Enhanced Crystallinity of Triple-Cation Perovskite Film via Doping NH\u3csub\u3e4\u3c/sub\u3eSCN

The trap-state density in perovskite films largely determines the photovoltaic performance of perovskite solar cells (PSCs). Increasing the crystal grain size in perovskite films is an effective method to reduce the trap-state density. Here, we have added NH4SCN into perovskite precursor solution to obtain perovskite films with an increased crystal grain size. The perovskite with increased crystal grain size shows a much lower trap-state density compared with reference perovskite films, resulting in an improved photovoltaic performance in PSCs. The champion photovoltaic device has achieved a power conversion efficiency of 19.36%. The proposed method may also impact other optoelectronic devices based on perovskite films

Pretrained Language Model based Web Search Ranking: From Relevance to Satisfaction

Search engine plays a crucial role in satisfying users' diverse information needs. Recently, Pretrained Language Models (PLMs) based text ranking models have achieved huge success in web search. However, many state-of-the-art text ranking approaches only focus on core relevance while ignoring other dimensions that contribute to user satisfaction, e.g., document quality, recency, authority, etc. In this work, we focus on ranking user satisfaction rather than relevance in web search, and propose a PLM-based framework, namely SAT-Ranker, which comprehensively models different dimensions of user satisfaction in a unified manner. In particular, we leverage the capacities of PLMs on both textual and numerical inputs, and apply a multi-field input that modularizes each dimension of user satisfaction as an input field. Overall, SAT-Ranker is an effective, extensible, and data-centric framework that has huge potential for industrial applications. On rigorous offline and online experiments, SAT-Ranker obtains remarkable gains on various evaluation sets targeting different dimensions of user satisfaction. It is now fully deployed online to improve the usability of our search engine

Formation of PbSe/CdSe Core/Shell Nanocrystals for Stable Near-Infrared High Photoluminescence Emission

PbSe/CdSe core/shell nanocrystals with quantum yield of 70% were obtained by the “successive ion layer adsorption and reaction” technology in solution. The thickness of the CdSe shell was exactly controlled. A series of spectral red shifts with the CdSe shell growth were observed, which was attributed to the combined effect of the surface polarization and the expansion of carriers’ wavefunctions. The stability of PbSe nanocrystals was tremendously improved with CdSe shells

A parametric study of adhesive bonded joints with composite material using black-box and grey-box machine learning methods:Deep neuron networks and genetic programming

The aerospace, automotive and marine industries have witnessed a rapid increase of using adhesive bonded joints due to their advantages in joining dissimilar and/or new engineering materials. Joint strength is the key property in evaluating the capability of the adhesive joint. In this paper, developments of black-box and grey-box machine learning (ML) models are presented to allow accurate predictions of the failure load of single lap joints by considering a mix of continuous and discrete design (geometry and material) variables. Firstly, the failure loads of 300 single lap joint samples with different geometry/material parameters are calculated by FE models to generate a data set of which accuracy is validated by experimental results. Then, a deep neuron network (black-box) and a genetic programming (grey-box) model are developed for accurately predicting the failure load of the joint. Based on both ML models, a case study is conducted to explore the relationships between specific design variables and overall mechanical performances of the single lap adhesive joint, and optimal designs of structure and material can be obtained

Understanding mixed mode ratio of adhesively bonded joints using genetic programming (GP)

Adhesively bonding has been increasingly used for numerous industrial applications to meet the high demand for lightweight and safer structures. Debonding of adhesively bonded joints is a typical mixed mode failure process. It is highly depended on the interactional effects of material properties and geometric definitions of the constituents, which is very complicated. The existing studies in identifying fracture modes of joints based on either experiments or finite element analysis are often prohibitively time and computational expensive. This paper proposed an innovate method by combining Finite Element Analysis (FEA), Latin Hypercube Sampling (LHS) and Genetic Programming (GP) to understand the effect of the physical attributes on the fracture modes of adhesively single lap joints. A dataset of 150 adhesive joint samples has been generated using LHS, including different combinations of adherend and adhesive’s material properties and thicknesses. The mixed mode ratios of the 150 samples are calculated using Strain Energy Release Rate (SERR) outputs embedded in Linear Elastic Fracture Mechanics (LEFM), which has been validated by experimental tests. Finally, a GP model is developed and trained to provide an extracted explicit expression used for evaluating the early-state failure modes of the adhesively bonded joints against the design variables

Design optimisation of braided composite beams for lightweight rail structures using machine learning methods

Braided composites have seen substantial industrial uptake for structural applications in the past decade. The dependence of their properties on braid angle provides opportunities for lightweighting through structure-specific optimisation. This paper presents an integrated approach, combining finite element (FE) simulations and a genetic algorithm (GA) to optimise braided beam structures in the spaceframe chassis of a rail vehicle. The braid angle and number of layers for each beam were considered as design variables. A set of 200 combinations of these variables were identified using a sampling strategy for FE simulations. The results were utilised to develop a surrogate model using genetic programming (GP) to correlate the design variables with structural mass and FE-predicted chassis displacements under standard loads. The surrogate model was then used to optimise the design variables using GA to minimise mass without compromising mechanical performance. The optimised design rendered approximately 15.7% weight saving compared to benchmark design

Hybrid hydrovoltaic electricity generation driven by water evaporation

Water evaporation is a ubiquitous natural process exploiting thermal energy from ambient environment. Hydrovoltaic technologies emerged in recent years offer one prospective route to generate electricity from water evaporation, which has long been overlooked. Herein, we developed a hybrid hydrovoltaic generator driven by natural water evaporation, integrating an “evaporation motor” with an evaporation-electricity device and a droplet-electricity device. A rotary motion of the “evaporation motor” relies on phase change of ethanol driven by water-evaporation induced temperature gradient. This motion enables the evaporation-electricity device to work under a beneficial water-film operation mode to produce output of ~4 V and ~0.2 µA, as well as propels the droplet-electricity device to convert mechanical energy into pulsed output of ~100 V and ~0.2 mA. As different types of hydrovoltaic devices require distinctive stimuli, it was challenging to make them work simultaneously, especially under one single driving force. We here for the first time empower two types of hydrovoltaic devices solely by omnipresent water evaporation. Therefore, this work presents a new pathway to exploiting water evaporation-associated ambient thermal energy and provides insights on developing hybrid hydrovoltaic generators

Novel Reversible-Binding PET Ligands for Imaging Monoacylglycerol Lipase Based on the Piperazinyl Azetidine Scaffold

Monoacylglycerol lipase (MAGL) is a 33 kDa serine protease primarily responsible for hydrolyzing 2-arachidonoylglycerol into the proinflammatory eicosanoid precursor arachidonic acid in the central nervous system. Inhibition of MAGL constitutes an attractive therapeutic concept for treating psychiatric disorders and neurodegenerative diseases. Herein, we present the design and synthesis of multiple reversible MAGL inhibitor candidates based on a piperazinyl azetidine scaffold. Compounds 10 and 15 were identified as the best-performing reversible MAGL inhibitors by pharmacological evaluations, thus channeling their radiolabeling with fluorine-18 in high radiochemical yields and favorable molar activity. Furthermore, evaluation of [18F]10 and [18F]15 ([18F]MAGL-2102) by autoradiography and positron emission tomography (PET) imaging in rodents and nonhuman primates demonstrated favorable brain uptakes, heterogeneous radioactivity distribution, good specific binding, and adequate brain kinetics, and [18F]15 demonstrated a better performance. In conclusion, [18F]15 was found to be a suitable PET radioligand for the visualization of MAGL, harboring potential for the successful translation into humans