BackgroundThe application of artificial intelligence (AI) in education is undergoing rapid advancements, with models such as ChatGPT-4 showing potential in medical education. This study aims to evaluate the proficiency of ChatGPT-4 in answering Saudi Medical Licensing Exam (SMLE) questions.MethodologyA dataset of 220 questions across four medical disciplines was used. The model was trained using a specific code to answer the questions accurately, and its performance was assessed using key performance indicators, difficulty level, and exam sections.ResultsEasy and Average questions, but accuracy decreased for Hard questions. Performance was consistent across all disciplines, indicating a broad knowledge base. However, an error analysis revealed areas for further refinement, particularly with category (Option) A questions across all sections.ChatGPT-4 demonstrated an overall accuracy of 88.6%. It showed high proficiency with ConclusionsThis study underscores the potential of ChatGPT-4 as an AI-assisted tool in medical education, demonstrating high proficiency in answering SMLE questions. Future research is recommended to expand the scope of training and evaluation as well as to enhance the model’s performance on complex clinical questions. OpenAI introduced ChatGPT, a comprehensive large language model (LLM), to the public in November 2022. Often referred to as GPT-3.5, this model underwent training on vast text datasets using both supervised and unsupervised learning methods and then refined using reinforcement learning based on human input. A distinguishing feature of ChatGPT is its inability to browse the web, setting it apart from other chatbots with internet access. While OpenAI’s proprietary version of ChatGPT has online querying capabilities, the public version does not currently offer this feature, though it is anticipated in forthcoming releases. On March 14, 2023, OpenAI unveiled GPT-4, another LLM trained in a manner consistent with its previous model [Lately, several teams have assessed ChatGPT’s efficacy on national medical licensing tests, encompassing exams from countries such as China, Japan, and Germany -8. AlthoThe Saudi Medical Licensing Exam (SMLE) is a comprehensive evaluation mechanism implemented by the Saudi Commission for Health Specialties to qualify physicians, whether domestic or international, to practice medicine in Saudi Arabia, or to permit postgraduate medical students to apply for local residency programs. It is a compulsory examination for all prospective doctors during their internship as part of their bachelor’s degree. The exam’s structure was created by the SMLE steering committee, which includes members from the Saudi Medical College Deans. The exam is designed to test a specific learning domain, encompassing not only knowledge but also cognitive abilities  and attitude . The SMLThe objective of this study is to evaluate the performance of ChatGPT-4, a state-of-the-art AI language model developed by OpenAI, in answering questions from the SMLE. This assessment aims to understand the model’s accuracy, proficiency, and reliability in interpreting and responding to complex medical questions that are used to gauge the competency of medical graduates in Saudi Arabia. The results from this study will offer insights into the potential applications of using advanced AI models such as ChatGPT-4 in medical education and testing.This study was designed to evaluate the efficacy of the ChatGPT-4 model in answering multiple-choice questions (MCQs) with four options, i.e., A, B, C, and D, from the SMLE.Data collectionhttps://canadaqbank.com/smle.php). This dataset was accessed on July 9, 2023. Four researchers collaborated in the data collection process to ensure comprehensive coverage of questions and eliminate potential bias in question selection. The questions were derived from various sections of the SMLE, including Medicine, Pediatrics, Obstetrics and Gynecology, and Surgery, to ensure diversity in subject matter. Each question was accompanied by four potential answers , with only one correct response.A total of 220 questions were extracted from the SMLE test bank available on the CanadaQBank website . This decision was made to ensure that our study accurately reflected the conditions and challenges of the real examination. Furthermore, the sections from which we distributed were as follows: 30% of the questions were from Medicine, 25% were from Obstetrics and Gynecology, 25% were from Pediatrics, and the remaining 20% were from Surgery. The questions were chosen based on the guidelines provided in the Commission’s handbook . This apModel training and evaluationThe ChatGPT 4.0 model was trained using the given codes, which defined its role as a medical professional with the knowledge required to answer SMLE questions accurately and coherently. We gave ChatGPT 4.0 the following training code: “You are a medical professional, I will give you Saudi Medical License Exam (SMLE) questions and four multiple choice for each question. You have the knowledge required to choose one single answer in an accurate, coherent manner. I will provide you with the questions from multiple tests on various topics such as medicine, surgery, obstetrics and gynecology, and pediatrics. I will provide you with the questions and I want you to choose the correct answer.” Without this training code, the ChatGPT 4.0 model would not be able to answer the questions accurately. Instead, it might default to its general behavior, which includes referring the user to a specialist doctor for consultation. This training code is crucial as it provides the model with specific information about the nature of the questions and their source, and instructs it to select one correct answer from the four options provided. This prompt provided the model with information about the nature of the questions and their source. It also directed the model to select one correct answer from the four options provided.Following the training phase, the model was evaluated on its ability to select the correct answer for each question. Performance metrics, such as accuracy, precision, and recall, were computed to assess the model’s overall performance. The model’s performance was further evaluated across different question difficulty levels and sections.Statistical analysisData analysis was performed using SPSS, version 26.0 . Given the descriptive nature of our study, we primarily focused on descriptive statistics to gain insights from our dataset. Specifically, frequencies and percentages were calculated for all categorical variables to offer a comprehensive understanding of the distribution and characteristics of the data.We calculated key performance indicators to gauge the model’s performance. The metrics we focused on included accuracy and precision for each response category . Accuracy was determined as the proportion of total correct answers provided by the model out of all the questions it attempted. This gave us a general sense of how often the model was correct in its predictions. Precision, on the other hand, was calculated for each category. It represented the ratio of true positives  to all instances that the model classified as positive for that category. This gave us an understanding of how precise the model was in its predictions for each specific category.Easy, Average, and Hard question categories. This analysis was intended to discern any possible correlation between the intricacy of a question and the model’s propensity to answer it correctly. Finally, the performance was assessed according to the different sections of the SMLE. This analysis revealed how well the model responded to questions related to Medicine, Pediatrics, Obstetrics and Gynecology, and Surgery, thus providing an indication of its strengths and areas for improvement in different medical disciplines. Through these detailed statistical analyses, a comprehensive understanding of ChatGPT-4’s capabilities in answering SMLE questions was attained.Furthermore, an error analysis was undertaken to discern the types and frequencies of mistakes made by the model. This was crucial in pinpointing the specific areas that might benefit from further refinement. We also evaluated the model’s performance based on the difficulty level of the questions. We computed the mean accuracy for the We utilized a dataset comprising 220 questions, divided as per the SMLE blueprint, to evaluate the performance of the ChatGPT-4 model. The results of this evaluation are organized by key performance indicators, performance across different levels of question difficulty, and performance based on the section of the exam.ChatGPT’s key performance indicatorsThe overall success rate of ChatGPT-4 in correctly answering the