-ray image segmentation

Neural Computing and Applications - Dental X-ray image segmentation is helpful for assisting clinicians to examine tooth conditions and identify dental diseases. Fast and lightweight segmentation...     

A 2.20 eV Bandgap Polymer Donor for Efficient Colorful Semitransparent Organic Solar Cells

Semitransparent organic solar cells (ST-OSCs) have attracted increasing attention due to their promising prospect in building-integrated photovoltaics. Generally, efficient ST-OSCs with good average visible transmittance (AVT) can be realized by developing active layer materials with light absorption far from the visible light range. Herein, the development of ultrawide bandgap polymer donors with near-ultraviolet absorption, paired with near-infrared acceptors, is proposed to achieve high-performance ST-OSCs. The key points for the design of ultrawide bandgap polymers include constructing donor–donor type conjugated skeleton, suppressing the quinoidal resonance effect, and minimizing the twist of conjugated skeleton via noncovalent conformational locks. As a proof of concept, a polymer named PBOF with an optical bandgap of 2.20 eV is synthesized, which exhibited largely reduced overlap with the human eye photopic response spectrum and afforded a power conversion efficiency (PCE) of 16.40% in opaque device. As a result, ST-OSCs with a PCE over 10% and an AVT over 30% are achieved without optical modulation. Moreover, colorful ST-OSCs with visual aesthetics can be achieved by tuning the donor/acceptor weight ratio in active layer benefiting from the ultrawide bandgap nature of PBOF. This study demonstrates the great potential of ultrawide bandgap polymers for efficient colorful ST-OSCs.     

Study on Recognition Method of Similar Weather Scenes in Terminal Area

Weather is a key factor affecting the control of air traffic. Accurate recognition and classification of similar weather scenes in the terminal area is helpful for rapid decision-making in air traffic flow management. Current researches mostly use traditional machine learning methods to extract features of weather scenes, and clustering algorithms to divide similar scenes. Inspired by the excellent performance of deep learning in image recognition, this paper proposes a terminal area similar weather scene classification method based on improved deep convolution embedded clustering (IDCEC), which uses the combination of the encoding layer and the decoding layer to reduce the dimensionality of the weather image, retaining useful information to the greatest extent, and then uses the combination of the pre-trained encoding layer and the clustering layer to train the clustering model of the similar scenes in the terminal area. Finally, terminal area of Guangzhou Airport is selected as the research object, the method proposed in this article is used to classify historical weather data in similar scenes, and the performance is compared with other state-of-the-art methods. The experimental results show that the proposed IDCEC method can identify similar scenes more accurately based on the spatial distribution characteristics and severity of weather; at the same time, compared with the actual flight volume in the Guangzhou terminal area, IDCEC's recognition results of similar weather scenes are consistent with the recognition of experts in the field.     

Semi-supervised transformable architecture search for feature distillation

Pattern Analysis and Applications - The designed method aims to perform image classification tasks efficiently and accurately. Different from the traditional CNN-based image classification methods,...     

Graph-based fine-grained model selection for multi-source domain

Pattern Analysis and Applications - The prosperity of datasets and model architectures has led to the development of pretrained source models, which simplified the learning process in multi-domain...