INSULATING TUNNELING CONTACT FOR EFFICIENT AND STABLE PEROVSKITE SOLAR CELLS

Perovskite-based photoactive devices, such as solar cells, include an insulating tunneling layer inserted between the perovskite photoactive material and the electron collection layer to reduce charge recombination and concomitantly provide water resistant properties to the device

Self-Supervised Multi-Modal Sequential Recommendation

With the increasing development of e-commerce and online services, personalized recommendation systems have become crucial for enhancing user satisfaction and driving business revenue. Traditional sequential recommendation methods that rely on explicit item IDs encounter challenges in handling item cold start and domain transfer problems. Recent approaches have attempted to use modal features associated with items as a replacement for item IDs, enabling the transfer of learned knowledge across different datasets. However, these methods typically calculate the correlation between the model's output and item embeddings, which may suffer from inconsistencies between high-level feature vectors and low-level feature embeddings, thereby hindering further model learning. To address this issue, we propose a dual-tower retrieval architecture for sequence recommendation. In this architecture, the predicted embedding from the user encoder is used to retrieve the generated embedding from the item encoder, thereby alleviating the issue of inconsistent feature levels. Moreover, in order to further improve the retrieval performance of the model, we also propose a self-supervised multi-modal pretraining method inspired by the consistency property of contrastive learning. This pretraining method enables the model to align various feature combinations of items, thereby effectively generalizing to diverse datasets with different item features. We evaluate the proposed method on five publicly available datasets and conduct extensive experiments. The results demonstrate significant performance improvement of our method

Study Design and Data Analysis of Artificial Pancreas Device Systems with Closed-Loop Glucose-Sensing Insulin Delivery

Objective: The objective of this article is to provide a high-profile review and discussion on the study design and statistical analysis of pivotal clinical trials conducted to demonstrate the safety and effectiveness of closed-loop investigational artificial pancreas device systems (APDSs) in premarket approval applications. Methods: The United States Food and Drug Administration (FDA) guidance on the content of investigational device exemption and premarket approval applications for APDSs is reviewed with special emphasis on study design and statistical analysis of the pivotal clinical trials. The two pivotal studies for the MiniMed 670G hybrid closed-loop system by Medtronic in their premarket approval application are summarized and discussed. Results: The United States FDA established detailed recommendations on the study design and statistical analysis of pivotal clinical trials for the industry that seek market investigational APDSs and for FDA scientific reviewers that regulate the device applications. The recommendations cover specifics regarding patient population, clinical endpoints, and strategies for data analysis. However, the two pivotal studies that demonstrated the effectiveness of the FDA-approved MiniMed 670G hybrid closed-loop system were not typical randomized controlled trials as per FDA recommendations. Conclusion: The development and regulation of investigational APDSs require careful and sophisticated clinical study designs and data analysis in premarket approval applications. The regulatory evaluation process of the APDSs is rather complicated since the devices consist of multiple components that collaboratively function to mimic human pancreases

WizardLM: Empowering Large Language Models to Follow Complex Instructions

Training large language models (LLM) with open-domain instruction following data brings colossal success. However, manually creating such instruction data is very time-consuming and labor-intensive. Moreover, humans may struggle to produce high-complexity instructions. In this paper, we show an avenue for creating large amounts of instruction data with varying levels of complexity using LLM instead of humans. Starting with an initial set of instructions, we use our proposed Evol-Instruct to rewrite them step by step into more complex instructions. Then, we mix all generated instruction data to fine-tune LLaMA. We call the resulting model WizardLM. Human evaluations on a complexity-balanced test bed show that instructions from Evol-Instruct are superior to human-created ones. By analyzing the human evaluation results of the high complexity part, we demonstrate that outputs from our WizardLM model are preferred to outputs from OpenAI ChatGPT. Even though WizardLM still lags behind ChatGPT in some aspects, our findings suggest that fine-tuning with AI-evolved instructions is a promising direction for enhancing large language models. Our codes and generated data are public at https://github.com/nlpxucan/WizardLMComment: large language model, instruction fine-tun