questions was found to be 88.6%. We also measured how well ChatGPT-4 did for each question category (options), A, B, C, and D. The performance of the ChatGPT-4 model was evaluated across four response options . For option A, out of 52 instances, the model correctly answered 43 (82.7%) questions and incorrectly answered nine 17.3%). In the case of option B, out of 45 instances, the model correctly answered 42 (93.3%) questions and incorrectly answered three (6.7%). For option C, out of 42 instances, the model correctly answered 37 (88.1%) questions and incorrectly answered five (11.9%). Lastly, for option D, out of 81 instances, the model correctly answered 76 (93.8%) questions and incorrectly answered five (6.2%). These results provide a comprehensive understanding of the model’s performance across the different response categories  difficulty-level questions, out of 53 instances, the model correctly answered 50 questions, resulting in a success rate of 94.3%. It incorrectly answered three questions, yielding an error rate of 5.7%. For Easy difficulty-level questions, the model showed exceptional performance. Out of 54 instances, it correctly answered all questions, achieving a success rate of 100%. For Hard difficulty-level questions, out of 113 instances, the model correctly answered 94 questions, achieving a success rate of 83.2%. It incorrectly answered 19 questions, resulting in an error rate of 16.8% (Figure We next examined how ChatGPT performed for questions of different difficulty levels. To ascertain the difficulty level of each question, we turned to the historical performance metrics provided by the CanadaQBank website. For every question within their database, CanadaQBank not only indicates the proportion of users who answered the question correctly but also categorizes the questions based on this success rate. This categorization, rooted in real-world performance, serves as a reflection of each question’s complexity. CanadaQBank’s method ensures that the difficulty categorization of each question is based on actual user performance, eliminating subjective biases and offering a consistent, objective measure of a question’s challenge level. For Performance based on exam sectionWe then examined how well ChatGPT-4 did based on the exam section the questions came from. The model did well across all sections. In the Medicine section, out of 66 instances, the model correctly answered 57 questions. It incorrectly answered nine questions, yielding an error rate of 13.6%. In the Obstetrics and Gynecology section, out of 55 instances, the model correctly answered 53 questions. It incorrectly answered two questions, resulting in an error rate of 3.6%. In the Pediatrics section, out of 55 instances, the model correctly answered 49 questions. It incorrectly answered six questions, yielding an error rate of 10.9%. In the Surgery section, out of 44 instances, the model correctly answered 39 questions. It incorrectly answered five questions, yielding an error rate of 11.4% (Figure Error analysisFinally, we examined the types of errors made by ChatGPT-4. An error analysis was conducted to identify the types and frequencies of errors committed by the ChatGPT-4 model across different sections and response categories.In the Medicine section, the model made errors primarily in category A, with an error rate of 26.7%. Categories C and D also saw error rates of 13.3% and 11.5%, respectively. Category B had no errors. In the Obstetrics and Gynecology section, the model made a few errors in categories B and C, with error rates of 8.3% and 7.1%, respectively. There were no errors in categories A and D. In the Pediatrics section, the model made the most errors in category A, with an error rate of 20%. Categories B, C, and D saw fewer errors, with rates of 6.7%, 12.5%, and 5.9%, respectively. In the Surgery section, the model made errors primarily in category A, with an error rate of 16.7%. Categories B and C also saw errors, with rates of 12.5% and 20.0%, respectively. Category D had a low error rate of 5.3% Figure .This error analysis provides insights into specific areas where the model could benefit from further refinement. It appears that the model struggles the most with category A questions across all sections, suggesting that future training could focus on improving performance in this area.Our findings demonstrate the substantial potential of ChatGPT in assisting medical education, particularly in the context of preparing for exams such as the SMLE. This study presents a comprehensive evaluation of ChatGPT’s performance on a set of 220 SMLE questions, covering four major medical disciplines, namely, Medicine, Pediatrics, Obstetrics and Gynecology, and Surgery.Easy and Average questions suggests that it could be a valuable asset for foundational learning and ongoing review. As we continue to refine and train the model, we anticipate that its applicability in medical education and assessment will broaden, eventually revolutionizing the way we prepare for exams and engage in lifelong learning.The overall accuracy of the model was 88.6%, showing a high degree of proficiency in answering the SMLE questions correctly. While our study showcases the high potential of ChatGPT-4 as a supplementary educational tool, the margin of error of 4-16% emphasizes that it should be used cautiously and in conjunction with traditional study methods and expert consultation. Given the present limitations, ChatGPT-4 may not yet be ready for standalone use in high-stakes exam preparation or medical decision-making. However, its high level of accuracy in answering Easy and Average questions, suggesting a strong grasp of foundational medical knowledge. However, the accuracy decreased for Hard questions, indicating that the model might benefit from additional training on complex clinical scenarios or rare diseases. When analyzed based on the subject matter of questions, the model’s performance was fairly consistent across Medicine, Pediatrics, Obstetrics and Gynecology, and Surgery. This suggests that the model has been successful in acquiring a broad knowledge base in diverse medical fields, supporting its utility as a comprehensive learning tool. The integration of AI into medical education, as noted by Davenport and Kalakota [Our observations are consistent with the findings of Kung et al. , who conKalakota , has sigKalakota . The preOur study does have certain limitations. First, our study trained ChatGPT-4 using a limited number of SMLE questions. Increasing the volume of questions for training might improve the model’s performance. Second, while our study included scenario-based MCQs, it did not incorporate other formats like open-ended or short-answer questions. Therefore, our findings may not be fully generalizable to assessments that use diverse question formats. Third, we evaluated the performance of ChatGPT-4 on questions from only four medical disciplines. Including questions from other medical and surgical sub-specialties could provide a more comprehensive understanding of the model’s capability.Hard questions suggests it may benefit from exposure to more complex and nuanced medical datasets. By training ChatGPT with a higher proportion of intricate clinical scenarios, it may become better attuned to understanding and answering challenging questions. Incorporation of Feedback Loops: Implementing a feedback mechanism where medical professionals can review and correct the model’s outputs, especially on the Hard questions it gets wrong, can help it learn from its mistakes. Over time, this iterative feedback can help the model improve its accuracy in identifying the nuances of tougher questions. The model demonstrated high accuracy with Easy and Average questions but had reduced accuracy with Hard questions. Analysis of the model’s performance by subject matter indicated consistent efficacy across all four disciplines. The significance of this study extends beyond merely quantifying the performance of an AI language model in answering medical examination questions. In the field of medical education, there is a growing need for innovative, scalable, and time-efficient assessment tools, particularly for formative assessments that facilitate learning in addition to measuring performance. Our study’s implications are broad and relevant for both medical students and educators. For students, ChatGPT-4 can act as a real-time, supplementary self-assessment tool that helps identify both strengths and areas for improvement. For educators, the model offers a highly accurate and efficient means to design and validate assessments. While promising, we recommend additional studies and refinements before this technology is widely incorporated into medical education and assessments. Its high accuracy in Easy and Average questions could be leveraged to create baseline assessments, freeing educators to focus on designing more complex and nuanced questions to challenge their students. The study opens a promising avenue for further research into how AI-assisted tools can be effectively integrated into medical education and assessment practices. While our findings indicate strong potential, we recommend additional refinement and validation before the model can be widely adopted in educational settings.This study evaluated the performance of ChatGPT-4, an AI language model, in answering SMLE questions. The study involved a set of 220 SMLE questions encompassing four medical disciplines, namely, Medicine, Pediatrics, Obstetrics and Gynecology, and Surgery. Statistical analyses using R software revealed that the overall accuracy of ChatGPT-4 in answering the questions was 88.6%. An error analysis identified areas where the model could benefit from further refinement. Here are some potential pathways for refinement: Focused Training on Complex Scenarios: The model’s reduced accuracy with


