Self-powered multifunctional platform based on dual-photoelectrode for dual-mode detection and inactivation of Salmonella enteritidis.

Self-powered photoelectrochemical (PEC) sensing is a novel sensing modality. The introduction of dual-mode sensing and photoelectrocatalysis in a self-powered system enables both detection and sterilization purposes. To this end, herein, a self-powered multifunctional platform for the photoelectrochemical-fluorescence (PEC-FL) detection and in-situ inactivation of Salmonella enteritidis (SE) was constructed. The platform utilized Bi4NbO8Cl/V2CTx/FTO as a photoanode and CuInS2/FTO as a photocathode and incubated quantum dot (QDs) signaling probes on the surface of the photocathode. During detection, the system drives the transfer of photogenerated electrons between the dual photoelectrodes through the Fermi energy level difference. The photoanode amplifies the photoelectric signal, while the photocathode is solely dedicated to the immune recognition process. QDs provide an additional fluorescence signal to the system. Under optimal experimental conditions, the multifunctional platform achieves detection limits of 3.2 and 5.3 CFU/mL in PEC and FL modes respectively, with a detection range of 2.91 × 102 to 2.91 × 108 CFU/mL. With the application of an external bias voltage, it further promotes electron transfer between the dual photoelectrodes, inhibits the recombination of photogenerated electrons and holes. It generates a significant amount of superoxide radicals (·O2-) in the cathodic region, resulting in strong sterilization efficiency (99%). The constructed self-powered multifunctional platform exhibits high sensitivity and sterilization efficiency, it provides a feasible and effective strategy to enhance the comprehensive capability of self-powered sensors.

Thermodynamic and Kinetic Behaviors of Electrolytes Mediated by Intermolecular Interactions Enabling High-Performance Lithium-Ion Batteries.

Electrolyte solvation chemistry regulated by lithium salts, solvents, and additives has garnered significant attention since it is the most effective strategy for designing high-performance electrolytes in lithium-ion batteries (LIBs). However, achieving a delicate balance is a persistent challenge, given that excessively strong or weak Li+-solvent coordination markedly undermines electrolyte properties, including thermodynamic redox stability and Li+-desolvation kinetics, limiting the practical applications. Herein, we elucidate the crucial influence of solvent-solvent interactions in modulating the Li+-solvation structure to enhance electrolyte thermodynamic and kinetic properties. As a paradigm, by combining strongly coordinated propylene carbonate (PC) with weakly coordinated cyclopentylmethyl ether (CPME), we identified intermolecular interactions between PC and CPME using 1H-1H correlation spectroscopy. Experimental and computational findings underscore the crucial role of solvent-solvent interactions in regulating Li+-solvent/anion interactions, which can enhance both the thermodynamic (i.e., antireduction capability) and kinetic (i.e., Li+-desolvation process) aspects of electrolytes. Additionally, we introduced an interfacial model to reveal the intricate relationship between solvent-solvent interactions, electrolyte properties, and electrode interfacial behaviors at a molecular scale. This study provides valuable insights into the critical impact of solvent-solvent interactions on electrolyte properties, which are pivotal for guiding future efforts in functionalized electrolyte engineering for metal-ion batteries.

Clinical performance of automated machine learning: A systematic review.

Introduction: Automated machine learning (autoML) removes technical and technological barriers to building artificial intelligence models. We aimed to summarise the clinical applications of autoML, assess the capabilities of utilised platforms, evaluate the quality of the evidence trialling autoML, and gauge the performance of autoML platforms relative to conventionally developed models, as well as each other. Method: This review adhered to a prospectively registered protocol (PROSPERO identifier CRD42022344427). The Cochrane Library, Embase, MEDLINE and Scopus were searched from inception to 11 July 2022. Two researchers screened abstracts and full texts, extracted data and conducted quality assessment. Disagreement was resolved through discussion and if required, arbitration by a third researcher. Results: There were 26 distinct autoML platforms featured in 82 studies. Brain and lung disease were the most common fields of study of 22 specialties. AutoML exhibited variable performance: area under the receiver operator characteristic curve (AUCROC) 0.35-1.00, F1-score 0.16-0.99, area under the precision-recall curve (AUPRC) 0.51-1.00. AutoML exhibited the highest AUCROC in 75.6% trials; the highest F1-score in 42.3% trials; and the highest AUPRC in 83.3% trials. In autoML platform comparisons, AutoPrognosis and Amazon Rekognition performed strongest with unstructured and structured data, respectively. Quality of reporting was poor, with a median DECIDE-AI score of 14 of 27. Conclusion: A myriad of autoML platforms have been applied in a variety of clinical contexts. The performance of autoML compares well to bespoke computational and clinical benchmarks. Further work is required to improve the quality of validation studies. AutoML may facilitate a transition to data-centric development, and integration with large language models may enable AI to build itself to fulfil user-defined goals.

