Self-powered perovskite photon-counting detectors.

Metal-halide perovskites (MHPs) have been successfully exploited for converting photons to charges or vice versa in applications of solar cells, light-emitting diodes and solar fuels1-3, for which all these applications involve strong light. Here we show that self-powered polycrystalline perovskite photodetectors can rival the commercial silicon photomultipliers (SiPMs) for photon counting. The photon-counting capability of perovskite photon-counting detectors (PCDs) is mainly determined by shallow traps, despite that deep traps also limit charge-collection efficiency. Two shallow traps with energy depth of 5.8 ± 0.8 millielectronvolts (meV) and 57.2 ± 0.1 meV are identified in polycrystalline methylammonium lead triiodide, which mainly stay at grain boundaries and the surface, respectively. We show that these shallow traps can be reduced by grain-size enhancement and surface passivation using diphenyl sulfide, respectively. It greatly suppresses dark count rate (DCR) from >20,000 counts per second per square millimetre (cps mm-2) to 2 cps mm-2 at room temperature, enabling much better response to weak light than SiPMs. The perovskite PCDs can collect γ-ray spectra with better energy resolution than SiPMs and maintain performance at high temperatures up to 85 °C. The zero-bias operation of perovskite detectors enables no drift of noise and detection property. This study opens a new application of photon counting for perovskites that uses their unique defect properties.

Suppressed phase segregation for triple-junction perovskite solar cells.

The tunable bandgaps and facile fabrication of perovskites make them attractive for multi-junction photovoltaics1,2. However, light-induced phase segregation limits their efficiency and stability3-5: this occurs in wide-bandgap (>1.65 electron volts) iodide/bromide mixed perovskite absorbers, and becomes even more acute in the top cells of triple-junction solar photovoltaics that require a fully 2.0-electron-volt bandgap absorber2,6. Here we report that lattice distortion in iodide/bromide mixed perovskites is correlated with the suppression of phase segregation, generating an increased ion-migration energy barrier arising from the decreased average interatomic distance between the A-site cation and iodide. Using an approximately 2.0-electron-volt rubidium/caesium mixed-cation inorganic perovskite with large lattice distortion in the top subcell, we fabricated all-perovskite triple-junction solar cells and achieved an efficiency of 24.3 per cent (23.3 per cent certified quasi-steady-state efficiency) with an open-circuit voltage of 3.21 volts. This is, to our knowledge, the first reported certified efficiency for perovskite-based triple-junction solar cells. The triple-junction devices retain 80 per cent of their initial efficiency following 420 hours of operation at the maximum power point.

Mobile iodides capture for highly photolysis- and reverse-bias-stable perovskite solar cells.

For halide perovskites that are susceptible to photolysis and ion migration, iodide-related defects, such as iodine (I2) and iodine vacancies, are inevitable. Even a small number of these defects can trigger self-accelerating chemical reactions, posing serious challenges to the durability of perovskite solar cells. Fortunately, before I2 can damage the perovskites under illumination, they generally diffuse over a long distance. Therefore, detrimental I2 can be captured by interfacial materials with strong iodide/polyiodide (Ix-) affinities, such as fullerenes and perfluorodecyl iodide. However, fullerenes in direct contact with perovskites fail to confine Ix- ions within the perovskite layer but cause detrimental iodine vacancies. Perfluorodecyl iodide, with its directional Ix- affinity through halogen bonding, can both capture and confine Ix-. Therefore, inverted perovskite solar cells with over 10 times improved ultraviolet irradiation and thermal-light stabilities (under 85 °C and 1 sun illumination), and 1,000 times improved reverse-bias stability (under ISOS-V ageing tests) have been developed.

Light-enhanced oxygen degradation of MAPbBr3 single crystal.