Create a timeline of diagnosis and treatment for IPF in the US.A retrospective analysis was performed in collaboration with the OptumLabs Data Warehouse using an administrative claims database of Medicare Fee for Service beneficiaries. Adults 50 and over with IPF were included (2014 to 2019).To focus on IPF, the following diagnoses were excluded: post-inflammatory fibrosis, hypersensitivity pneumonitis, rheumatoid arthritis, sarcoidosis, scleroderma, and connective tissue disease.Data were collected from periods prior, during, and following initial clinical diagnosis of IPF. This included prior respiratory diagnoses, number of respiratory-related hospitalizations, anti-fibrotic and oxygen use, and survival.A total of 44,891 with IPF were identified. The most common diagnoses prior to diagnosis of IPF were upper respiratory infections (47%), acute bronchitis (13%), other respiratory disease (10%), chronic obstructive pulmonary disease and bronchiectasis (7%), and pneumonia (6%). The average time to a diagnosis of IPF was 2.7 years after initial respiratory diagnosis. Half of patients had two or more respiratory-related hospitalizations prior to IPF diagnosis. Also, 37% of patients were prescribed oxygen prior to diagnosis of IPF. These observations suggest delayed diagnosis. We also observed only 10.4% were treated with anti-fibrotics. Overall survival declined each year after diagnosis with median survival of 2.80 years.Our retrospective cohort demonstrates that IPF is often diagnosed late, usually preceded by other respiratory diagnoses and hospitalizations. Use of available therapies is low and outcomes remain poor.The online version contains supplementary material available at 10.1186/s12890-023-02565-7. Idiopathic pulmonary fibrosis (IPF) is the most common type of fibrosing interstitial lung disease affecting an estimated 2.8–9.3 cases per 100,000 people per year [Differentiating IPF from other causes of ILD is important as it can lead to different approaches to treatment and altered prognosis . TreatmeAs it is widely held that patients with IPF experience diagnostic delays based on data gathered from academic centers, we sought to describe the timeline to diagnosis and initiation of treatment for patients with IPF using real world datasets. Our retrospective cohort analysis is unique because we used data from the Center for Medicare Services Fee for Service database, which consists of over 50,000 patients with IPF across the United States. Therefore, the current study aimed to investigate the identified patients’ initial respiratory diagnosis, subsequent time to clinical diagnosis of IPF, timing of oxygen initiation and prescription of anti-fibrotic agents. We also obtained information regarding respiratory-related hospitalizations and survival.To refine our IPF code identification strategy, an initial local cohort analysis of 200 consecutive patients coded for IPF were first analyzed by medical record review. This retrospective cohort study included patients > 18 years of age who were present within our institution’s electronic record system between 2011 and 2019. ICD 9 and 10 codes for IPF (516.3 and J84.122) were used to identify the validation study cohort. Each patient’s clinical history, laboratory results (including serologies for connective tissue disease and hypersensitivities), exposure history, CT scan pattern, lung biopsy results, and competing diagnoses were reviewed independently by three physicians , and consensus diagnoses derived  data. The database contains longitudinal health information on enrollees, representing a diverse mixture of ages, ethnicities, and geographical regions across the United States . Since tWe included all adult patients 50 years or older who had their first coded clinical diagnosis of IPF between January 1, 2014, and December 31, 2019. We then constructed an incident cohort by requiring individuals to have five years of continuous enrollment prior to their IPF diagnosis. IPF was identified using the following International Classification of Diseases, Ninth Edition (ICD-9) diagnosis codes: 516.31; and International Classification of Diseases, 10th Edition (ICD-10) diagnosis codes: J84.112, as previously described [Descriptive variables of interest at index IPF diagnosis included age, gender, race/ethnicity, region of residence, and history of smoking . We further evaluated respiratory-related diagnoses and hospitalizations at baseline and after IPF diagnosis. Respiratory-related hospitalizations were identified using AHRQ’s Clinical Classification Software .The data from this observational cohort analysis are reported using descriptive statistics including frequencies and percentages or average mean values. We used the Fisher’s exact test for multiple group comparisons . In addiThe funding source for this study played no role in study design; collection, analysis, or interpretation of the data; writing of the report; and decision to submit this paper for publication.Our initial local cohort analysis described above allowed us to exclude other competing diagnoses and focus more specifically on IPF  of patients with IPF had other initial respiratory diagnosis in their baseline period before the index diagnosis of IPF. The most common respiratory-related diagnoses were upper respiratory infections (47%), acute bronchitis 13%), other upper respiratory disease (10%), chronic obstructive pulmonary disease and bronchiectasis (7%), and pneumonia (6%). Additional diagnoses can be found in Table %, other The average time from initial respiratory-related hospitalization to diagnosis of IPF was 786 days or 2.2 years. Most patients with IPF were hospitalized with a respiratory-related diagnosis between baseline and follow-up . A majority had a respiratory-related hospitalization prior to a diagnosis of IPF  and many had additional hospitalizations after their diagnosis of IPF . Furthermore, many patients had multiple hospitalizations.  used oxygen at some point in their disease course. Overall, 37% of the cohort received oxygen prior to their diagnosis of IPF , with the remainder initiating oxygen therapy either on or after their diagnosis of IPF . For those who initiated oxygen use prior to their IPF diagnosis, the average time between first oxygen use and IPF diagnosis was 814 days or 2.2 years Fig. . Those wSurvival declined drastically each year after respiratory hospitalizations, with an approximately 50% survival rate 2 years after the first respiratory related hospitalizations Fig. . The medIt has been suggested that the diagnosis of IPF has been increasing in prevalence over time . DespiteOur examined data (from 2014 to 2019) found a total number of patients with IPF = 44,891, yielding a prevalence estimate of 3 per 100,000 persons, per year. However, our cohort of patients with IPF  is certainly a significant underestimate of the total numbers with IPF. There are several reasons that indicate that the total of 44,891 patients in our cohort is an underestimate of the total numbers of patients with IPF. First, we required five years of consecutive enrollment and data to better understand the diagnostic timeline. In addition, we employed a comprehensive exclusion of competing diagnoses based upon our initial review of complete records on consecutive patients in our local electronic health records. Indeed, the available data initially searched yield over 90,00 patients with a code for IPF. However, our intent was to have a rigorously defined cohort of IPF with adequate timeline data to analyze diagnostic delays, treatment use, and survival. For these reasons, this estimate of IPF prevalence cannot be