Insights from high-fidelity modeling of industrial rotary bell atomization.

The global automotive industry sprayed over 2.6 billion liters of paint in 2018, much of which through electrostatic rotary bell atomization, a highly complex process involving the fluid mechanics of rapidly rotating thin films tearing apart into micrometer-thin filaments and droplets. Coating operations account for 65% of the energy usage in a typical automotive assembly plant, representing 10,000s of gigawatt-hours each year in the United States alone. Optimization of these processes would allow for improved robustness, reduced material waste, increased throughput, and significantly reduced energy usage. Here, we introduce a high-fidelity mathematical and algorithmic framework to analyze rotary bell atomization dynamics at industrially relevant conditions. Our approach couples laboratory experiment with the development of robust non-Newtonian fluid models; devises high-order accurate numerical methods to compute the coupled bell, paint, and gas dynamics; and efficiently exploits high-performance supercomputing architectures. These advances have yielded insight into key dynamics, including i) parametric trends in film, sheeting, and filament characteristics as a function of fluid rheology, delivery rates, and bell speed; ii) the impact of nonuniform film thicknesses on atomization performance; and iii) an understanding of spray composition via primary and secondary atomization. These findings result in coating design principles that are poised to improve energy- and cost-efficiency in a wide array of industrial and manufacturing settings.

High-performance seawater oxidation by a homogeneous multimetallic layered double hydroxide electrocatalyst.

SignificanceSeawater is one of the most abundant resources on Earth. Direct electrolysis of seawater is a transformative technology for sustainable hydrogen production without causing freshwater scarcity. However, this technology is severely impeded by a lack of robust and active oxygen evolution reaction (OER) electrocatalysts. Here, we report a highly efficient OER electrocatalyst composed of multimetallic layered double hydroxides, which affords superior catalytic performance and long-term durability for high-performance seawater electrolysis. To the best of our knowledge, this catalyst is among the most active for OER and it advances the development of seawater electrolysis technology.

A high-performance computational workflow to accelerate GATK SNP detection across a 25-genome dataset.

BACKGROUND: Single-nucleotide polymorphisms (SNPs) are the most widely used form of molecular genetic variation studies. As reference genomes and resequencing data sets expand exponentially, tools must be in place to call SNPs at a similar pace. The genome analysis toolkit (GATK) is one of the most widely used SNP calling software tools publicly available, but unfortunately, high-performance computing versions of this tool have yet to become widely available and affordable. RESULTS: Here we report an open-source high-performance computing genome variant calling workflow (HPC-GVCW) for GATK that can run on multiple computing platforms from supercomputers to desktop machines. We benchmarked HPC-GVCW on multiple crop species for performance and accuracy with comparable results with previously published reports (using GATK alone). Finally, we used HPC-GVCW in production mode to call SNPs on a "subpopulation aware" 16-genome rice reference panel with ~ 3000 resequenced rice accessions. The entire process took ~ 16 weeks and resulted in the identification of an average of 27.3 M SNPs/genome and the discovery of ~ 2.3 million novel SNPs that were not present in the flagship reference genome for rice (i.e., IRGSP RefSeq). CONCLUSIONS: This study developed an open-source pipeline (HPC-GVCW) to run GATK on HPC platforms, which significantly improved the speed at which SNPs can be called. The workflow is widely applicable as demonstrated successfully for four major crop species with genomes ranging in size from 400 Mb to 2.4 Gb. Using HPC-GVCW in production mode to call SNPs on a 25 multi-crop-reference genome data set produced over 1.1 billion SNPs that were publicly released for functional and breeding studies. For rice, many novel SNPs were identified and were found to reside within genes and open chromatin regions that are predicted to have functional consequences. Combined, our results demonstrate the usefulness of combining a high-performance SNP calling architecture solution with a subpopulation-aware reference genome panel for rapid SNP discovery and public deployment.

Enhancing Field Emission in Air via Ultrascaled Nanorod Electrodes.

Enhancing field emission in ultrascaled electronics improves the device performance and energy efficiency. Conventional lithography defines electrodes with a few-nanometer spacing on the cost of strengthened electron scattering and the reduced field enhancement factor, thus presenting challenges to enhance field emission at a small bias. Here, we used self-assembled nanorods with sub-5 nm spacing as electrodes to overcome these challenges. Intrinsic ballistic transport through high-crystallinity solution-synthesized nanorods minimized charge scattering; meanwhile ultrascaled anisotropic morphologies concentrated local electric fields and thereby lowered the barrier height. Enabled by these structural features, we demonstrated field emission density up to 4.1 × 104 A cm-2 at 1 V in air, more than 10-fold higher than typical molecular and vacuum electronics at similar conditions, and constructed an air-operating electron source with an on/off ratio of 105 at the collector electrode. Energy-efficient high-conductance electron emission suggested the potential of using solution-synthesized nanomaterials in ultrascaled electronics.