Organometal halide perovskites are promising materials for optoelectronic applications, whose commercial realization depends critically on their stability under multiple environmental factors. In this study, a methylammonium lead bromide (MAPbBr3) single crystal was cleaved and exposed to simultaneous oxygen and light illumination under ultrahigh vacuum (UHV). The exposure process was monitored using X-ray photoelectron spectroscopy (XPS) with precise control of the exposure time and oxygen pressure. It was found that the combination of oxygen and light accelerated the degradation of MAPbBr3, which could not be viewed as a simple addition of that by oxygen-only and light-only exposures. The XPS spectra showed significant loss of carbon, bromine, and nitrogen at an oxygen exposure of 1010 Langmuir with light illumination, approximately 17 times of the additive effects of oxygen-only and light-only exposures. It was also found that the photoluminescence (PL) emission was much weakened by oxygen and light co-exposure, while previous reports had shown that PL was substantially enhanced by oxygen-only exposure. Measurements using a scanning electron microscope (SEM) and focused ion beam (FIB) demonstrated that the crystal surface was much roughened by the co-exposure. Density functional theory (DFT) calculations revealed the formation of superoxide and oxygen induced gap state, suggesting the creation of oxygen radicals by light illumination as a possible microscopic driving force for enhanced degradation.

An association study of m6A methylation with major depressive disorder.

OBJECTIVE: To find the relationship between N6-methyladenosine (m6A) genes and Major Depressive Disorder (MDD). METHODS: Differential expression of m6A associated genes between normal and MDD samples was initially identified. Subsequent analysis was conducted on the functions of these genes and the pathways they may affect. A diagnostic model was constructed using the expression matrix of these differential genes, and visualized using a nomogram. Simultaneously, an unsupervised classification method was employed to classify all patients based on the expression of these m6A associated genes. Following this, common differential genes among different clusters were computed. By analyzing the functions of the common differential expressed genes among clusters, the role of m6A-related genes in the pathogenesis of MDD patients was elucidated. RESULTS: Differential expression was observed in ELAVL1 and YTHDC2 between the MDD group and the control group. ELAVL1 was associated with comorbid anxiety in MDD patients. A linear regression model based on these two genes could accurately predict whether patients in the GSE98793 dataset had MDD and could provide a net benefit for clinical decision-making. Based on the expression matrix of ELAVL1 and YTHDC2, MDD patients were classified into three clusters. Among these clusters, there were 937 common differential genes. Enrichment analysis was also performed on these genes. The ssGSEA method was applied to predict the content of 23 immune cells in the GSE98793 dataset samples. The relationship between these immune cells and ELAVL1, YTHDC2, and different clusters was analyzed. CONCLUSION: Among all the m6A genes, ELAVL1 and YTHDC2 are closely associated with MDD, ELAVL1 is related to comorbid anxiety in MDD. ELAVL1 and YTHDC2 have opposite associations with immune cells in MDD.

Trends in respiratory diseases before and after the COVID-19 pandemic in China from 2010 to 2021.

BACKGROUND: The ongoing benefits of coronavirus disease 2019 (COVID-19) nonpharmaceutical interventions (NPIs) for respiratory infectious diseases in China are still unclear. We aimed to explore the changes in seven respiratory infectious diseases before, during, and after COVID-19 in China from 2010 to 2021. METHODS: The monthly case numbers of seven respiratory infectious diseases were extracted to construct autoregressive integrated moving average (ARIMA) models. Eight indicators of NPIs were chosen from the COVID-19 Government Response Tracker system. The monthly case numbers of the respiratory diseases and the eight indicators were used to establish the Multivariable generalized linear model (GLM) to calculate the incidence rate ratios (IRRs). RESULTS: Compared with the year 2019, the percentage changes in 2020 and 2021 were all below 100% ranging from 3.81 to 84.71%. Pertussis and Scarlet fever started to increase in 2021 compared with 2020, with a percentage change of 183.46 and 171.49%. The ARIMA model showed a good fit, and the predicted data fitted well with the actual data from 2010 to 2019, but the predicted data was bigger than the actual number in 2020 and 2021. All eight indicators could negatively affect the incidence of respiratory diseases. The seven respiratory diseases were significantly reduced during the COVID-19 pandemic in 2020 and 2021 compared with 2019, with significant estimated IRRs ranging from 0.06 to 0.85. In the GLM using data for the year 2020 and 2021, the IRRs were not significant after adjusting for the eight indicators in multivariate analysis. CONCLUSION: Our study demonstrated the incidence of the seven respiratory diseases decreased rapidly during the COVID-19 pandemic in 2020 and 2021. At the end of 2021, we did see a rising trend for the seven respiratory diseases compared to the year 2020 when the NPIs relaxed in China, but the rising trend was not significant after adjusting for the NPIs indicators. Our study showed that NPIs have an effect on respiratory diseases, but Relaxation of NPIs might lead to the resurgence of respiratory diseases.