directly compared to other studies .Our data demonstrated that many patients received other respiratory diagnoses prior to their coded clinical diagnosis of IPF. The majority of these were pulmonary fibrosis unspecified, post-inflammatory fibrosis, chronic obstructive pulmonary disease unspecified, and interstitial pulmonary disease unspecified. It is also interesting that 19% of patients had three or more respiratory-related hospitalizations prior to diagnosis as well. Furthermore, roughly 37% of patients were already prescribed oxygen at the time their coded IPF diagnosis, with 27% of those receiving oxygen having been prescribed this therapy for 4 or more years prior to their diagnosis. These observations further support the conclusion that the diagnosis of IPF is rendered extremely late in the disease course.It is concerning that only a small percentage of patients ultimately received anti-fibrotic treatment. Prior studies indicate that pirfenidone and nintedanib use is associated with slowing of lung function deterioration, as well as reduced short-term mortality and reduction in hospitalizations –6, 12. IPrevious studies have also suggested gender differences in the initiation of anti-fibrotic therapy, with women less likely to receive anti-fibrotic therapy compared to male patients with IPF , 12. In The prognosis for IPF from this real-world patient cohort was similar to other cohorts described, with a mean 2.88-year prognosis following clinical diagnosis . This agOur stated goal was to evaluate the timeline to diagnosis and therapy in patients with IPF. To accomplish this, we excluded patients with coexisting diagnostic codes for other fibrotic lung diseases including chronic hypersensitivity pneumonitis and connective tissue disease-associated lung fibrosis (Supplementary Figure Additional studies using different methodologies have also documented delays in the diagnosis of interstitial lung disease and idiopathic pulmonary fibrosis. The INTENSITY study reported a survey of 600 subjects with diagnosed interstitial lung disease, finding that over half of patients had received at least one misdiagnosis, with delay to the current diagnosis occurring a median of seven months after the onset of symptoms and with 43% being delayed over a year . AnotherThis study does have several limitations. In a claims-based cohort dataset, we have no way to review the clinical notes and records to determine whether the medical team considered IPF earlier than when the code appeared in the claims-based record system. Furthermore, the respiratory related hospitalizations were determined using AHRQ software. We do not claim or report that the hospitalizations were solely due to IPF. They could have been coded as due to pneumonia, asthma exacerbation, post inflammatory fibrosis, COPD, or other diagnoses. It is not possible to retrospectively determine whether the hospitalizations were due to IPF alone from review of this claims-based data. That noted, we employed the greatest precision possible to determine the timing of diagnosis of IPF and related clinical events such as oxygen use, antifibrotics, hospitalizations and death.In addition, as an observational retrospective dataset review, our findings rely on the validity of accurate recording and diagnostic coding and miscoding is a possibility which would impact our analysis. To mitigate this, we performed a local cohort code validation study to exclude concurrent competing codes to refine our study group to patients with IPF. Despite our concerted efforts to refine our study cohort, it remains possible that some patients with competing fibrotic diagnoses such as fibrotic NSIP, may have been included. Furthermore, as a descriptive survey study, no control group was required or reported and confounding factors could have impacted the results. We would also note that it also remains possible that some of prior respiratory diagnoses did represent simple infections rather than a true misdiagnosis of IPF. Finally, our analysis was limited to OptumLabs and Medicare Fee for Service data and may not be generalizable to patients that are uninsured or included under Medicaid based coverage. Our analysis was therefore limited to the United States and may not be generalizable to other parts of the world as access to healthcare and medication costs differ greatly across different geographic settings.Despite increasing knowledge regarding the risk factors and available treatments for IPF, delays in the diagnosis of this disorder and the initiation of therapy persist. Furthermore, prognosis remains poor following diagnosis. In addition, anti-fibrotic therapy is underutilized in our cohort. We hope to reduce the delay to IPF diagnosis and treatment initiation by increasing the awareness of care of IPF patients and by enhancing education for providers on the appropriate diagnostic approach.Below is the link to the electronic supplementary material.Supplementary Material 1


Idiopathic pulmonary fibrosis (IPF) is a chronic lung disease with a very poor prognosis as it has a 2.5 to 5 years mean survival after proper diagnosis. Even nintedanib and pirfenidone cannot halt the progression, though they slow the progression of IPF. Hence, there is a need to understand the novel pathophysiology. Phospholipase A2 (PLA2) could be the ideal candidate to study in IPF, as they have a role in both inflammation and fibrosis. In the present study, we have shown the expression profile of various secretory Phospholipase A2 (PLA2) isoforms by analyzing publicly available transcriptome data of single cells from the lungs of healthy individuals and IPF patients. Among 11 members of sPLA2, PLA2G2A is found to be increased in the fibroblasts and mesothelial cells while PLA2G5 is found to be increased in the fibroblasts of IPF patients. We identified a subset of fibroblasts expressing high PLA2G2A with moderate expression of PLA2G5 and which are specific to IPF only; we named it as PLA2G2A+ IPF fibroblast. Pathway analysis revealed that these PLA2G2A+ IPF fibroblast have upregulation of both inflammatory and fibrosis-related pathways like the TGF-β signaling pathway, IL-17 signaling, the arachidonic acid metabolism pathway and ECM-receptor interaction. In addition to this, we found elevated levels of sPLA2-IIA in plasma samples of IPF patients in our cohort. PLA2G3, PLA2G10 and PLA2G12B are found in to be increased in certain epithelial cells of IPF patients. Thus, these findings indicate that these five isoforms have a disease-dominant role along with innate immune roles as these isoforms are found predominantly in structural cells of IPF patients. Further, we have targeted sPLA2 in mice model of bleomycin-induced lung fibrosis by pBPB, a known sPLA2 inhibitor. pBPB treatment attenuated lung fibrosis induced by bleomycin along with a reduction in TGF-β and deposition of extracellular matrix in lung. Thus, these findings indicate that these sPLA2 isoforms especially PLA2G2A may serve as a therapeutic target in lung fibrosis. Idiopathic pulmonary fibrosis (IPF) is a gradually progressive lung deteriorating disease with a very poor prognosis, with mere 2.5 to 5 years mean survival. IPF mostly affects older individuals; however, various studies had found that IPF may affect young individuals. IPF was considered an inflammatory disease principally. In contrast, recent literature based on clinical and experimental evidences emphasized that it could be an epithelial-driven disease ,2,3. The2+ independent (iPLA2) and lipoprotein associated phospholipase A2, based on biochemical characteristics . The 73(a)EPCAM+ epithelial cells (clusters 1 and 4),(b)PTPRC+ immune cells ,(c)PECAM1+ and EPAS1+ endothelial cells (cluster 3) and(d)EPCAM- PTPRC- and PECAM1- mesenchymal cells (cluster 5).Cells were broadly annotated based on the expression of markers and the Seurat object was then split into four subgroups :(a)EPCAMFor each subgroup Seurat object, a similar dimensional reduction, clustering and UMAP visualization approach were applied to get individual UMAP for each subgroup. Each cluster was annotated manually based on the expression of cell type markers. For integrative analysis, all the four annotated subgroup Seurat objects were merged into one large Seurat object followed by SCTransform normalization, scaling, dimensional reduction and UMAP visualization. After merging, using Seurat package in R, we observed 21 cell type identities   Figure A, and alFrom the above results, it is clear that PLA2G2A is expressed in mesenchymal cells, particularly in fibroblasts and mesothelial cells whereas PLA2G5 is expressed in fibroblasts. However, we wanted to study the expressions of PLA2G2A and PLA2G5 at the single cell level in the mesenchymal cell subgroup Seurat