Simultaneous and Ultrasensitive Detection of Foodborne Bacteria by Gold Nanoparticles-Amplified Microcantilever Array Biosensor

Foodborne pathogens, especially bacteria, are explicitly threatening public health worldwide. Biosensors represent advances in rapid diagnosis with high sensitivity and selectivity. However, multiplexed analysis and minimal pretreatment are still challenging. We fabricate a gold nanoparticle (Au NP)-amplified microcantilever array biosensor that is capable of determining ultralow concentrations of foodborne bacteria including Escherichia coli O157:H7, Vibrio parahaemolyticus, Salmonella, Staphylococcus aureus, Listeria monocytogenes, Shigella, etc. The method is much faster than using conventional tools without germiculturing and PCR amplification. The six pairs of ssDNA probes (ssDNA1 + ssDNA2 partially complementary to the target gene) that originated from the sequence analysis of the specific gene of the bacteria were developed and validated. The ssDNA1 probes were modified with -S-(CH2)6 at the 5′-end and ready to immobilize on the self-assembled monolayers (SAMs) of the sensing cantilevers in the array and couple with Au NPs, while 6-mercapto-1-hexanol SAM modification was carried out on the reference cantilevers to eliminate the interferences by detecting the deflection from the environment induced by non-specific interactions. For multianalyte sensing, the target gene sequence was captured by the ssDNA2-Au NPs in the solution, and then the Au NPs-ssDNA2-target complex was hybridized with ssNDA1 fixed on the beam of the cantilever sensor, which results in a secondary cascade amplification effect. Integrated with the enrichment of the Au NP platform and the microcantilever array sensor detection, multiple bacteria could be rapidly and accurately determined as low as 1–9 cells/mL, and the working ranges were three to four orders of magnitude. There was virtually no cross-reaction among the various probes with different species. As described herein, it holds great potential for rapid, multiplexed, and ultrasensitive detection in food, environment, clinical, and communal samples

Unraveling the hidden function of a stabilizer in a precursor in improving hybrid perovskite film morphology for high efficiency solar cells

The morphology of the organometal trihalide perovskite (OTP) plays a critical role in the performance of solar cell devices. Nevertheless it has been frequently reported that the morphology of OTP films tends to be different in different laboratories even with the same film preparation procedure, which makes it very difficult to compare and understand the material and device physics. Here, we unravel a critical role of the H3PO2 stabilizer in HI, which has been largely ignored, in controlling the morphology of the perovskite films. The H3PO2 stabilizer in HI solution introduces MAH2PO2 impurities into the synthesized MAI (non-purified MAI) by reacting with methylamine (MA) aqueous solution. MAH2PO2 impurities can slow down the overall crystallization process of perovskite by forming an intermediate phase of Pb(H2PO2)2. Both MAH2PO2 and Pb(H2PO2)2 impede the fast reaction of PbI2 and MAI, resulting in highly uniform and smooth perovskite films with larger grain sizes. The recrystallization of non-purified MAI can remove the MAH2PO2 impurity and form purified MAI, which however results in rough and non-uniform perovskite films. Uniform and smooth perovskite films can also be obtained by directly adding artificially synthesized MAH2PO2 into the purified MAI precursor. This study also suggests Pb(H2PO2)2 to be a new precursor to formhigh quality perovskite films

Superhydrophobicity, Microwave Absorbing Property of NiFe 2

Magnetic NiFe2O4 nanoparticles were successfully deposited on the wood surface via a hydrothermal process at 70°C. The surface of the as-prepared magnetic NiFe2O4/wood hybrids (MWHs) was covered by spherical-like NiFe2O4 particles with an average size of 50 nm. MWH exhibited the thermostability, microwave absorbability, and superparamagnetism with saturation magnetization (Ms) of 1.79 emu·g−1. With further modification by 1H,1H,2H,2H-perfluorodecyltrimethoxysilane (FAS-17), MWH expressed superhydrophobic performances with a water contact angle of 158°. Its superparamagnetism stably remained under harsh conditions after chemical solutions corrosion and physical frozen test