Nomogram established on account of Lasso-logistic regression for predicting hemorrhagic transformation in patients with acute ischemic stroke after endovascular thrombectomy.

BACKGROUND: Hemorrhagic transformation (HT) is a common and serious complication in patients with acute ischemic stroke (AIS) after endovascular thrombectomy (EVT). This study was performed to determine the predictive factors associated with HT in stroke patients with EVT and to establish and validate a nomogram that combines with independent predictors to predict the probability of HT after EVT in patients with AIS. METHODS: All patients were randomly divided into development and validation cohorts at a ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) regression was used to select the optimal factors, and multivariate logistic regression analysis was used to build a clinical prediction model. Calibration plots, decision curve analysis (DCA) and receiver operating characteristic curve (ROC) were generated to assess predictive performance. RESULTS: LASSO regression analysis showed that Alberta Stroke Program Early CT Scores (ASPECTS), international normalized ratio (INR), uric acid (UA), neutrophils (NEU) were the influencing factors for AIS with HT after EVT. A novel prognostic nomogram model was established to predict the possibility of HT with AIS after EVT. The calibration curve showed that the model had good consistency. The results of ROC analysis showed that the AUC of the prediction model established in this study for predicting HT was 0.797 in the development cohort and 0.786 in the validation cohort. CONCLUSION: This study proposes a novel and practical nomogram based on ASPECTS, INR, UA, NEU, which can well predict the probability of HT after EVT in patients with AIS.

Temporal Feature Fusion for 3D Detection in Monocular Video.

Previous monocular 3D detection works focus on the single frame input in both training and inference. In real-world applications, temporal and motion information naturally exists in monocular video. It is valuable for 3D detection but under-explored in monocular works. In this paper, we propose a straightforward and effective method for temporal feature fusion, which exhibits low computation cost and excellent transferability, making it conveniently applicable to various monocular models. Specifically, with the help of optical flow, we transform the backbone features produced by prior frames and fuse them into the current frame. We introduce the scene feature propagating mechanism, which accumulates history scene features without extra time-consuming. In this process, occluded areas are removed via forward-backward scene consistency. Our method naturally introduces valuable temporal features, facilitating 3D reasoning in monocular 3D detection. Furthermore, accumulated history scene features via scene propagating mitigate heavy computation overheads for video processing. Experiments are conducted on variant baselines, which demonstrate that the proposed method is model-agonistic and can bring significant improvement to multiple types of single-frame methods.

Effects of natural extract from medicinal herbs on broilers experimentally infected with Eimeria tenella.

This study aimed to evaluate the effects of natural extracts from nine medicinal herbs (SMA) on the growth performance, immunity, and intestinal integrity of broilers experimentally infected with Eimeria tenella. A total of 252 one-day-old broiler chicks were divided into 7 groups with 3 replicates per group and 12 broilers per cage. The groups were uninfected-untreated blank control group (BC), infected-untreated negative control group (NC), SMA treatment groups, Chinese medicine positive control group (CM), and chemical drug positive control group (CD). The SMA groups were infected and fed a basal diet supplemented with 0.6 (SMA-L), 0.8 (SMA-M), and 1.0 (SMA-H) g/kg SMA. The CM and CD groups were infected and fed a basal diet supplemented with 15 g/kg Jiqiuchong San and 0.2 g/kg Diclazuril, respectively. Results showed that feeding SMA could significantly reduce the number of oocysts in infected chickens, especially 1.0 g/kg SMA, which exhibited moderate anticoccidial efficacy. When infected with E. tenella, the supplementation of 1.0 g/kg SMA increased the renal index; restored the hepatic, splenic, and bursal indexes to BC levels; increased the levels of immunoglobulin A (IgA), IgM, and IgY; and reduced the contents of tumor necrosis factor (TNF-α), interferon-γ (IFN-γ), interleukin-6 (IL-6), and IL-10 of the infected chickens. Moreover, treatment with 1.0 g/kg SMA alleviated the pathological changes in cecal tissue and increased the contents of zonula occludens-1 (ZO-1), occludin, claudin-1, and mucoprotein 2 (mucin-2) in cecal tissues of E. tenella-infected chickens. We found that 1.0 g/kg SMA reduced the number of oocysts, improved immunity, and alleviated intestinal barrier damage, which could improve the growth performance of infected chickens. Thus, SMA proved to be an effective natural extract against E. tenella and has the potential to be used as an efficient anticoccidial drug or additive.