object of IPF patients compared to controls. We observed six sub clusters after re-analysis of mesenchymal cells subgroup Seurat object A. Based The pathway enrichment analysis revealed a significant upregulation of TGF-β signaling pathway, IL−17 signaling, arachidonic acid metabolism pathway, ECM-receptor interaction and other fibrosis-related pathways indicating the active role of PLA2G2A+ IPF fibroblasts in IPF . In addiWe wanted to validate the existence of PLA2G2A+ IPF fibroblast in other datasets that are publicly available as mentioned in Methods. In the second dataset  , we studIn the third dataset   also, weWe found expression of PLA2G1B, PLA2G3, PLA2G10 and PLA2G12A in epithelial cells, therefore the differential expression of PLA2G1B, PLA2G3,PLA2G10 and PLA2G12A were assessed in epithelial cells by dot plot. Further sub-clustering of epithelial cells results in six sub-clusters A. Based We selected sPLA2-IIA for validation of single cell RNA seq data analysis, as we found predominant and increased expression of PLA2G2A (sPLA2-IIA) in IPF patients as compared to healthy individuals. There were 20 healthy controls and 10 IPF patients , and we We found increased expression of various sPLA2 in IPF patients as compared to healthy individuals while most of the sPLA2 has a pro-inflammatory role and the end stage of a number of inflammatory conditions is fibrosis ,42,43. TAs we found increased expression of PLA2-IIA in the sera of IPF patients and increased activity of sPLA2 in lungs of bleomycin-treated mice, we wanted to study the effect of inhibition of sPLA2 by pBPB. Bleomycin-administered mice were treated with pBPB  from Day 12–Day 21 as shown in The entire PLA2 superfamily has more than 30 members; among them, there are 11 isoforms of sPLA2  . HoweverIn this regard, we found relatively unique and specific expression of PLA2G2A in a subset of fibroblast (PLA2G2A+ IPF fibroblast) that is very specific to IPF A–D and aFurther, sPLA2-IIA has been shown to be linked to the degradation of surfactant proteins that are rich in phosphatidylglycerol, which is one of the favorable substrate for sPLA2-IIA . This insPLA2-IIA has been shown to stimulate cardiac fibroblasts to transdifferentiate into myofibroblasts through EGFR activation . HoweverWe found increased expression of PLA2G5 in PLA2G2A+ IPF fibroblast population E and thiA number of studies have indicated that airway epithelium act as an innate cell as it has to face the external environment, and the innate immune role of fibroblasts might be interesting as they are located just beneath the basement membrane and away from the external environment compared to epithelial cells. More investigations are required to find the possible mechanisms for the upregulation of sPLA2-IIA in IPF patients and to see whether sPLA2-IIA can be taken as an effective therapeutic target to prevent the lung fibrosis.KRT5−/KRT17+ cells are recently identified basal-like epithelial cell phenotype, highly enriched in IPF patients and known for their role in ECM production ,40. AccusPLA2-X is also considered as asthmatic sPLA2 (34). It has been demonstrated that sPLA2-X is secreted by airway epithelium, and further, this released sPLA2-X acts on the phospholipids present in the immune cells like eosinophils and releases lysophospholipid, thus increasing the generation of CysLT from eosinophils. Importantly, these entire events are inhibited by inhibition of cPLA2 alpha . These iThe physiological function of PLA2G12A is not clear yet, though it is highly expressed in various tissues. It is both structurally and functionally different from other sPLA2 and has shown much less sPLA2 activity . A genomWe found increased expression of PLA2G2D in macrophages and ciliated cells in IPF patients C, indicaThus, sPLA2 seems to have a disease-associated role in IPF. Next, we wanted to determine the possible therapeutic effect of sPLA2 in the human-relevant mice model of IPF. The American Thoracic Society recommends the use of male mice to develop Bleomycin-induced lung fibrosis. However, the NIH recommends the use of both male and female mice to develop Bleomycin-induced lung fibrosis. In any event, a number of studies had used female mice for developing Bleomycin-induced lung fibrosis ,76,77,78While there is always a debate regarding the role of animal models in human translational approaches, it will be ideal to explore the pathways that are common to both human and animal models. In this context, a number of commonly expressed genes were found upon comparison between human lung fibrotic condition and the rodent model of bleomycin-induced lung fibrosis, indicating that these genes can be taken as a target in human-translational strategies. One such common gene is PLA2G2A, which was found to be upregulated significantly in the lungs of both rodent and human fibrotic conditions . pBPB isThere is a considerable overlap between asthma and lung fibrosis in the context of sPLA2. Arachidonic acid metabolites such as leukotrienes and thromboxane are increased in both the diseases asthma and lung fibrosis and are well known for their pro-inflammatory roles. Our previous study has shown that pBPB; a known sPLA2 inhibitor attenuated asthma along with reduction in sub-epithelial fibrosis in a mice model ,22. We hIn summary, we have revealed the PLA2G2A high fibroblast population with both fibrotic and inflammatory properties in the lungs of IPF patients. Expression of PLA2G2A and PLA2G5 were found to be increased in fibroblasts of IPF patients whereas PLA2G3, PLA2G10 and PLA2G12A were increased in certain key epithelial cells like club cells, KRT5−/KRT17+ cells and ciliated cells . In addi


Coupling with the eminent synergism among specific constituents and the highly efficient mass transport from integrated porous backbone, FeNiZn/FeNi3@NiFe depicts exceptional bifunctional activities for water splitting with extremely low overpotentials toward OER and HER (η1000 = 367/245 mV) as well as the robust durability during the 400 h testing in alkaline solution. The as-built water electrolyzer with FeNiZn/FeNi3@NiFe as both anode and cathode exhibits record-high performances for sustainable hydrogen output in terms of much lower cell voltage of 1.759 and 1.919 V to deliver the current density of 500 and 1000 mA cm−2 as well long working lives. Density functional theory calculations disclose that the interface interaction between FeNiZn alloy and FeNi3 intermetallic generates the modulated electron structure state and optimized intermediate chemisorption, thus diminishing the energy barriers for hydrogen production in water splitting. With the merits of fine performances, scalable fabrication, and low cost, FeNiZn/FeNi3@NiFe holds prospective application potential as the bifunctional electrocatalyst for water splitting.The sluggish kinetics of both hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) generate the large overpotential in water electrolysis and thus high-cost hydrogen production. Here, multidimensional nanoporous interpenetrating-phase FeNiZn alloy and FeNiThe online version contains supplementary material available at 10.1007/s40820-023-01066-w. Electrochemical water splitting in alkaline solution represents the most promising green technique to the production of clean and sustainable hydrogen energy from the intermittent electricity harvested from renewable sources . However4 [3 [3 [3-based nanocomposites have been focused to achieve the exceptional bifunctional catalytic activities. For instance, composited Ni3FeN/Ni3Fe nanosheets exhibits high bifunctional activities with the overpotentials of 250 and 125 mV for OER and HER to drive a current density of 10 mA cm−2 [3N incorporated by FeNi3, which displayed the η10 of 260 and 51 mV toward OER and HER, respectively [3-based nanocomposites can significantly improve the bifunctional catalytic activities because the electronic interaction between the components can optimize the electronic structure, balance the adsorption and desorption of the reaction intermediates, as well increase the number of the active sites [3-based nanocomposite toward water splitting by way of tailoring the constituents and architecture.Among diverse categories of electrocatalysts toward water splitting, intermetallics as one particular class of alloy-type materials have attracted widespread attentions in recent years , 6. Inte4 , FeNi3 [4 [3 , 11, CuN4 [3 [3  toward O4 [3 [3 , 14, Ni–4 [3 [3 , 16, and4 [3 [3 , 18 towa mA cm−2 . Liang eectively . Compareve sites , 20. Mot3 hybrid supported on nickel foam (IrNi-FeNi3/NF), which displays the ultralow overpotentials of 330.0/288.8 mV to generate a current density 1000 mA cm−2 for OER and HER as well good stability for overall water splitting [Of