A long trend of sexually transmitted diseases before and after the COVID-19 pandemic in China (2010-21).

BACKGROUND: The longer ongoing benefits of coronavirus disease 2019 (COVID-19) non-pharmaceutical interventions (NPIs) for sexually transmitted diseases (STDs) in China are still unclear. We aimed to explore the changes in five STDs (AIDS, hepatitis B, hepatitis C, gonorrhoea, and syphilis) before, during, and after the COVID-19 pandemic in mainland China, from 2010 to 2021. METHODS: The number of the monthly reported cases of the five STDs were extracted from the website to construct the Joinpoint regression and autoregressive integrated moving average (ARIMA) models. Eight indicators reflecting NPIs were chosen from the COVID-19 Government Response Tracker system. The STDs and eight indicators were used to establish the Multivariable generalised linear model (GLM) to calculate the incidence rate ratios (IRRs). RESULTS: With the exception of hepatitis B, the other four STDs (AIDS, hepatitis C, gonorrhoea, and syphilis) had a positive average annual percent change over the past 12years. All the ARIMA models had passed the Ljung-Box test, and the predicted data fit well with the data from 2010 to 2019. All five STDs were significantly reduced in 2020 compared with 2019, with significant estimated IRRs ranging from 0.88 to 0.92. In the GLM, using data for the years 2020 (February-December) and 2021, the IRRs were not significant after adjusting for the eight indicators in multivariate analysis. CONCLUSION: Our study demonstrated that the incidence of the five STDs decreased rapidly during the COVID-19 pandemic in 2020. A recovery of STDs in 2021 was found to occur compared with that in 2020, but the rising trend disappeared after adjusting for the NPIs. Our study demonstrated that NPIs have an effect on STDs, but the relaxation of NPI usage might lead to a resurgence.

pH-Responsive and liver-targeting drug delivery system for combination delivery of artesunate with arsenic trioxide prodrug against hepatocellular carcinoma.

OBJECTIVE: Arsenic trioxide (ATO) exerts therapeutic effects on various solid tumors, and artesunate (ART) synergizes with antitumor drugs. We herein combined ART and an ATO prodrug (ATOP) in pH-responsive and liver-targeting liposomes to improve targeted hepatocellular carcinoma (HCC) treatment. METHODS: 1,2-Distearoyl-sn-glycero-3-phosphoethanolamine (DSPE)-hydrazone (HYD)-polyethylene glycol (PEG)-glycyrrhetinic acid (GA) (DSPE-HYD-PEG-GA) was synthesized and characterized. The optimal ratio of ART and ATOP was selected. Calcium arsenate nanoparticles (CaAs NPs) and DSPE-HYD-PEG-GA@ART/CaAs NPs liposomes were prepared and their physicochemical properties were characterized. Their intracellular uptake, intracellular localization, uptake pathway identification, cytotoxicity, proapoptotic effects, and relevant mechanisms were studied. RESULTS: The DSPE-HYD-PEG-GA was successfully synthesized. The best ratio of ART and ATOP was 7:1. The particle size of CaAs NPs under transmission electron microscopy was 142.39 ± 21.50 nm. Arsenic (As), calcium, and oxygen elements were uniformly distributed in CaAs NPs, and the drug loading and encapsulation efficiency of As are 37.28% and 51.40%, respectively. The liposomes were elliptical, and the particle size was 100.91 ± 39.31 nm. The liposome cell intake was significantly increased in Huh-7 cells. The liposomes entered the cell through macropinocytosis and caveolin-mediated endocytosis and were predominantly distributed in the cytoplasm. They exerted an excellent inhibitory effect on Huh-7 cells and promoted tumor cell apoptosis through lipid peroxidation, mitochondrial membrane potential reduction, and cell-cycle blockage. CONCLUSIONS: The pH-responsive and liver-targeting drug delivery system for the combination delivery of ART with ATOP showed promising effects on hepatocellular carcinoma (HCC).