Characterization and interactions of spoilage of Pseudomonas fragi C6 and Brochothrix thermosphacta S5 in chilled pork based on LC-MS/MS and screening of potential spoilage biomarkers.

Pseudomonas and Brochothrix are the main spoilage organisms in pork, and each of these plays an essential role in the spoilage process. However, the effect of co-contamination of these two organisms in pork has not been elucidated. The changing bacterial communities during spontaneous spoilage of pork at 4 °C were evaluated using high-throughput sequencing. The dominant spoilage bacteria were isolated and these were identified as Pseudomonas fragi C6 and Brochothrix thermosphacta S5. Chilled pork was then experimentally contaminated with these strains, individually and in combination, and the progression of spoilage was assessed by analyzing various physicochemical indicators. These included total viable counts (TVC), pH, color, total volatile basic nitrogen (TVB-N), and detection of microbial metabolites. After 7 days of chilled storage, co-contaminated pork produced higher TVC and TVB-N values than mono-contaminated samples. Metabolomic analysis identified a total of 8,084 metabolites in all three groups combined. Differential metabolites were identified, which were involved in 38 metabolic pathways. Among these pathways, the biosynthesis of alkaloids derived from purine and histidine was identified as an important pathway related to spoilage. Specifically, histidine, histamine, AMP, IMP, GMP, succinic acid, and oxoglutaric acid were identified as potential spoilage biomarkers. The study showed that the combined presence of P. fragi C6 and B. thermosphacta S5 bacteria makes chilled pork more prone to spoilage, compared to their individual presence. This study provides insights that can assist in applying appropriate techniques to maintain quality and safety changes in meat during storage and further the assessment of freshness.

Gait Domains May Be Used as an Auxiliary Diagnostic Index for Alzheimer's Disease.

BACKGROUND: Alzheimer's disease (AD) is a progressive neurodegenerative disorder with cognitive dysfunction and behavioral impairment. We aimed to use principal components factor analysis to explore the association between gait domains and AD under single and dual-task gait assessments. METHODS: A total of 41 AD participants and 41 healthy control (HC) participants were enrolled in our study. Gait parameters were measured using the JiBuEn® gait analysis system. The principal component method was used to conduct an orthogonal maximum variance rotation factor analysis of quantitative gait parameters. Multiple logistic regression was used to adjust for potential confounding or risk factors. RESULTS: Based on the factor analysis, three domains of gait performance were identified both in the free walk and counting backward assessments: "rhythm" domain, "pace" domain and "variability" domain. Compared with HC, we found that the pace factor was independently associated with AD in two gait assessments; the variability factor was independently associated with AD only in the counting backwards assessment; and a statistical difference still remained after adjusting for age, sex and education levels. CONCLUSIONS: Our findings indicate that gait domains may be used as an auxiliary diagnostic index for Alzheimer's disease.

Artificial intelligence education: An evidence-based medicine approach for consumers, translators, and developers.

Current and future healthcare professionals are generally not trained to cope with the proliferation of artificial intelligence (AI) technology in healthcare. To design a curriculum that caters to variable baseline knowledge and skills, clinicians may be conceptualized as "consumers", "translators", or "developers". The changes required of medical education because of AI innovation are linked to those brought about by evidence-based medicine (EBM). We outline a core curriculum for AI education of future consumers, translators, and developers, emphasizing the links between AI and EBM, with suggestions for how teaching may be integrated into existing curricula. We consider the key barriers to implementation of AI in the medical curriculum: time, resources, variable interest, and knowledge retention. By improving AI literacy rates and fostering a translator- and developer-enriched workforce, innovation may be accelerated for the benefit of patients and practitioners.