note, the architecture of catalyst electrode is also one of pivotal factor to decrease the overpotential of water electrolysis not only by providing the abundant active sites and the fluent diffusion path but also eliminating a series of inherent resistance among the catalyst surface, electrolyte, and the electrode itself. In this respect, self-supporting electrode is preferable for exposing more active sites and reducing the interface resistance owing to free of conductive binders –23. Receplitting . Such thplitting , 28. Furplitting –31. In a3 intermetallic heterostructure in situ constructed within the surface layer of NiFe foam (FeNiZn/FeNi3@NiFe) with multilevel pore distribution. NiFe foam of the atomic ratio around 80:20 was chosen as the support with the phase structure consisting of FeNi3 intermetallic and pure Ni. The versatile dealloying strategy was deployed to simultaneously in situ build one layer of bimodal nanoporous structure and dispersed FeNiZn alloy within NiFe foam through the handy operation of electroplating Zn, annealing, and etching part of Zn. The as-made FeNiZn/FeNi3@NiFe gets together multiple structural advantages in terms of multimodal porous structure from macro- to nanoscale and the rich interface between interpenetrating FeNiZn and FeNi3, thus resulting in unlimited mass transport and high density of active sites. First-principle calculations testified that FeNiZn and FeNi3 heterostructure has diversified highly active sites with low-Gibbs free energy toward OER and HER. The as-built water electrolyzer achieves record-high stable performances for hydrogen production under much lower cell voltage even compared with the previously reported similar noble metal-containing catalysts, such as IrNi-FeNi3/NF [Given all that, in current work we focused on the design and fabrication of high-performance bifunctional interpenetrating-phase FeNiZn and FeNiFeNi3/NF . This wo2 were obtained from Sigma Aldrich Chemical Reagent Co., Ltd. The other chemicals were bought from Shanghai Sinopharm Chemical Reagent Ltd. Co of China. All agents were used as an analytical purity without further purification.Commercial Pt/C (20 wt%) and RuO4 + 0.2 M Na2SO4 + 0.2 M H3BO3 in a three-electrode system with Pt sheet (2 cm × 3 cm) and saturated calomel electrode (SCE) as the counter and reference electrode under − 1.7 V versus SCE for 1 h. Subsequently, Zn@NiFe was annealed at 430 °C for 5 h in the Ar atmosphere to make the alloy precursor, which was further corroded in 2.0 M ammonia solution for 12, 24, and 48 h to obtain the final FeNiZn/FeNi3@NiFe-12 h, FeNiZn/FeNi3@NiFe-24 h, and FeNiZn/FeNi3@NiFe-48 h samples, respectively.NiFe foam with the size around 1 cm × 2 cm was pretreated by ultrasonicating in 1 M HCl solution, ethanol, and deionized water, respectively. Zn coating was electrodeposited on NiFe foam to form Zn@NiFe in the mixture solution of 0.2 M ZnSOλ = 1.5418 Å) at a step rate of 0.02° s−1. The morphology and composition were characterized by transmission electron microscope  and field-emission scanning electron microscope  equipped with X-sight energy-dispersive X-ray spectrometer . Electron backscattered diffraction (EBSD) was conducted by a field-emission SEM (FEI Scios) using an OXFORD EBSD acquisition system. X-ray photoelectron spectroscopy (XPS) data were recorded on an ESCALAB MK II X-ray photoelectron spectrometer. The pore size distribution of the sample was measured with a Quadrasorb SI-MP (Quantachrome Instruments) according to the Brunauer-Emmett-Teller (BET) method.The crystal phase structure of the sample was determined by X-ray diffraction  with Cu Kα radiation (2 (0.4 cm × 0.5 cm). Graphite rod and mercuric oxide electrode (MOE) were selected as the counter electrode and reference electrode, respectively. The electrolyte of 1.0 M KOH (pH = 14) solution was first purged with N2 and O2 for 30 min before HER and OER testing, respectively. The linear sweep voltammetry (LSV) was conducted with the scan rate of 5 mV s−1. All the potentials were converted to reversible hydrogen electrode (RHE) according to the equation of ERHE = EMOE + 0.924 V with 90% iR-compensation. The double-layer capacitance (Cdl) values were confirmed by cyclic voltammetry (CV) in the potential range of 1.00 to 1.10 V (vs. RHE) at different scan rates. Electrochemical impedance spectroscopy (EIS) was measured at the overpotential of 100 and 250 mV for HER and OER with a frequency scope from 100 kHz to 0.01 Hz, respectively. Pt/C and RuO2 were dropped onto NiFe foam (0.4 cm × 0.5 cm) with the loading mass of ~ 3.5 mg cm−2 for all tests, respectively. Current–time (i–t) curves were tested under constant potentials.All electrochemical data were recorded on CHI 760E electrochemical workstation  in a three-electrode system. The electrocatalysts were employed as the working electrode with the fixed size at ~ 0.2 cmk-points meshes with a resolution of 2π × 0.03 Å−1 and Methfessel–Paxton electronic smearing were adopted for the integration in the Brillouin zone for geometric optimization. The simulation was run with a cutoff energy of 500 eV throughout the computations. These settings ensure convergence of the total energies to within 1 meV per atom. Structure relaxation proceeded until all forces on atoms were less than 1 meV Å−1 and the total stress tensor was within 0.01 GPa of the target value.Spin-polarized first-principle calculations were performed by the density functional theory (DFT) using the Vienna Ab-initio Simulation Package (VASP) package . The genGH*) is obtained by:EH* describes the energy needed to increase the coverage by one hydrogen atom. ΔEZPE is the difference in zero point energy and ΔSH* is the difference in entropy. ΔEZPE–TΔSH* is about 0.24 eV, so ΔGH* = ΔEH* + 0.24 [EH*, it is calculated as follows:E(surface + H) represents the total energy of the selected surfaces with one adsorbed hydrogen atom on the surfaces, E(surface) represents the total energy of the surfaces, while E(H2) represents the total energy of a gas phase H2 molecule.The free energy of the adsorption atomic hydrogen –(S4) can be calculated using aforementioned equations:Generally, in alkaline media the OER reaction mechanism can be written as 4-elecetron change mechanism:η for OER can be calculated using Eqs. The theoretical overpotential 3 heterojunction on NiFe foam was fabricated as shown in Fig. 3@NiFe).The interpenetrating FeNiZn and FeNi3 intermetallic  alloy (JCPDS No. 37-0474). In the light of the high content of Zn upon dealloying as indicated in Fig. 3 intermetallic in NiFe foam to form Ni2Zn11 and Fe6.8Zn3.2 alloy. After selectively dissolving the Zn atoms, the residual Fe, Ni, and Zn atoms underwent interdiffusion to form the ternary FeNiZn alloy. Note that no extra peaks emerged, which manifested the formation of FeNi3 intermetallic and FeNiZn alloy composites. By further extending the corrosion time to 48 h, the XRD pattern has almost no change apart from the minor variation of peak width and intensity, indicating the more stable phase structure for the FeNiZn and FeNi3 heterojuction.XRD was first applied to examine the evolution of crystal phase structure from NiFe foam to the final product during electroplating Zn, annealing, and partial etching of Zn. For the NiFe foam, three asymmetric diffraction peaks with the evident shoulder at the lower-angle side emerged at 44.5°, 51.8°, and 76.1°, which is owing to the neighboring relationship for the diffraction peaks of pure Ni and FeNilic Fig. a. Upon tlic Fig. b. After The microstructural evolution process from NiFe foam to the resultant heterostructure was monitored by electron microscope. From Fig. S1a, b, it is clear that NiFe foam has the porous structure with interconnected pore channels and smooth 3D skeleton, which can provide high supporting area as well as the expedite pathway for mass transfer. Its atomic ratio of Ni and Fe is around 81.20:18.80 . Upon electroplating Zn on NiFe foam, one dense layer of Zn was successfully coated as observed in Fig. S1c, d and EDS data . After annealing, the coating layer became more flattening with Ni, Fe, and Zn elements detectable, implying the formation of Zn-based alloy precursor .3@NiFe sample upon etching in 2.0 M ammonia solution for 24 h is shown in Fig. 3@NiFe-24 h sample by long-time ultrasonicating. As illustrated in Fig. 3@NiFe-24 h sample mainly distributes in the range of 3 ~ 6 nm, which accords with the TEM observation. As shown by the high-resolution TEM (HRTEM) image in Fig. 3 intermetallic and (110) plane of FeNiZn alloy. From Fig. 3 and the (110) and (200) planes of FeNiZn alloy, confirming the polycrystalline nature of FeNiZn and FeNi3 heterojunction. EBSD images in Fig. 3 intermetallic, where the measured phase ratio is around 