A novel U-shaped relationship between mitochondrial mass and risk of incomplete immune reconstitution in HIV-infected patients on antiviral therapy.

Mitochondrial mass (MM) is considered an essential parameter of the immune system, but the association of MM with incomplete immune reconstitution (IIR) in people living with HIV (PLWH) remains unclear. Here, we tested 2148 blood samples from 1999 PLWH by flow cytometry in China between August 2021 and February 2022. A novel U-shaped relationship, determined by multivariable smooth curve fitting and piecewise-linear mixed-effect model, was observed between the ratio of MM to SD (MM/SD) and IIR, with a threshold cutoff of 2.8. For MM/SD <2.8, per SD increment of MM was independently associated with 30%, 30%, 20%, and 20% decreased risk of CD4+ T-cell counts <500 cells/µL after 4 years of treatment and CD4+ T-cell counts <350 cells/µL after 4, 5, 6 years of treatment, respectively. Our study suggested that increasing MM may indicate the low risk of IIR for PLWH with MM/SD <2.8.

Machine learning to predict post-operative acute kidney injury stage 3 after heart transplantation.

BACKGROUND: Acute kidney injury (AKI) stage 3, one of the most severe complications in patients with heart transplantation (HT), is associated with substantial morbidity and mortality. We aimed to develop a machine learning (ML) model to predict post-transplant AKI stage 3 based on preoperative and perioperative features. METHODS: Data from 107 consecutive HT recipients in the provincial center between 2018 and 2020 were included for analysis. Logistic regression with L2 regularization was used for the ML model building. The predictive performance of the ML model was assessed using the area under the curve (AUC) in tenfold stratified cross-validation and was compared with that of the Cleveland-clinical model. RESULTS: Post-transplant AKI occurred in 76 (71.0%) patients including 15 (14.0%) stage 1, 18 (16.8%) stage 2, and 43 (40.2%) stage 3 cases. The top six features selected for the ML model to predicate AKI stage 3 were serum cystatin C, estimated glomerular filtration rate (eGFR), right atrial long-axis dimension, left atrial anteroposterior dimension, serum creatinine (SCr) and FVII. The predictive performance of the ML model (AUC: 0.821; 95% confidence interval [CI]: 0.740-0.901) was significantly higher compared with that of the Cleveland-clinical model (AUC: 0.654; 95% [CI]: 0.545-0.763, p < 0.05). CONCLUSIONS: The ML model, which achieved an effective predictive performance for post-transplant AKI stage 3, may be helpful for timely intervention to improve the patient's prognosis.

Frequency dependence of the ultrasonic power reflected from the water-tissue interface of human cancellous bone in vitro.

Numerous studies have performed in vitro ultrasonic measurements of cancellous bone in water to develop techniques for ultrasonic bone assessment. Because cancellous bone is a highly porous medium, ultrasonic reflections at the water-bone interface may be frequency dependent. The goal of this study was to investigate the effect of porosity on the frequency dependence of the reflected power. Ultrasonic measurements were performed in a water tank at room temperature on 15 specimens of cancellous bone prepared from the proximal end of 9 human femurs using single element, broadband transducers with center frequencies of 3.5, 5, 7.5, and 10 MHz. Power spectra of pulses reflected from the water-specimen interface were corrected for the frequency response of the measurement system to obtain the reflected power in decibels RdB(f). To suppress random phase cancellation effects, RdB(f) was averaged over multiple sites on multiple specimens. A frequency dependence of RdB(f) was observed in the 2.6-10 MHz range. The frequency dependence was moderate, with a maximum change of less than 6 dB over the entire frequency range. RdB(f) was greatest for low porosity specimens. The frequency averaged intensity reflection coefficient ranged from 7.4 × 10-4 to 7.8 × 10-3 for high and low porosity specimen groups, respectively.

Severity-associated markers and assessment model for predicting the severity of COVID-19: a retrospective study in Hangzhou, China.