Cationic metal-organic framework with charge separation effect as a high output triboelectric nanogenerator material for self-powered anticorrosion.

New stable frictional materials based on metal-organic frameworks (MOFs) are greatly desired for applications in self-powered systems. This work reports an ionic MOF material with efficient charge separation mediated by charge induction. ZUT-iMOF-1(Cu) is chemically stable and its triboelectric output performance surpasses those of traditional MOF materials. The short-circuit current of the iMOF triboelectric nanogenerator is 73.79 µA at 5 Hz. The output performance remains stable over 50 000 cycles of continuous operation. The charge and power densities peak at 123.20 µC m-2 and 3133.23 mW m-2. Owing to its high output performance, ZUT-iMOF-1(Cu) effectively prevents metal corrosion in cathodic-protection systems. Theoretical calculations show that increasing the charge-separation effect promotes the frictional electricity generation behaviour. This study provides research suggestions for ionic MOF frictional materials and will promote their application in self-powered electrochemical cathodic-protection systems.

Digestion profile, antioxidant, and antidiabetic capacity of Morchella esculenta exopolysaccharide: in vitro, in vivo and microbiota analysis.

BACKGROUND: Novel functional polysaccharides from fungi are important nutraceuticals. An exopolysaccharide, Morchella esculenta exopolysaccharide (MEP 2), was extracted and purified from the fermentation liquor of M. esculenta. The aim of this study was to investigate its digestion profile, antioxidant capacity, and effect on the microbiota composition in diabetic mice. RESULTS: The study found that MEP 2 was stable during in vitro saliva digestion but was partially degraded during gastric digestion. The digest enzymes exerted a negligible effect on the chemical structure of MEP 2. Molecular weight and atomic force microscope (AFM) images suggest that both smaller chains and larger aggregations were produced. Scanning electron microscope (SEM) images reveal that the surface morphology was much altered after intestinal digestion. After digestion, the antioxidant ability increased as revealed by the 2,2-diphenyl-1-picrylhydrazyl (DPPH) and 2,2'-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) assays. Both MEP 2 and its digested components showed strong α-amylase and moderate α-glucosidase inhibition activity, leading us to further investigate its ability to modulate the diabetic symptoms. The MEP 2 treatment ameliorated the inflammatory cell infiltration and increased the size of pancreas inlets. Serum concentration of HbA1c was significantly reduced. Blood glucose level during the oral glucose tolerance test (OGTT) was also slightly lower. The MEP 2 increased the diversity of the gut microbiota and modulated the abundance of several important bacteria including Alcaligenaceae, Caulobacteraceae, Prevotella, Brevundimonas, Demequina, and several Lachnospiraceae species. CONCLUSION: It was found that MEP 2 was partially degraded during in vitro digestion. Its potential antidiabetic bioactivity may be associated with its α-amylase inhibition and gut microbiome modulation ability. © 2023 Society of Chemical Industry.

Design and synthesis of hederagenin derivatives modulating STING/NF-κB signaling for the relief of acute liver injury in septic mice.

Systemic inflammatory responses often result in sepsis and inhibition of inflammation is one strategy for sepsis treatment. In this study, we designed and synthesized 32 novel hederagenin (HD) derivatives with modifications at the A-ring, C-28, and C-23 positions and screened their anti-inflammatory activities in vitro, finding multiple compounds with potential anti-inflammatory activity. Of these, compound 1 was the most effective and was used for subsequent investigations into its mechanism of action and in vivo activity. In vivo assessments of anti-inflammatory activity showed that compound 1 reduced inflammation in a mouse model of sepsis with acute liver injury caused by lipopolysaccharide (LPS). Compound 1 also inhibited STING, p-IRF3, p-TBK1, p-p65, and p-IκB proteins in cGAS-STING-associated signaling. These findings indicated that compound 1 reduced inflammation through inhibition of STING expression and hence reducing activation of STING and nuclear factor-κB (NF-κB) signaling. Our work demonstrated that compound 1 is a promising lead compound for designing and developing anti-sepsis drugs.

Development and analysis of machine-learning guided flash nanoprecipitation (FNP) for continuous chitosan nanoparticles production.