0.51:0.49. According to the elemental components and phase ratio, it can be speculated that the FeNiZn alloy ratio is 8.74:57.48:33.78. In addition, the influence of the different corrosion time on the resulting structure was explored as displayed in Fig. S3, and the detailed description was provided in Supporting Information.The microstructure of the dealloyed FeNiZn/FeNi3@NiFe-24 h and pure NiFe foam. As shown in Fig. 3@NiFe-24 h sample the peaks with the binding energy at 852.52 and 868.54 eV can be ascribed to the metallic Ni. The slight negative shift compared with the pure Ni suggests that the metallic Ni comes from the FeNiZn alloy and FeNi3 intermetallic [2+ species accompanied with the coexistence of Ni3+ corresponding to the fitted peaks around 858.05 and 876.15 eV [3@NiFe-24 h encounter the positive shift to higher value, which testifies that the addition of Zn and the interface formation effectively regulates the electronic structure state of Ni. As for the Fe 2p core level region  and NiFe foam (358 mV). At the current density of 100 and 500 mA cm−2, its overpotentials are 258 and 315 mV, respectively, which is also much smaller than those of RuO2@NiFe (331 and 397 mV) and NiFe foam (372 and 432 mV). Such the OER activity for FeNiZn/FeNi3@NiFe-24 h surpasses some similar electrocatalysts reported previously as shown in Table S1 [The OER performances of FeNiZn/FeNi to Ni2+ , 46. By  331 and 7 mV and Table S1 –51.Fig. 3@NiFe-24 h displays the smallest value around 45.7 mV dec−1 compared with other electrodes, implying that it has the relatively low reaction kinetic limitation. In an effort to understand the reaction kinetic behavior of electrode and interface, EIS analysis was conducted. As shown in Fig. 3@NiFe-24 h sample shows the lowest electrochemical transfer resistance (Rct) at 3.06 Ω compared with other electrodes, which means its much better electrocatalytic efficacy favoring for highly effective electron transfer and mass transport during the oxygen evolution. The Cdl value calculated from CV curves is further utilized to estimate the electrochemical surface area (ECSA) of all electrodes . As can be observed in Fig. Cdl values for all the electrodes can clearly account for their disparity in the OER activities. FeNiZn/FeNi3@NiFe-24 h has the Cdl value as large as 203.4 mF cm−2, which is much larger than that of FeNiZn/FeNi3@NiFe-12 h (115.6 mF cm−2), FeNiZn/FeNi3@NiFe-48 h (191.2 mF cm−2), and NiFe foam (7.0 mF cm−2). Such the large ECSA of FeNiZn/FeNi3@NiFe-24 h states its high density of active sites toward OER. It is regarded that the mesoporous structure with small pore size offers high surface area and thus the abundant active sites. Considering the experimental observations above, there is no doubt that the exceptional OER activity of FeNiZn/FeNi3@NiFe-24 h profits from the specific multidimensional porous interpenetrating-phase heterostructure between FeNiZn alloy and FeNi3 intermetallic. The rich mesoscale pores dispersed within the interconnected 3D skeleton can furnish high density of active sites, while the large pore channels can provide smooth electron transfer and unblocked electrolyte diffusion. In particular, the strong synergy from FeNiZn alloy and FeNi3 intermetallic is beneficial for the modulation of electronic structure and thus the optimization of reaction intermediates chemisorption [We further deduced the Tafel plots of all electrocatalysts on the basis of the LSV curves to gain insight into their OER mechanism. As depicted in Fig. sorption .3@NiFe-24 h sample. For comparison, the catalytic durability of the commercial RuO2 supported on NiFe foam was also provided. As illustrated in Fig. 3@NiFe-24 h electrode can steadily proceed successive oxygen evolution for 400 h under the overpotential of 340 mV without the evident activity degradation, suggesting its excellent catalytic stability toward OER. In contrast, the catalytic activity of RuO2@NiFe dramatically decreased with only 28% of the initial current density retained upon testing for 22 h. The obvious activity decline of RuO2@NiFe is mainly because such the catalyst easily dissolves in a strong alkaline electrolyte under high voltage, meanwhile the particle-type catalyst usually suffers from peeling off during long term test in comparison with the integrated self-supporting electrocatalyst [3@NiFe-24 h electrode toward OER was further confirmed by the well overlapped LSV curves before and after the long-term running  of the electrode after testing became even larger than that of pre-cycling sample, which is responsible for the stable OER activity upon long term test . The slight increment of Cdl value is considered to stem from the surface reconstruction due to the further dissolution of little Zn atom as confirmed by EDS data . EDS data confirm that the ratio of Ni/Fe/Zn is 62.35:16.38:21.27 after long-term test. By comparison with that of the fresh sample, the Zn content decreased, while the contents of Fe and Ni both increased. It can be inferred that during long-term catalysis the Zn atoms encounter relatively more dissolution in KOH solution compared with Fe and Ni in the oxygen-rich environment, which also lead to the enlarged pore channels as observed in Fig. S5a, and thus generate much higher surface area as well as the increment of active sites toward OER . It is considered that during the long-term measurement Fe atoms tend to combine with oxygen-containing species to form hydroxide, thus retarding its dissolution. XRD was further utilized to analyze the phase structure of the resultant sample. As shown in Fig. S5c, the FeNiZn and FeNi3 well maintained the initial phase, indicating its unique stability in alkaline solution under the high applied potential. The results above demonstrate that the free-standing multi-scale 3D porous heterostructure has robust structure stability, which can well preserve the surface active sites during long period of electrocatalysis.The electrocatalytic durability for one OER electrocatalyst also represents one crucial indicator for its practical application in industrial hydrogen production. Chronoamperometry method was first used to estimate the long-term persistence in water electrolysis for FeNiZn/FeNicatalyst , 52. The3@NiFe-24 h sample after testing. As shown in Fig. S7a, c, the spectra of both Ni 2p and Zn 2p have almost no change compared with those of the fresh sample. In the respect of Fe 2p spectrum , it is evident that the peak intensity of Fe3+ increased along with that of Fe2+ diminished, which is due to the little oxidation of Fe2+ to Fe3+ during OER process. In addition, the enhanced intensity of M–OH also suggested that a little Fe2+ species converted to FeO–OH species . The observations above dramatically demonstrated that self-supporting multimodal porous FeNiZn and FeNi3 heterostructure has excellent electrocatalytic durability toward OER in virtue of the robust structure stability as well as the high corrosion resistance, demonstrating powerful application prospect in water electrocatalysis.XPS was also used to examine the chemical state of all elements for the resultant FeNiZn/FeNi3@NiFe-24 h sample, it also shows the impressive HER performances. Figure 3@NiFe samples in 1.0 M KOH solution with those of Pt/C@NiFe and pure NiFe foam also tested for comparison. As shown in Fig. 3@NiFe-24 h sample displays the highest electrocatalytic activity toward HER with very small overpotential of 245 mV to reach the high current density of 1000 mA cm−2. Furthermore, it is clearly observed that the onset potential of Pt/C@NiFe toward HER is the lowest, but it became much larger than FeNiZn/FeNi3@NiFe-12 h and FeNiZn/FeNi3@NiFe-24 h samples when the current density is beyond to 70 mA cm−2. This is because the coated Pt/C particles on NiFe foam through the conductive binders are easily stripped down by the massive produced bubbles under the high current densities. Figure 3@NiFe-24 h has the overpotential of 84 mV at 50 mA cm−2, which is 86, 112, and 403 mV less than FeNiZn/FeNi3@NiFe-12 h, FeNiZn/FeNi3@NiFe-48 h, and NiFe foam, respectively. At the current density of 100 and 500 mA cm−2, the overpotentials of FeNiZn/FeNi3@NiFe-24 h sample are only 102 and 177 mV, which are much lower than those of other electrodes. Moreover, the HER activity of FeNiZn/FeNi3@NiFe-24 h sample is superior to the comparable electrocatalysts reported previously as shown in Table S2, especially under the high current density [3@NiFe-24 h sample in virtue of high density of active sites and fluent mass transfer [Aside from the excellent OER performances of FeNiZn/FeNi density , 53. As 3@NiFe-24 h is 44.7 mV dec−1, which is slightly larger than the 39.6 mV dec−1 of Pt/C@NiFe catalyst, but smaller than those of other