BACKGROUND: The severity of COVID-19 associates with the clinical decision making and the prognosis of COVID-19 patients, therefore, early identification of patients who are likely to develop severe or critical COVID-19 is critical in clinical practice. The aim of this study was to screen severity-associated markers and construct an assessment model for predicting the severity of COVID-19. METHODS: 172 confirmed COVID-19 patients were enrolled from two designated hospitals in Hangzhou, China. Ordinal logistic regression was used to screen severity-associated markers. Least Absolute Shrinkage and Selection Operator (LASSO) regression was performed for further feature selection. Assessment models were constructed using logistic regression, ridge regression, support vector machine and random forest. The area under the receiver operator characteristic curve (AUROC) was used to evaluate the performance of different models. Internal validation was performed by using bootstrap with 500 re-sampling in the training set, and external validation was performed in the validation set for the four models, respectively. RESULTS: Age, comorbidity, fever, and 18 laboratory markers were associated with the severity of COVID-19 (all P values < 0.05). By LASSO regression, eight markers were included for the assessment model construction. The ridge regression model had the best performance with AUROCs of 0.930 (95% CI, 0.914-0.943) and 0.827 (95% CI, 0.716-0.921) in the internal and external validations, respectively. A risk score, established based on the ridge regression model, had good discrimination in all patients with an AUROC of 0.897 (95% CI 0.845-0.940), and a well-fitted calibration curve. Using the optimal cutoff value of 71, the sensitivity and specificity were 87.1% and 78.1%, respectively. A web-based assessment system was developed based on the risk score. CONCLUSIONS: Eight clinical markers of lactate dehydrogenase, C-reactive protein, albumin, comorbidity, electrolyte disturbance, coagulation function, eosinophil and lymphocyte counts were associated with the severity of COVID-19. An assessment model constructed with these eight markers would help the clinician to evaluate the likelihood of developing severity of COVID-19 at admission and early take measures on clinical treatment.

Reducing Surface Halide Deficiency for Efficient and Stable Iodide-Based Perovskite Solar Cells.

State-of-the-art, high-performance perovskite solar cells (PSCs) contain a large amount of iodine to realize smaller bandgaps. However, the presence of numerous iodine vacancies at the surface of the film formed by their evaporation during the thermal annealing process has been broadly shown to induce deep-level defects, incur nonradiative charge recombination, and induce photocurrent hysteresis, all of which limit the efficiency and stability of PSCs. In this work, modifying the defective surface of perovskite films with cadmium iodide (CdI2) effectively reduces the degree of surface iodine deficiency and stabilizes iodine ions via the formation of strong Cd-I ionic bonds. This largely reduces the interfacial charge recombination loss, yielding a high efficiency of 21.9% for blade-coated PSCs with an open-circuit voltage of 1.20 V, corresponding to a record small voltage deficit of 0.31 V. The CdI2 surface treatment also improves the operational stability of the PSCs, retaining 92% efficiency after constant illumination at 1 sun intensity for 1000 h. This work provides a promising strategy to optimize the surface/interface optoelectronic properties of perovskites for more efficient and stable solar cells and other optoelectronic devices.

Modulation of the plasticity of an all-metal oxide synaptic transistor via laser irradiation.

Artificial intelligence devices that can mimic human brains are the foundation for building future artificial neural networks. A key step in mimicking biological neural systems is the modulation of synaptic weight, which is mainly achieved by various engineering approaches using material design, or modification of the device structure. Here, we realize the modulation of the synaptic weight of a Ta2O5/ITO-based all-metal oxide synaptic transistor via laser irradiation. Prior to the deposition of the active layer and electrodes, a femtosecond laser was used to irradiate the surface of the insulator layer. Typical synaptic characteristics such as excitatory postsynaptic current, paired pulse facilitation and long-term potentiation were successfully simulated under different laser intensities and scanning rates. In particular, we demonstrate for the first time that laser irradiation could control the quantity of oxygen vacancies in the Ta2O5 thin film, leading to precise modulation of the synaptic weight. Our research provides an instantaneous (<1 s), convenient and low-temperature approach to improving synaptic behaviors, which could be promising for neuromorphic computing hardware design.

Imperfections and their passivation in halide perovskite solar cells.

