Mechanochemical route to fabricate an efficient nitrate reduction electrocatalyst

The electrochemical nitrate reduction reaction(NO_(3)RR)to ammonia under ambient conditions is a promising approach for addressing elevated nitrate levels in water bodies,but the progress of this reaction is impeded by the complex series of chemical reactions involving electron and proton transfer and competing hydrogen evolution reaction.Therefore,it becomes imperative to develop an electro-catalyst that exhibits exceptional efficiency and remarkable selectivity for ammonia synthesis while maintaining long-term stability.Herein the magnetic biochar(Fe-C)has been synthesized by a two-step mechanochemical route after a pyrolysis treatment(450,700,and 1000℃),which not only significantly decreases the particle size,but also exposes more oxygen-rich functional groups on the surface,promoting the adsorption of nitrate and water and accelerating electron transfer to convert it into ammonia.Results showed that the catalyst(Fe-C-700)has an impressive NH_(3)production rate of 3.5 mol·h^(−1)·gcat^(−1),high Faradaic efficiency of 88%,and current density of 0.37 A·cm^(−2)at 0.8 V vs.reversible hydrogen electrode(RHE).In-situ Fourier transform infrared spectroscopy(FTIR)is used to investigate the reaction intermediate and to monitor the reaction.The oxygen functionalities on the catalyst surface activate nitrate ions to form various intermediates(NO_(2),NO,NH_(2)OH,and NH_(2))and reduce the rate determining step energy barrier(*NO_(3)→*NO_(2)).This study presents a novel approach for the use of magnetic biochar as an electro-catalyst in NO_(3)RR and opens the road for solving environmental and energy challenges.

Efficient perovskite solar cells based on novel three-dimensional TiO2 network architectures

Mesoscopic lead halide perovskite solar cells typically use TiO2 nanoparticle films as the scaffolds for electron-transport pathway and perovskite deposition. Here, we demonstrate that swelling-induced mesoporous block copolymers can be templates for producing three- dimensional TiO2 networks by combining the atomic layer deposition technique. Thickness adjustable TiO2 network is an excellent alternative scaffold material for efficient per- ovskite solar cells. Our best performing cells using such a 270 nm thick template have achieved a high efficiency of 12.5 % with pristine poly-3-hexylthiophene as a hole transport material. The high performance is attributed to the direct transport pathway and high absorption of scaf- folds, small leakage current and largely reduced recombi- nation rate at interfaces. The results show that TiO2 network architecture is a promising scaffold for meso- scopic perovskite solar cells.

A MULTI-OBJECTIVE ROBUST OPERATION MODEL FOR ELECTRONIC MARKET ENABLED SUPPLY CHAIN WITH UNCERTAIN DEMANDS

A multi-objective robust operation model is proposed in this paper for an electronic market enablexi supply chain consisting of multi-supplier and multi-customer with uncertain demands. Suppliers in this supply chain provide many kinds of products to different customers directly or through electronic market. Uncertain demands are described as a scenario set with certain probability;, the supply chain operation model is constructed by using the robust optimization method based on scenario analyses. The operation model we proposed is a multi-objective programming problem satisfying several conflict objectives, such as meeting the demands of all customers, minimizing the system cost, the availabilities of suppliers＇ capacities not below a certain level, and robustness of decision to uncertain demands. The results of numerical examples proved that the solution of the model is most conservative; however, it can ensure the robustness of the operation of the supply chain effectively.

A fluorine-rich phenolic polyurethane elastomer with excellent selfhealability and reprocessability and its applications for wearable electronics

Polyurethane elastomers with covalent adaptable networks(PU-CANs)based on the dynamic urethane bond have attracted remarkable attention owing to their reprocessability,adaptability,and self-healability.However,it is still a formidable challenge to achieve excellent dynamics at low temperatures since the urethane bond energy is usually high.Herein,a fluorinated phenolic polyurethane(FPPU)elastomer with CANs based on phenol±carbamate bonds was successfully designed and prepared.Subsequently,the effects of fluorine atoms on the mechanical properties,thermal stability,reprocessability,and self-healability,surface free energy,and hydrophobicity of the prepared elastomers were systematically investigated.The FPPU elastomer showed notch-insensitivity,remarkable self-healable efficiency(98%),low dynamic dissociation temperature(60℃),excellent reprocessability,and low surface energy(62 MJ m^(-2))compared with non-fluorinated counterpart phenolic polyurethane elastomer(APPU).Based on the above-mentioned features,FPPU was selected as an elastic substrate to assemble into a triboelectric nanogenerator(TENG)to harvest energy from natural motion.This TENG exhibited an excellent electrical output performance with a peak open-circuit voltage of 103 V,a peak short-circuit current of 4.7μA and a peak short-circuit charge of 43 nC.In addition,the TENG possessed high selfcleaning and reprocessing efficiency.Furthermore,a stretchable and self-healing composite conductor based on FPPU was fabricated for flexible electronic devices.