electrodes. The Tafel value of FeNiZn/FeNi3@NiFe-24 h means its Volmer-Heyrovsky mechanism during HER process with the adsorption/desorption of H intermediates as the rate-determination step [Rct values follows the same trend as the electrodes made by different dealloying time. Of note, FeNiZn/FeNi3@NiFe-24 h sample has the lowest Rct value as small as 2.4 Ω, implying its more rapid electron transport and ion diffusion. In addition, FeNiZn/FeNi3@NiFe-24 h presents the excellent electrocatalytic durability toward HER under the overpotential of 160 mV with almost no decay in the long period of continuous 400 h, as illustrated in Fig. −2, while which underwent a significant activity decay with 79% of the initial current density retained upon merely testing for 12 h. This observation further proves that the particle-type catalyst is more likely to fall off at high current density than the self-supporting catalyst, thus resulting in poor catalytic stability [3@NiFe-24 h shifts to the forward, displaying the better HER activity with the decrease of the overpotential . It is worth mentioning that the pore channels became more penetrated through long period running in the alkaline solution accompanied with the decrease of Ni and Fe contents due to their slight dissolution as depicted in Fig. S8d. By comparison with the initial components, the reduced contents of Ni and Fe disclose that the FeNiZn alloy phase is more stable than FeNi3 intermetallic under the measurement conditions for HER. Meanwhile, the catalytic sites over FeNiZn . The little increase of Fe3+ suggests the slight oxidation of Fe2+ . It should be noted that the peak intensity of M–O bond evidently attenuated compared with those of M–OH and absorbed H2O, which implies that the major part of metal oxides on the surface has been reduced during hydrogen evolution process .It is supposed that the high electrocatalytic stability of FeNiZn/FeNi3@NiFe-24 h sample displays outstanding electrocatalytic performances toward both HER and OER in terms of low overpotential and long-term catalytic durability. It is interesting to utilize the FeNiZn/FeNi3@NiFe-24 h sample as both the OER and HER electrocatalysts to drive the water electrolysis. As shown in Fig. −2, the cell voltage only needs as small as 1.578 V, which is dramatically lower than the 1.691 V of Pt/C@NiFe (–)||RuO2@NiFe (+). Expressively, the voltage of merely 1.759 and 1.919 V is required to achieve the current density as high as 500 and 1000 mA cm−2 over FeNiZn/FeNi3@NiFe-24 h (+ //−), which is dramatically lower than that of Pt/C@NiFe (−)||RuO2@NiFe (+) (1.957 V@500 mA cm−2). We noted that the overall splitting performances of FeNiZn/FeNi3@NiFe-24 h electrode in alkaline electrolyzer is record-high compared with the similar and even the noble metal-containing materials in previous reports. For example, the IrNi-FeNi3/NF needs the 1.78 V to achieve the 500 mA cm−2; in contrast, our material only require 1.759 V [3@NiFe-24 h (+ //−) retained the initial of 92.5%, exhibiting the steady and reliable hydrogen export  crystal planes with the different Ni coordinate sites involved, as displayed in Fig. 3 heterostructure, where the yellow region represents the electron accumulation, and the blue region represents the electron depletion calculated based on the equation: Δρ = ρ(FeNiZn/FeNi3)–ρ(FeNiZn)–ρ(FeNi3). It is clear that the FeNiZn section in the interface has the evident electron enrichment, revealing that the electrons in FeNi3 transfer to the neighboring FeNiZn. The calculated charge density difference in the FeNiZn/FeNi3 heterojunction in Table S4 further established this result, where the negative Bader charge for FeNi3 indicates its loss of electrons, further confirming the electron transfer from FeNi3 to FeNiZn. The density of states (DOS) of these models are also calculated as shown in Fig. 3 has the largest DOS value at Fermi level, implying that the FeNiZn/FeNi3 heterostructure has much higher valence electron concentration. The d band center of Fe and Ni in FeNiZn/FeNi3 is synergistically modulated compared with those in FeNi3 and FeNiZn single counterpart. It is worthy to mention that too low or too high d band energy levels are disadvantageous toward the chemisorption process. The Sabatier principle uncovers that the moderate adsorption and desorption energies of reaction intermediates are crucial for the electrocatalytic efficiency [d band energy levels decrease the electron filling of antibonding states, resulting in a too strong bond strength, which is unfavorable to the desorption of reaction intermediates. Likewise, the lower d band energy levels will greatly weaken the bond strength, possibly hindering the following catalytic process [d-band center of Ni, Fe, and Zn in FeNiZn/FeNi3 is more favorable for the moderate adsorption and desorption of reaction intermediates during water splitting as confirmed in the following data below.DFT calculations were conducted for the sake of the origin of high bifunctional activity of FeNiZn and FeNificiency –61. Beca process , 63. The3 heterojunction were selected to explore the ΔGH* to assess the HER activity of catalysts . From Figs. GH* values around − 0.348 and − 0.349 eV, indicating its stronger absorption and thus the high energy barriers for the subsequent formation of H2. For the Ni–Ni–Zn sites , during the calculation process the H will transfer onto the adjacent sites of Ni–Ni–Fe sites with the lower ΔGH* around − 0.252 eV . This result revealed that the Ni–Ni–Fe site adjacent to Ni–Ni–Zn site has the higher HER activity compared with that adjacent to Ni–Ni–Ni site due to the substitution of one Zn atom to Ni. If one Zn atom further substitutes one Ni atom to form the Ni–Fe–Zn site, it is intriguing to find that the ΔGH* can reach as small as − 0.090 eV. It can be concluded that the further introduction of Zn to near Ni–Fe sites can form much higher active sites, synergistically promoting the hydrogen evolution with more rapid reaction kinetics.Ni–Ni–Ni, Ni–Ni–Zn, Ni–Ni–Fe, and Ni–Fe–Zn sites on the FeNiZn/FeNi3 heterojunction . From Figs. 3 heterojunction. Note that the O* to OOH* over three different Ni top sites on the interface has much low energy barriers around 0.61 to 0.64 eV at the applied potential of 1.23 V  were calculated over different Ni sites, Fe site, and Zn site on the interface of FeNiZn/FeNi3 V Fig. h and aro3 V Fig. h. From F3@NiFe-24 h owns a series of exciting electrocatalytic performances toward water splitting as the bifunctional electrocatalyst. It is considered that its specific architecture and the extraordinary components contribute to the superior OER and HER activities. First, the bimodal nanoporous structure in situ built within the microporous NiFe foam increases the density of active sites and thus ensures highly efficient mass transfer, while the interconnected hollow channels can facilitate the electrolyte transportation as well as the full contact with active sites. Second, the integrated 3D multiscale porous architecture is conducive to boost the oxidation resistance and corrosion resistance, thus endowing the robust structure stability and catalytic durability. Third, the strong synergy between FeNiZn alloy and FeNi3 intermetallic generates the impelling electron modulation from the alloying effect, interface coupling, and structural benefits. These structural advantages can generate multiple synergistic actions to optimize the adsorption/desorption of reactants and thus the superior bifunctional activity toward HER and OER.It is clearly found that FeNiZn/FeNi3 heterostructure anchored on the surface layer of 3D NiFe foam. On the account of self-supporting multiscale porous architecture, the intercrossing heterostructure, and strong synergy between FeNiZn alloy and FeNi3 intermetallic, the as-made FeNiZn/FeNi3@NiFe electrocatalyst displays superior electrocatalytic activities toward both HER and OER in terms of low overpotentials and the exceptional electrocatalytic durability, especially at large current density. In particular, the low cell voltage and steady hydrogen output under large current endow FeNiZn/FeNi3@NiFe prospective application in the industrial hydrogen production as the bifunctional electrocatalyst. The developed protocol may inspire great research interests to screen low cost, highly active, and highly stable intermetallic-based heterostructure electrocatalysts toward overall water splitting with the merits of scalable, handy, and massive preparation.In summary, one powerful dealloying strategy of Zn electrodeposition, annealing, and mild corrosion successfully in situ constructs bimodal porous interpenetrating FeNiZn and FeNiSupplementary file1 (PDF 3791 KB)Below is the link to the electronic supplementary material.