All highly-efficient organic-inorganic halide perovskite (OIHP) solar cells to date are made of polycrystalline perovskite films which contain a high density of defects, including point and extended imperfections. The imperfections in OIHP materials play an important role in the process of charge recombination and ion migration in perovskite solar cells (PSC), which heavily influences the resulting device energy conversion efficiency and stability. Here we review the recent advances in passivation of imperfections and suppressing ion migration to achieve improved efficiency and highly stable perovskite solar cells. Due to the ionic nature of OIHP materials, the defects in the photoactive films are inevitably electrically charged. The deep level traps induced by particular charged defects in OIHP films are major non-radiative recombination centers; passivation by coordinate bonding, ionic bonding, or chemical conversion have proven effective in mitigating the negative impacts of these deep traps. Shallow level charge traps themselves may contribute little to non-radiative recombination, but the migration of charged shallow level traps in OIHP films results in unfavorable band bending, interfacial reactions, and phase segregation, influencing the carrier extraction efficiency. Finally, the impact of defects and ion migration on the stability of perovskite solar cells is described.

Landslide Susceptibility Prediction Modeling Based on Remote Sensing and a Novel Deep Learning Algorithm of a Cascade-Parallel Recurrent Neural Network.

Landslide susceptibility prediction (LSP) modeling is an important and challenging problem. Landslide features are generally uncorrelated or nonlinearly correlated, resulting in limited LSP performance when leveraging conventional machine learning models. In this study, a deep-learning-based model using the long short-term memory (LSTM) recurrent neural network and conditional random field (CRF) in cascade-parallel form was proposed for making LSPs based on remote sensing (RS) images and a geographic information system (GIS). The RS images are the main data sources of landslide-related environmental factors, and a GIS is used to analyze, store, and display spatial big data. The cascade-parallel LSTM-CRF consists of frequency ratio values of environmental factors in the input layers, cascade-parallel LSTM for feature extraction in the hidden layers, and cascade-parallel full connection for classification and CRF for landslide/non-landslide state modeling in the output layers. The cascade-parallel form of LSTM can extract features from different layers and merge them into concrete features. The CRF is used to calculate the energy relationship between two grid points, and the extracted features are further smoothed and optimized. As a case study, the cascade-parallel LSTM-CRF was applied to Shicheng County of Jiangxi Province in China. A total of 2709 landslide grid cells were recorded and 2709 non-landslide grid cells were randomly selected from the study area. The results show that, compared with existing main traditional machine learning algorithms, such as multilayer perception, logistic regression, and decision tree, the proposed cascade-parallel LSTM-CRF had a higher landslide prediction rate (positive predictive rate: 72.44%, negative predictive rate: 80%, total predictive rate: 75.67%). In conclusion, the proposed cascade-parallel LSTM-CRF is a novel data-driven deep learning model that overcomes the limitations of traditional machine learning algorithms and achieves promising results for making LSPs.

Preparation, evaluation, and in vitro cytotoxicity studies of artesunate-loaded glycyrrhetinic acid decorated PEG-PLGA nanoparticles.

OBJECTIVE: The objective of this study was to prepare the liver targeting drug delivery system (TDDS) of artesunate (ART)-loaded polyethylene glycol (PEG)-poly(d,l-lactic-co-glycolic) acid (PLGA) nanoparticles (NPs) modified by glycyrrhetinic acid (GA), and evaluate its in vitro cytotoxicity. SIGNIFICANCE: The GA-PEG-PLGA-ART NPs enhanced the in vitro cytotoxicity on HCC cell lines. The development of GA-PEG-PLGA NPs will greatly push the clinical applications of ART as a novel anticancer drug. METHODS: The NPs were prepared using solvent evaporation method, and the formulation was optimized through an orthogonal design. In addition, physical properties were determined, including particle size, polydispersity index (PDI), zeta potential (ZP), morphology, drug loading capacity (LC) and encapsulation efficiency (EE), and in vitro drug release. Moreover, the in vitro cytotoxicity of NPs with three human cancer cell lines viz. HepG2, Hep3B, and SMCC-7721 was conducted using the SRB assay. Additionally, lyophilization was conducted to improve the long-term physical stability. RESULTS: The GA-PEG-PLGA-ART NPs have spherical shape, small particle size (around 88 nm) with a narrow size distribution (PDI < 0.3), high drug LC (up to 59.3 ± 1.65%), and high EE (up to 73.13 ± 5.17%). In vitro drug release behavior showed that drugs were released from NPs in a sustained and controlled release pattern. Cytotoxicity study indicated the NPs achieved lower cancer cell survival fraction. The GA-PEG-PLGA NPs freeze-dried with 3% (w/v) of mannitol showed better effect on long-term physical stability. CONCLUSION: The GA-PEG-PLGA-ART NPs appear as a potential liver targeted intracellular delivery platform for ART.

