Novel Hybrid Ligands for Passivating Pb S Colloidal Quantum Dots to Enhance the Performance of Solar Cells

We developed novel hybrid ligands to passivate Pb S colloidal quantum dots(CQDs),and two kinds of solar cells based on as-synthesized CQDs were fabricated to verify the passivation effects of the ligands.It was found that the ligands strongly affected the optical and electrical properties of CQDs,and the performances of solar cells were enhanced strongly.The optimized hybrid ligands,oleic amine/octyl-phosphine acid/Cd Cl2improved power conversion efficiency(PCE)to much higher of 3.72%for Schottky diode cell and 5.04%for p–n junction cell.These results may be beneficial to design passivation strategy for low-cost and high-performance CQDs solar cells.

Systematical analysis of sludge treatment and disposal technologies for carbon footprint reduction

This study aims to comprehensively analyze the Greenhouse Gases(GHGs)emissions from current sewage sludge treatment and disposal technologies(buildingmaterial,landfill,land spreading,anaerobic digestion,and thermochemical processes)based on the database of Science Citation Index(SCI)and Social Science Citation Index(SSCI)from 1998 to 2020.The general patterns,spatial distribution,and hotspotswere provided by bibliometric analysis.A comparative quantitative analysis based on life cycle assessment(LCA)put forward the current emission situation and the key influencing factors of different technologies.The effective GHG emissions reduction methods were proposed to mitigate climate change.Results showed that incineration or building materials manufacturing of highly dewatered sludge,and land spreading after anaerobic digestion have the best GHG emissions reduction benefits.Biological treatment technologies and thermochemical processes have great potential for reducing GHGs.Enhancement of pretreatment effect,co-digestion,and newtechnologies(e.g.,injection of carbon dioxide,directional acidification)are major approaches to facilitate substitution emissions in sludge anaerobic digestion.The relationship between the quality and efficiency of secondary energy in thermochemical process and GHGs emission still needs further study.Solid sludge products generated by bio-stabilization or thermochemical processes are considered to have a certain carbon sequestration value and can improve the soil environment to control GHG emissions.The findings are useful for future development and processes selection of sludge treatment and disposal facing carbon footprint reduction.

KTI-RNN:Recognition of Heart Failure from Clinical Notes

Although deep learning methods have recently attracted considerable attention in the medical field,analyzing large-scale electronic health record data is still a difficult task.In particular,the accurate recognition of heart failure is a key technology for doctors to make reasonable treatment decisions.This study uses data from the Medical Information Mart for Intensive Care database.Compared with structured data,unstructured data contain abundant patient information.However,this type of data has unsatisfactory characteristics,e.g.,many colloquial vocabularies and sparse content.To solve these problems,we propose the KTI-RNN model for unstructured data recognition.The proposed model overcomes sparse content and obtains good classification results.The term frequency-inverse word frequency(TF-IWF)model is used to extract the keyword set.The latent dirichlet allocation(LDA)model is adopted to extract the topic word set.These models enable the expansion of the medical record text content.Finally,we embed the global attention mechanism and gating mechanism between the bidirectional recurrent neural network(BiRNN)model and the output layer.We call it gated-attention-BiRNN(GA-BiRNN)and use it to identify heart failure from extensive medical texts.Results show that the F 1 score of the proposed KTI-RNN model is 85.57%,and the accuracy rate of the proposed KTI-RNN model is 85.59%.

基于离子液体的蛋白质组样品制备新方法研究进展

蛋白质组的深度覆盖分析可以全景式地呈现蛋白质的动态变化,对于揭示疾病的发生发展机制以及发现新的药物靶点具有重要意义.样品制备是蛋白质组分析的重要环节,其中蛋白质提取至关重要,影响着蛋白质组定性定量的覆盖度.然而,现有提取试剂对疏水性蛋白质溶解能力不够,造成其在样品制备过程中损失严重.因此亟需发展对蛋白质,尤其是疏水性蛋白质,具有强溶解能力的提取试剂,并同时需要确保和后续酶解以及液质联用分析的兼容性.近年来,离子液体(ionic liquids, ILs)由于具有强溶解能力,已逐渐被应用于单一蛋白质的溶解、提取、分离等,并开始在复杂的蛋白质组样品制备中展露锋芒.本文系统总结了ILs辅助的样品制备方法在膜蛋白质组、全蛋白质组和蛋白质翻译后修饰分析方面的研究进展,探索了ILs与蛋白质的相互作用机理,并对其在蛋白质组分析中的应用前景进行了展望.

Isolation and identification of phosphorylated lysine peptides by retention time difference combining dimethyl labeling strategy

Protein phosphorylation plays essential roles in various biological procedures. Despite the well-established enrichment strategies for O-phosphoproteomics, the intrinsic acid lability of N–P phosphoramidate bond(phosphorylation of histidine, arginine and lysine) has impaired the progress of N-phosphoproteomics. Herein, we reported a retention time difference combining dimethyl labeling(ReDD) strategy for the isolation and identification of phosphorylated lysine(pLys) peptides. By such a method, pLys peptide could be isolated under 100000-fold interference of non-phosphorylated peptides. Furthermore, ReDD strategy was applied to map pLys sites from E. coli samples, leading to the identification of 11 pLys sites, among which K26p that originating from autonomous glycyl radical cofactor was validated both in mass spectrometry and HPLC co-elution experiments. Furthermore, 112 pLys sites from 100 proteins were identified in HeLa cells. All these results demonstrate that ReDD could provide a first glimpse into Lys phosphorylation, and could be an important step toward the global perspective on protein phosphorylation.

Fully automated sample treatment method for high throughput proteome analysis

The bottom-up strategy for proteome analysis typically employs a multistep sample preparation workflow that suffers from being time-consuming and sample loss or contamination caused by the off-line manual operation.Herein,we developed a hollow fibre membrane(HFM)-aided fully automated sample treatment(FAST)method.Due to the confinement effects of HFMs and the immobilized enzymatic reactor,the proteome samples could be denatured,reduced,desalted and digested within 8–20 min via the one-stop service.This method also showed superiority in trace sample analysis.In one and half hours,we could identify about 1,600 protein groups for 500 HeLa cells as the starting materials,1.5–8 times more than those obtained by previously reported methods.Through the on-line combination of FAST with nano-liquid chromatography-electrospray ionization tandem mass spectrometry(nanoLC-ESI-MS/MS),we further established a fully integrated platform for label-free quantification of proteome with high reproducibility and precision.Collectively,FAST presented here represents a major advance in the high throughput sample treatment and quantitative analysis of proteomes.

Erratum to“Fully automated sample treatment method for high throughput proteome analysis”[Sci.China Chem.,2021,64:313-321]

In the above referenced publication[1],there is a mistake about one of funding numbers in the acknowledgement part.Here we provide correction to it:We gratefully acknowledge the financial supports from the National Key Research and Development Program of China(2020YFE0202200,2018YFC0910202 and 2017YFA0505-002),and the National Natural Science Foundation of China(21974136,21725506 and 91543201).