HPC-T-Annotator: an HPC tool for de novo transcriptome assembly annotation.

BACKGROUND: The availability of transcriptomic data for species without a reference genome enables the construction of de novo transcriptome assemblies as alternative reference resources from RNA-Seq data. A transcriptome provides direct information about a species' protein-coding genes under specific experimental conditions. The de novo assembly process produces a unigenes file in FASTA format, subsequently targeted for the annotation. Homology-based annotation, a method to infer the function of sequences by estimating similarity with other sequences in a reference database, is a computationally demanding procedure. RESULTS: To mitigate the computational burden, we introduce HPC-T-Annotator, a tool for de novo transcriptome homology annotation on high performance computing (HPC) infrastructures, designed for straightforward configuration via a Web interface. Once the configuration data are given, the entire parallel computing software for annotation is automatically generated and can be launched on a supercomputer using a simple command line. The output data can then be easily viewed using post-processing utilities in the form of Python notebooks integrated in the proposed software. CONCLUSIONS: HPC-T-Annotator expedites homology-based annotation in de novo transcriptome assemblies. Its efficient parallelization strategy on HPC infrastructures significantly reduces computational load and execution times, enabling large-scale transcriptome analysis and comparison projects, while its intuitive graphical interface extends accessibility to users without IT skills.

Maboss for HPC environments: implementations of the continuous time Boolean model simulator for large CPU clusters and GPU accelerators.

BACKGROUND: Computational models in systems biology are becoming more important with the advancement of experimental techniques to query the mechanistic details responsible for leading to phenotypes of interest. In particular, Boolean models are well fit to describe the complexity of signaling networks while being simple enough to scale to a very large number of components. With the advance of Boolean model inference techniques, the field is transforming from an artisanal way of building models of moderate size to a more automatized one, leading to very large models. In this context, adapting the simulation software for such increases in complexity is crucial. RESULTS: We present two new developments in the continuous time Boolean simulators: MaBoSS.MPI, a parallel implementation of MaBoSS which can exploit the computational power of very large CPU clusters, and MaBoSS.GPU, which can use GPU accelerators to perform these simulations. CONCLUSION: These implementations enable simulation and exploration of the behavior of very large models, thus becoming a valuable analysis tool for the systems biology community.

ScRNAbox: empowering single-cell RNA sequencing on high performance computing systems.

BACKGROUND: Single-cell RNA sequencing (scRNAseq) offers powerful insights, but the surge in sample sizes demands more computational power than local workstations can provide. Consequently, high-performance computing (HPC) systems have become imperative. Existing web apps designed to analyze scRNAseq data lack scalability and integration capabilities, while analysis packages demand coding expertise, hindering accessibility. RESULTS: In response, we introduce scRNAbox, an innovative scRNAseq analysis pipeline meticulously crafted for HPC systems. This end-to-end solution, executed via the SLURM workload manager, efficiently processes raw data from standard and Hashtag samples. It incorporates quality control filtering, sample integration, clustering, cluster annotation tools, and facilitates cell type-specific differential gene expression analysis between two groups. We demonstrate the application of scRNAbox by analyzing two publicly available datasets. CONCLUSION: ScRNAbox is a comprehensive end-to-end pipeline designed to streamline the processing and analysis of scRNAseq data. By responding to the pressing demand for a user-friendly, HPC solution, scRNAbox bridges the gap between the growing computational demands of scRNAseq analysis and the coding expertise required to meet them.

kalis: a modern implementation of the Li & Stephens model for local ancestry inference in R.

BACKGROUND: Approximating the recent phylogeny of N phased haplotypes at a set of variants along the genome is a core problem in modern population genomics and central to performing genome-wide screens for association, selection, introgression, and other signals. The Li & Stephens (LS) model provides a simple yet powerful hidden Markov model for inferring the recent ancestry at a given variant, represented as an N × N distance matrix based on posterior decodings. RESULTS: We provide a high-performance engine to make these posterior decodings readily accessible with minimal pre-processing via an easy to use package kalis, in the statistical programming language R. kalis enables investigators to rapidly resolve the ancestry at loci of interest and developers to build a range of variant-specific ancestral inference pipelines on top. kalis exploits both multi-core parallelism and modern CPU vector instruction sets to enable scaling to hundreds of thousands of genomes. CONCLUSIONS: The resulting distance matrices accessible via kalis enable local ancestry, selection, and association studies in modern large scale genomic datasets.