Item-level Forecasting for E-commerce Demand with High-dimensional Data Using a Two-stage Feature Selection Algorithm

With the rapid development of information technology and fast growth of Internet users,e-commerce nowadays is facing complex business environment and accumulating large-volume and highdimensional data.This brings two challenges for demand forecasting.First,e-merchants need to find appropriate approaches to leverage the large amount of data and extract forecast features to capture various factors affecting the demand.Second,they need to efficiently identify the most important features to improve the forecast accuracy and better understand the key drivers for demand changes.To solve these challenges,this study conducts a multi-dimensional feature engineering by constructing five feature categories including historical demand,price,page view,reviews,and competition for e-commerce demand forecasting on item-level.We then propose a two-stage random forest-based feature selection algorithm to effectively identify the important features from the high-dimensional feature set and avoid overfittlng.We test our proposed algorithm with a large-scale dataset from the largest e-commerce platform in China.The numerical results from 21,111 items and 109 million sales observations show that our proposed random forest-based forecasting framework with a two-stage feature selection algorithm delivers 11.58%,5.81%and 3.68%forecast accuracy improvement,compared with the Autoregressive Integrated Moving Average(ARIMA),Random Forecast,and Random Forecast with one-stage feature selection approach,respectively,which are widely used in literature and industry.This study provides a useful tool for the practitioners to forecast demands and sheds lights on the B2C e-commerce operations management.

Photo-responsive shape memory polymer composites enabled by doping with biomass-derived carbon nanomaterials

As photothermal conversion agents,carbon nanomaterials are widely applied in polymers for light-triggered shape memory behaviors on account of their excellent light absorption.However,they are usually derived from non-renewable fossil resources,which go against the demand for sustainable development.Biomass-derived carbon nanomaterials are expected as alternatives if they are designed with good dispersibility as well as splendid photothermal properties.Up to date,very few researches focused on this area.Herein,we report a novel light-triggered shape memory composite by incorporating renewable biomass-derived carbon nanomaterials into acrylate polymers without deep purification and processing.These functionalized carbon nanomaterials not only have stable dispersion in polymers as fillers,but also can endow the polymers with excellent and stable thermal and photothermal responsive properties in biological friendly environment.With the introduction of biomass-derived carbon nanomaterials,the mechanical properties of the composites are also further enhanced with the formation of hydrogen bonding between the carbon nanomaterials and the polymers.Notably,the doping of 1%carbon nanomaterials endows the polymer with sufficient hydrogen bonds that not only exhibit excellent thermal and photothermal responsive properties,but also with enough space for the motion of chains.These properties make such composite a promising and safe candidate for shape memory applications,which provide a new avenue in smart fabrics or intelligent soft robotics.

Fast Evaporation Enabled Ultrathin Polymer Coatings on Nanoporous Substrates for Highly Permeable Membranes

Thin polymer coatings covering on porous substrates are a common composite structure required in numerous applications,including membrane separation,and there is a strong need to push the coating thicknesses down to the nanometer scale to maximize the performances.However,producing such ultrathin polymer coatings in a facile and efficient way remains a big challenge.Here,uniform ultrathin polymer covering films(UPCFs)are realized by a facile and general approach based on rapid solvent evaporation.By fast evaporating dilute polymer solutions spread on the surface of porous substrates,we obtain ultrathin coatings(down to
30 nm)exclusively on the top surface of porous substrates,forming UPCFs with a block copolymer of polystyrene-blockpoly(2-vinyl pyridine)at room temperature or a homopolymer of poly(vinyl alcohol)(PVA)at elevated temperatures.Upon selective swelling of the block copolymer and crosslinking of PVA,we obtain highly permeable membranes delivering
2–10 times higher permeance in ultrafiltration and pervaporation than state-of-the-art membranes with comparable selectivities.We have invented a very convenient but highly efficient process for the direct preparation of defective-free ultrathin coatings on porous substrates,which is extremely desired in different fields in addition to membrane separation.