Suppressing Interfacial Charge Recombination in Electron-Transport-Layer-Free Perovskite Solar Cells to Give an Efficiency Exceeding 21 .

The performances of electron-transport-layer (ETL)-free perovskite solar cells (PSCs) are still inferior to ETL-containing devices. This is mainly due to severe interfacial charge recombination occurring at the transparent conducting oxide (TCO)/perovskite interface, where the photo-injected electrons in the TCO can travel back to recombine with holes in the perovskite layer. Herein, we demonstrate for the first time that a non-annealed, insulating, amorphous metal oxyhydroxide, atomic-scale thin interlayer (ca. 3 nm) between the TCO and perovskite facilitates electron tunneling and suppresses the interfacial charge recombination. This largely reduced the interfacial charge recombination loss and achieved a record efficiency of 21.1 % for n-i-p structured ETL-free PSCs, outperforming their ETL-containing metal oxide counterparts (18.7 %), as well as narrowing the efficiency gap with high-efficiency PSCs employing highly crystalline TiO2 ETLs.

Tailoring Passivation Molecular Structures for Extremely Small Open-Circuit Voltage Loss in Perovskite Solar Cells.

Passivation of electronic defects at the surface and grain boundaries of perovskite materials has become one of the most important strategies to suppress charge recombination in both polycrystalline and single-crystalline perovskite solar cells. Although many passivation molecules have been reported, it remains very unclear regarding the passivation mechanisms of various functional groups. Here, we systematically engineer the structures of passivation molecular functional groups, including carboxyl, amine, isopropyl, phenethyl, and tert-butylphenethyl groups, and study their passivation capability to perovskites. It reveals the carboxyl and amine groups would heal charged defects via electrostatic interactions, and the neutral iodine related defects can be reduced by the aromatic structures. The judicious control of the interaction between perovskite and molecules can further realize grain boundary passivation, including those that are deep toward substrates. Understanding of the underlining mechanisms allows us to design a new passivation molecule, D-4- tert-butylphenylalanine, yielding high-performance p-i-structure solar cells with a stabilized efficiency of 21.4%. The open-circuit voltage ( VOC) of a device with an optical bandgap of 1.57 eV for the perovskite layer reaches 1.23 V, corresponding to a record small VOC deficit of 0.34 V. Our findings provide a guidance for future design of new passivation molecules to realize multiple facets applications in perovskite electronics.

Large electrostrictive response in lead halide perovskites.

Lead halide perovskites have demonstrated outstanding performance in photovoltaics, photodetectors, radiation detectors and light-emitting diodes. However, the electromechanical properties, which are the main application of inorganic perovskites, have rarely been explored for lead halide perovskites. Here, we report the discovery of a large electrostrictive response in methylammonium lead triiodide (MAPbI3) single crystals. Under an electric field of 3.7 V µm-1, MAPbI3 shows a large compressive strain of 1%, corresponding to a mechanical energy density of 0.74 J cm-3, comparable to that of human muscles. The influences of piezoelectricity, thermal expansion, intrinsic electrostrictive effect, Maxwell stress, ferroelectricity, local polar fluctuation and methylammonium cation ordering on this electromechanical response are excluded. We speculate, using density functional theory, that electrostriction of MAPbI3 probably originates from lattice deformation due to formation of additional defects under applied bias. The discovery of large electrostriction in lead iodide perovskites may lead to new potential applications in actuators, sonar and micro-electromechanical systems and aid the understanding of other field-dependent material properties.