Combined Proteomic and Phosphoproteomic Characterization of the Molecular Regulators and Functional Modules During Pancreatic Progenitor Cell Development.

Differentiated multipotent pancreatic progenitors have major advantages for both modeling pancreas development and preventing or treating diabetes. Despite significant advancements in inducing the differentiation of human pluripotent stem cells into insulin-producing cells, the complete mechanism governing proliferation and differentiation remains poorly understood. This study used large-scale mass spectrometry to characterize molecular processes at various stages of human embryonic stem cell (hESC) differentiation toward pancreatic progenitors. hESCs were induced into pancreatic progenitor cells in a five-stage differentiation protocol. A high-performance liquid chromatography-mass spectrometry platform was used to undertake comprehensive proteome and phosphoproteome profiling of cells at different stages. A series of bioinformatic explorations, including coregulated modules, gene regulatory networks, and phosphosite enrichment analysis, were then conducted. A total of 27,077 unique phosphorylated sites and 8122 proteins were detected, including several cyclin-dependent kinases at the initial stage of cell differentiation. Furthermore, we discovered that ERK1, a member of the MAPK cascade, contributed to proliferation at an early stage. Finally, Western blotting confirmed that the phosphosites from SIRT1 and CHEK1 could inhibit the corresponding substrate abundance in the late stage. Thus, this study extends our understanding of the molecular mechanism during pancreatic cell development.

Profiling Protein-Protein Interactions in the Human Brain by Refined Cofractionation Mass Spectrometry.

Proteins usually execute their biological functions through interactions with other proteins and by forming macromolecular complexes, but global profiling of protein complexes directly from human tissue samples has been limited. In this study, we utilized cofractionation mass spectrometry (CF-MS) to map protein complexes within the postmortem human brain with experimental replicates. First, we used concatenated anion and cation Ion Exchange Chromatography (IEX) to separate native protein complexes in 192 fractions and then proceeded with Data-Independent Acquisition (DIA) mass spectrometry to analyze the proteins in each fraction, quantifying a total of 4,804 proteins with 3,260 overlapping in both replicates. We improved the DIA's quantitative accuracy by implementing a constant amount of bovine serum albumin (BSA) in each fraction as an internal standard. Next, advanced computational pipelines, which integrate both a database-based complex analysis and an unbiased protein-protein interaction (PPI) search, were applied to identify protein complexes and construct protein-protein interaction networks in the human brain. Our study led to the identification of 486 protein complexes and 10054 binary protein-protein interactions, which represents the first global profiling of human brain PPIs using CF-MS. Overall, this study offers a resource and tool for a wide range of human brain research, including the identification of disease-specific protein complexes in the future.

Basal release of 6-cyanodopamine from rat isolated vas deferens and its role on the tissue contractility.

6-Cyanodopamine is a novel catecholamine released from rabbit isolated heart. However, it is not known whether this catecholamine presents any biological activity. Here, it was evaluated whether 6-cyanodopamine (6-CYD) is released from rat vas deferens and its effect on this tissue contractility. Basal release of 6-CYD, 6-nitrodopamine (6-ND), 6-bromodopamine, 6-nitrodopa, and 6-nitroadrenaline from vas deferens were quantified by LC-MS/MS. Electric-field stimulation (EFS) and concentration-response curves to noradrenaline, adrenaline, and dopamine of the rat isolated epididymal vas deferens (RIEVD) were performed in the absence and presence of 6-CYD and /or 6-ND. Expression of tyrosine hydroxylase was assessed by immunohistochemistry. The rat isolated vas deferens released significant amounts of both 6-CYD and 6-ND. The voltage-gated sodium channel blocker tetrodotoxin had no effect on the release of 6-CYD, but it virtually abolished 6-ND release. 6-CYD alone exhibited a negligible RIEVD contractile activity; however, at 10 nM, 6-CYD significantly potentiated the noradrenaline- and EFS-induced RIEVD contractions, whereas at 10 and 100 nM, it also significantly potentiated the adrenaline- and dopamine-induced contractions. The potentiation of noradrenaline- and adrenaline-induced contractions by 6-CYD was unaffected by tetrodotoxin. Co-incubation of 6-CYD (100 pM) with 6-ND (10 pM) caused a significant leftward shift and increased the maximal contractile responses to noradrenaline, even in the presence of tetrodotoxin. Immunohistochemistry revealed the presence of tyrosine hydroxylase in both epithelial cell cytoplasm of the mucosae and nerve fibers of RIEVD. The identification of epithelium-derived 6-CYD and its remarkable synergism with catecholamines indicate that epithelial cells may regulate vas deferens smooth muscle contractility.