Chitosan-based nanoparticles (CNPs) are widely used in drug delivery, cosmetics formulation and food applications. To accelerate the manufacturing of CNPs, the present study develops a workflow to prepare CNPs in a continuous model. Based on machine learning, the workflow precisely predicts size and polymer dispersity index (PDI) value of CNPs, which impacts on the colloidal stability and applications. Multi-inlet vortex mixer (MIVM) device was fabricated by 3D printing as the reactor. Peristaltic pump was applied to deliver the reaction streams into the MIVM device and produce CNPs by flash nanoprecipitation (FNP) in a continuous way. The developed MIVM device produces CNPs in a controlled manner at a higher output which is promising for upscale applications. Twelve machine learning algorithms were employed to investigate the potential relationship between the reaction independent variables and hydrodynamic characteristics of CNPs. Random Forest, Decision Tree, Extra Tree and Bagging algorithms performed better than other algorithms with the average prediction accuracy around 90 %. The current study demonstrated that supervised machine learning guided FNP using the developed MIVM device is an effective strategy for accurate and intelligent production of CNPs and other similar nanoparticles.

Zinc insulin hexamer loaded alginate zinc hydrogel: Preparation, characterization and in vivo hypoglycemic ability.

Alginate zinc hydrogel loaded with zinc insulin hexamer was prepared and characterized for oral insulin administration. The hydrogel was fabricated by dripping zinc insulin hexamer into sodium alginate solution and followed by crosslinking by zinc chloride. SEM image reveals the zinc insulin hexamer was integrated into the matrix of hydrogel. Zinc insulin hexamer loaded hydrogel shows no obvious cytotoxicity to both HT29 and Caco-2 cells. The developed hydrogel retards the burst release of insulin in simulated gastric fluid but promotes the release when in simulated intestinal fluid. In the diabetic mice, zinc insulin hexamer loaded alginate hydrogel demonstrates significant and prolonged hypoglycemic effect.

Switching Electrolyte Interfacial Model to Engineer Solid Electrolyte Interface for Fast Charging and Wide-Temperature Lithium-Ion Batteries.

Engineering the solid electrolyte interphase (SEI) that forms on the electrode is crucial for achieving high performance in metal-ion batteries. However, the mechanism of SEI formation resulting from electrolyte decomposition is not fully understood at the molecular scale. Herein, a new strategy of switching electrolyte to tune SEI properties is presented, by which a unique and thinner SEI can be pre-formed on the graphite electrode first in an ether-based electrolyte, and then the as-designed graphite electrode can demonstrate extremely high-rate capabilities in a carbonate-based electrolyte, enabling the design of fast-charging and wide-temperature lithium-ion batteries (e.g., graphite | LiNi0.6 Co0.2 Mn0.2 O2 (NCM622)). A molecular interfacial model involving the conformations and electrochemical stabilities of the Li+ -solvent-anion complex is presented to elucidate the differences in SEI formation between ether-based and carbonate-based electrolytes, then interpreting the reason for the obtained higher rate performances. This innovative concept combines the advantages of different electrolytes into one battery system. It is believed that the switching strategy and understanding of the SEI formation mechanism opens a new avenue to design SEI, which is universal for pursuing more versatile battery systems with greater stability.

Ultrafast Anisotropic Evolution of Photoconductivity in Sb2Se3 Single Crystals.

The antimony chalcogenide crystals are composed of quasi-one-dimensional [Sb4X6]n ribbons, which lead to strong anisotropic optical and electronic properties. An attempt to exploit photoconductivity anisotropy in the device fabrication may introduce a rewarding strategy to propel the development of the antimony chalcogenide solar cells. To achieve this, understanding of the dynamic evolution of the photoconductivity anisotropy is required. Here, the photoconductivities along different lattice directions in an antimony selenide single crystal are investigated by time-resolved terahertz spectroscopy. We find that electron trapping results in a variation of the photoconductivity anisotropy accompanied by a decrease in the photoconductivity magnitude, while electron-hole recombination only reduces the magnitude but does not affect the anisotropy. Therefore, measuring the temporal evolution of photoconductivity anisotropy can provide a wealth of information regarding the nature of the photocarrier and also render a probe to selectively evaluate the photoconductivity decay mechanisms.

Role Mismatch in Medical Decision-Making Participation Is Associated with Anxiety and Depression in Family Members of Patients in the Intensive Care Unit.