Computational limits to the legibility of the imaged human brain.

Our knowledge of the organisation of the human brain at the population-level is yet to translate into power to predict functional differences at the individual-level, limiting clinical applications and casting doubt on the generalisability of inferred mechanisms. It remains unknown whether the difficulty arises from the absence of individuating biological patterns within the brain, or from limited power to access them with the models and compute at our disposal. Here we comprehensively investigate the resolvability of such patterns with data and compute at unprecedented scale. Across 23 810 unique participants from UK Biobank, we systematically evaluate the predictability of 25 individual biological characteristics, from all available combinations of structural and functional neuroimaging data. Over 4526 GPU*hours of computation, we train, optimize, and evaluate out-of-sample 700 individual predictive models, including fully-connected feed-forward neural networks of demographic, psychological, serological, chronic disease, and functional connectivity characteristics, and both uni- and multi-modal 3D convolutional neural network models of macro- and micro-structural brain imaging. We find a marked discrepancy between the high predictability of sex (balanced accuracy 99.7%), age (mean absolute error 2.048 years, R2 0.859), and weight (mean absolute error 2.609Kg, R2 0.625), for which we set new state-of-the-art performance, and the surprisingly low predictability of other characteristics. Neither structural nor functional imaging predicted an individual's psychology better than the coincidence of common chronic disease (p < 0.05). Serology predicted chronic disease (p < 0.05) and was best predicted by it (p < 0.001), followed by structural neuroimaging (p < 0.05). Our findings suggest either more informative imaging or more powerful models will be needed to decipher individual level characteristics from the human brain. We make our models and code openly available.

Comparative Molecular Docking of Apigenin and Luteolin versus Conventional Ligands for TP-53, pRb, APOBEC3H, and HPV-16 E6: Potential Clinical Applications in Preventing Gynecological Malignancies.

This study presents a comparative analysis of molecular docking data, focusing on the binding interactions of the natural compounds apigenin and luteolin with the proteins TP-53, pRb, and APOBEC, in comparison to conventional pharmacological ligands. Advanced bioinformatics techniques were employed to evaluate and contrast binding energies, showing that apigenin and luteolin demonstrate significantly higher affinities for TP-53, pRb, and APOBEC, with binding energies of -6.9 kcal/mol and -6.6 kcal/mol, respectively. These values suggest strong potential for therapeutic intervention against HPV-16. Conventional ligands, by comparison, exhibited lower affinities, with energies ranging from -4.5 to -5.5 kcal/mol. Additionally, protein-protein docking simulations were performed to assess the interaction between HPV-16 E6 oncoprotein and tumor suppressors TP-53 and pRb, which revealed high binding energies around -976.7 kcal/mol, indicative of their complex interaction. A conversion formula was applied to translate these protein-protein interaction energies to a comparable scale for non-protein interactions, further underscoring the superior binding potential of apigenin and luteolin. These findings highlight the therapeutic promise of these natural compounds in preventing HPV-16-induced oncogenesis, warranting further experimental validation for clinical applications.

Flour Fortification Using Lablab Purpureus Evaluation with a Biosensor.

BACKGROUND/AIMS: Due to rapid metabolic and growth rates during the first two years of life, the nutritional needs of young children are high. Given the small portion sizes consumed by children between the ages of 6 and 24 months, it is necessary to improve diets to meet the nutritional needs of this age group. Therefore, the analysis of lysine content is an important parameter in the evaluation of enriched foods. METHODS: The utilization of an enzymatic sensor employing lysine-α-oxidase (LOx) as a biorecognition element represents an alternative to the existing methods. This sensor was optimized for quantifying the lysine content in flour mixtures: Quinoa-Lablab purpureus rye - Lablab purpureus, and pole beans - Lablab purpureus, with a maximum ratio of 85g/100g. RESULTS: The addition of lablab purpureus significantly increased the lysine concentration in the enriched samples. When 30 percent was substituted in quinoa, it reached a 143 percent increase. And when 15 percent was substituted in the rye flour, the final concentration of this amino acid increased by 64 percent. In order to quantify the lysine concentration, it was necessary to optimize various parameters during the use of the sensor, e.g. a potentiometric signal was detected upon the depletion of oxygen present during the oxidation of lysine in the samples, and the sensor response was recorded at 2 s. This was possible due to the modification of the pH and the thickness of the membrane. The oxidation of lysine is catalyzed by LOx using molecular oxygen as the electron acceptor. The corresponding acidic compounds and hydrogen peroxide were formed in the reaction medium. CONCLUSION: It was possible to increase and verify the concentration of lysine in all the flours tested through the use of the biosensor, which turned out to be a valid method for controlling the nutritional quality of flours.

Improvement and prediction of the extraction parameters of lupeol and stigmasterol metabolites of Melia azedarach with response surface methodology.

BACKGROUND: Melia azedarach is known as a medicinal plant that has wide biological activities such as analgesic, antibacterial, and antifungal effects and is used to treat a wide range of diseases such as diarrhea, malaria, and various skin diseases. However, optimizing the extraction of valuable secondary metabolites of M. azedarach using alternative extraction methods has not been investigated. This research aims to develop an effective, fast, and environmentally friendly extraction method using Ultrasound-assisted extraction, methanol and temperature to optimize the extraction of two secondary metabolites, lupeol and stigmasterol, from young roots of M. azedarach using the response surface methodology. METHODS: Box-behnken design was applied to optimize different factors (solvent, temperature, and ultrasonication time). The amounts of lupeol and stigmasterol in the root of M. azedarach were detected by the HPLC-DAD. The required time for the analysis of each sample by the HPLC-DAD system was considered to be 8 min. RESULTS: The results indicated that the highest amount of lupeol (7.82 mg/g DW) and stigmasterol (6.76 mg/g DW) was obtained using 50% methanol at 45 °C and ultrasonication for 30 min, and 50% methanol in 35 °C, and ultrasonication for 30 min, respectively. Using the response surface methodology, the predicted conditions for lupeol and stigmasterol from root of M. azedarach were as follows; lupeol: 100% methanol, temperature 45 °C and ultrasonication time 40 min (14.540 mg/g DW) and stigmasterol 43.75% methanol, temperature 34.4 °C and ultrasonication time 25.3 min (5.832 mg/g DW). CONCLUSIONS: The results showed that the amount of secondary metabolites lupeol and stigmasterol in the root of M. azedarach could be improved by optimizing the extraction process utilizing response surface methodology.

Ichor: A Python library for computational chemistry data management and machine learning force field development.

We present ichor, an open-source Python library that simplifies data management in computational chemistry and streamlines machine learning force field development. Ichor implements many easily extensible file management tools, in addition to a lazy file reading system, allowing efficient management of hundreds of thousands of computational chemistry files. Data from calculations can be readily stored into databases for easy sharing and post-processing. Raw data can be directly processed by ichor to create machine learning-ready datasets. In addition to powerful data-related capabilities, ichor provides interfaces to popular workload management software employed by High Performance Computing clusters, making for effortless submission of thousands of separate calculations with only a single line of Python code. Furthermore, a simple-to-use command line interface has been implemented through a series of menu systems to further increase accessibility and efficiency of common important ichor tasks. Finally, ichor implements general tools for visualization and analysis of datasets and tools for measuring machine-learning model quality both on test set data and in simulations. With the current functionalities, ichor can serve as an end-to-end data procurement, data management, and analysis solution for machine-learning force-field development.

An Ultra-Flexible Sodium-Ion Full Cell with High Energy/Power Density and Unprecedented Structural Stability.

Futuristic wearable electronics desperately need power sources with similar flexibility and durability. In this regard, the authors, therefore, propose a scalable PAN-PMMA blend-derived electrospinning protocol to fabricate free-standing electrodes comprised of cobalt hexacyanoferrate nanocube cathode and tin metal organic framework-derived nanosphere anode, respectively, for flexible sodium-ion batteries. The resulting unique inter-networked nanofiber mesh offers several advantages such as robust structural stability towards repeated bending and twisting stresses along with appreciable electronic/ionic conductivity retention without any additional post-synthesis processing. The fabricated flexible sodium ion full cells deliver a high working voltage of 3.0 V, an energy density of 273 Wh·kg-1 , and a power density of 2.36 kW·kg-1 . The full cells retain up to 86.73% of the initial capacity after 1000 cycles at a 1.0 C rate. After intensive flexibility tests, the full cells also retain 78.26% and 90.78% of the initial capacity after 1000 bending and twisting cycles (5 mm radius bending and 40o axial twisting), respectively. This work proves that the proposed approach can also be employed to construct similar robust, free-standing nanofiber mesh-based electrodes for mass-producible, ultra-flexible, and durable sodium ion full cells with commercial viability.