This study aimed to investigate the mismatch between the preferred and actual roles in the medical decision-making of intensive care unit (ICU) patients' family members and the relationship between the role mismatch of family members' decisions and anxiety and depression syndromes. A total of 223 family members of ICU patients in the Affiliated Hospital of Jiangnan University in China were enrolled. The simple Chinese version of the Control Preference Scale was used to complete the surveys to assess the preferred and actual roles, and anxiety and depression syndromes were measured using the Generalized Anxiety Disorder-7 scale and Patient Health Questionnaire-9, respectively. For the preferred and actual roles, the active role rates were 16.1% and 8.1%, the cooperative role rates were 49.3% and 31.4%, and the passive role rates were 34.5% and 60.5%, respectively. The incidence of mismatch was 43.0% between the preferred and actual roles, and the consistency between their preferred and actual decision-making roles was poor (kappa = 0.309, P < 0.001). Family members with mismatched decision-making roles had significantly higher incidence rates of anxiety (90.6% vs. 57.5%, P < 0.001) and depression (86.5% vs. 63.0%, P < 0.001). Logistic regression analysis revealed that mismatches in decision-making roles remained independently associated with these outcomes after adjustment for family members' sociodemographic features. The results of the present study demonstrate that the preferred role of ICU patients' family members is mainly cooperative, and the actual role is mainly passive. The mismatch between the preferred and actual roles is associated with anxiety and depression among the ICU patients' family members.

Elevated Serum Uric Acid Increases the Risk of Ischemic Stroke Recurrence and Its Inflammatory Mechanism in Older Adults.

Background: Serum uric acid (UA) has been reported to be associated with ischemic stroke and inflammation. However, whether or not UA is related to the recurrence of ischemic stroke, and whether inflammation plays a role in the relationship between them remain inconclusive. Objective: We sought to explore the relationship between UA and the recurrence of ischemic stroke and to define the role of neutrophil-to-lymphocyte ratio (NLR) in the aforementioned relationship. Methods: A total of 8,995 patients were included in this study. Basic information and blood samples were collected, and whether or not each participant experienced ischemic stroke recurrence within 3 years was documented. Patients were stratified into three groups according to their UA level, as follows: ≤ 266, 267-339, and ≥ 340 µmol/L. COX regression and restricted cubic spline regression models were used to evaluate the clinical correlation between UA and ischemic stroke recurrence, mediation analysis and interaction and joint analysis were used to evaluate the role of NLR in the association of UA and ischemic stroke recurrence, and sensitivity and subgroup analyses were performed to test the robustness of the data. Results: Ischemic stroke recurrence was related to male sex, older age, higher UA level, higher NLR, hypertension, diabetes, and cardiovascular disease. Following adjustment for potential confounders, a high level of UA (≥ 340 µmol/L) increased the risk of recurrence by 92.6% in patients with previous ischemic stroke. We also found that NLR affects the association between UA and the recurrence of ischemic stroke in older adults, suggesting that patients with high NLR and high UA levels are at greater risk for ischemic stroke recurrence. Conclusion: UA level is non-linearly associated with recurrence, and NLR has an additive interaction between UA and ischemic stroke recurrence.

Luminescent Thin Films Enabled by CsPbX3 (X=Cl, Br, I) Precursor Solution.

Inorganic cesium lead halide perovskite nanocrystals are candidates for lighting and display materials due to their outstanding optoelectronic properties. However, the dissolution issue of perovskite nanocrystals in polar solvents remains a challenge for practical applications. Herein, we present a newly designed one-step spin-coating strategy to prepare a novel multicolor-tunable CsPbX3 (X=Cl, Br, I) nanocrystal film, where the CsPbX3 precursor solution was formed by dissolving PbO, Cs2 CO3 , and CH3 NH3 X into the ionic liquid n-butylammonium butyrate. The as-designed CsPbX3 nanocrystal films show high color purity with a narrow emission width. Also, the blue CsPb(Cl/Br)3 film demonstrates an absolute photoluminescence quantum yields (PLQY) of 15.6 %, which is higher than 11.7 % of green CsPbBr3 and 8.3 % of red CsPb(Br/I)3 film. This study develops an effective approach to preparing CsPbX3 nanocrystal thin films, opening a new avenue to design perovskite nanocrystals-based devices for lighting and display applications.