Oxygen Vacancies on NH4 V4 O10 Accelerate Ion and Charge Transfer in Aqueous Zinc-Ion Batteries.

Vanadium-based compounds are identified as promising cathode materials for aqueous zinc ion batteries due to their high specific capacity. However, the low intrinsic conductivity and sluggish Zn2+ diffusion kinetics seriously impede their further practical application. Here, oxygen vacancies on NH4 V4 O10 is reported as a high-performing cathode material for aqueous zinc ion batteries via a facile hydrothermal strategy. The introduction of oxygen vacancy accelerates the ion and charge transfer kinetics, reduces the diffusion barrier of zinc ions, and establishes a stable crystal structure during zinc ion (de-intercalation). As a result, the oxygen vacancy enriched NH4 V4 O10 exhibits a high specific capacity of ≈499 mA h g-1 at 0.2 A g-1 , an excellent rate capability of 296 mA h g-1 at 10 A g-1 and the specific capacity cycling stability with 95.1% retention at 5 A g-1 for 4000 cycles, superior to the NVO sample (186.4 mAh g-1 at 5 A g-1 , 66% capacity retention).

Mechanically Ultra-Robust Fluorescent Elastomer for Elaborating Auxetic Composite.

Fluorescent elastomers are predominantly fabricated through doping fluorescent components or conjugating chromophores into polymer networks, which often involves detrimental effects on mechanical performance and also makes large-scale production difficult. Inspired by the heteroatom-rich microphase separation structures assisted by intensive hydrogen bonds in natural organisms, an ultra-robust fluorescent polyurethane elastomer is reported, which features a remarkable fracture strength of 87.2 MPa with an elongation of 1797%, exceptional toughness of 678.4 MJ m-3 and intrinsic cyan fluorescence at 445 nm. Moreover, the reversible fluorescence variation with temperature could in situ reveal the microphase separation of the elastomer in real time. By taking advantage of mechanical properties, intrinsic fluorescence and hydrogen bonds-promoted interfacial bonding ability, this fluorescent elastomer can be utilized as an auxetic skeleton for the elaboration of an integrated auxetic composite. Compared with the auxetic skeleton alone, the integrated composite shows an improved mechanical performance while maintaining auxetic deformation in a large strain below 185%, and its auxetic process can be visually detected under ultraviolet light by the fluorescence of the auxetic skeleton. The concept of introducing hydrogen-bonded heteroatom-rich microphase separation structures into polymer networks in this work provides a promising approach to developing fluorescent elastomers with exceptional mechanical properties.

Lattice Strain Regulation and Halogen Vacancies Passivation Enable High-Performance Formamidine-Based Perovskite Solar Cells.

Formamidinium lead iodide (FAPbI3) perovskite has lately surfaced as the preferred contender for highly proficient and robust perovskite solar cells (PSCs), owing to its favorable bandgap and superior thermal stability. Nevertheless, volatilization and migration of iodide ions (I-) result in non-radiating recombination centers, and the presence of large formamidine (FA) cations tends to cause lattice strain, thereby reducing the power conversion efficiency (PCE) and stability of PSCs. To solve these problems, the lead formate (PbFa) is added into the perovskite solution, which effectively mitigates the halogen vacancy and provides tensile strain outside the perovskite lattice, thereby enhancing its properties. The strong coordination between the C═O of HCOO- and Pb-I backbones effectively immobilizes anions, significantly increases the energy barrier for anion vacancy formation and migration, and reduces the risk of lead ion (Pb2+) leakage, thereby improving the operation and environmental safety of the device. Consequently, the champion PCE of devices with Ag electrodes can be increased from 22.15% to 24.32%. The unencapsulated PSCs can still maintain 90% of the original PCE even be stored in an N2 atmosphere for 1440 h. Moreover, the target devices have significantly improved performance in terms of light exposure, heat, or humidity.