Constructing nickel-iron oxyhydroxides integrated with iron oxides by microorganism corrosion for oxygen evolution.

Developing facile approaches for preparing efficient electrocatalysts is of significance to promote sustainable energy technologies. Here, we report a facile iron-oxidizing bacteria corrosion approach to construct a composite electrocatalyst of nickel­iron oxyhydroxides combined with iron oxides. The obtained electrocatalyst shows improved electrocatalytic activity and stability for oxygen evolution, with an overpotential of ∼230 mV to afford the current density of 10 mA cm−2. The incorporation of iron oxides produced by iron-oxidizing bacteria corrosion optimizes the electronic structure of nickel­iron oxyhydroxide electrodes, which accounts for the decreased free energy of oxygenate generation and the improvement of OER activity. This work demonstrates a natural bacterial corrosion approach for the facile preparation of efficient electrodes for water oxidation, which may provide interesting insights in the multidisciplinary integration of innovative nanomaterials and emerging energy technologies.

Unipolar Solution Flow in Calcium-Organic Frameworks for Seawater-Evaporation-Induced Electricity Generation.

Seawater-flow- and -evaporation-induced electricity generation holds significant promise in advancing next-generation sustainable energy technologies. This method relies on the electrokinetic effect but faces substantial limitations when operating in a highly ion-concentrated environment, for example, natural seawater. We present herein a novel solution using calcium-based metal-organic frameworks (MOFs, C12H6Ca2O19·2H2O) for seawater-evaporation-induced electricity generation. Remarkably, Ca-MOFs show an open-circuit voltage of 0.4 V and a short-circuit current of 14 µA when immersed in seawater under natural conditions. Our experiments and simulations revealed that sodium (Na) ions selectively transport within sub-nanochannels of these synthetic superhydrophilic MOFs. This selective ion transport engenders a unipolar solution flow, which drives the electricity generation behavior in seawater. This work not only showcases an effective Ca-MOF for electricity generation through seawater flow/evaporation but also contributes significantly to our understanding of water-driven energy harvesting technologies and their potential applications beyond this specific context.

BETA: a comprehensive benchmark for computational drug-target prediction.

Internal validation is the most popular evaluation strategy used for drug-target predictive models. The simple random shuffling in the cross-validation, however, is not always ideal to handle large, diverse and copious datasets as it could potentially introduce bias. Hence, these predictive models cannot be comprehensively evaluated to provide insight into their general performance on a variety of use-cases (e.g. permutations of different levels of connectiveness and categories in drug and target space, as well as validations based on different data sources). In this work, we introduce a benchmark, BETA, that aims to address this gap by (i) providing an extensive multipartite network consisting of 0.97 million biomedical concepts and 8.5 million associations, in addition to 62 million drug-drug and protein-protein similarities and (ii) presenting evaluation strategies that reflect seven cases (i.e. general, screening with different connectivity, target and drug screening based on categories, searching for specific drugs and targets and drug repurposing for specific diseases), a total of seven Tests (consisting of 344 Tasks in total) across multiple sampling and validation strategies. Six state-of-the-art methods covering two broad input data types (chemical structure- and gene sequence-based and network-based) were tested across all the developed Tasks. The best-worst performing cases have been analyzed to demonstrate the ability of the proposed benchmark to identify limitations of the tested methods for running over the benchmark tasks. The results highlight BETA as a benchmark in the selection of computational strategies for drug repurposing and target discovery.

An intrinsically disordered region-containing protein mitigates the drought-growth trade-off to boost yields.

Drought stress poses a serious threat to global agricultural productivity and food security. Plant resistance to drought is typically accompanied by a growth deficit and yield penalty. Herein, we report a previously uncharacterized, dicotyledon-specific gene, Stress and Growth Interconnector (SGI), that promotes growth during drought in the oil crop rapeseed (Brassica napus) and the model plant Arabidopsis (Arabidopsis thaliana). Overexpression of SGI conferred enhanced biomass and yield under water-deficient conditions, whereas corresponding CRISPR SGI mutants exhibited the opposite effects. These attributes were achieved by mediating reactive oxygen species (ROS) homeostasis while maintaining photosynthetic efficiency to increase plant fitness under water-limiting environments. Further spatial-temporal transcriptome profiling revealed dynamic reprogramming of pathways for photosynthesis and stress responses during drought and the subsequent recovery. Mechanistically, SGI represents an intrinsically disordered region-containing protein that interacts with itself, catalase isoforms, dehydrins, and other drought-responsive positive factors, restraining ROS generation. These multifaceted interactions stabilize catalases in response to drought and facilitate their ROS-scavenging activities. Taken altogether, these findings provide insights into currently underexplored mechanisms to circumvent trade-offs between plant growth and stress tolerance that will inform strategies to breed climate-resilient, higher yielding crops for sustainable agriculture.

DELAYED GREENING 409 encodes a dual-localized pentatricopeptide repeat protein required for chloroplast and mitochondrial development.

In flowering plants, hundreds of RNA editing events occur in the chloroplasts and mitochondria during posttranscriptional processes. Although several pentatricopeptide repeat (PPR) proteins have been shown to form the editosome core, the precise interactions between the different editing factors are still obscure. Here, we isolated an Arabidopsis (Arabidopsis thaliana) PPR protein, designated DELAYED GREENING 409 (DG409), that was dually targeted to chloroplasts and mitochondria. This protein consists of 409 amino acids with 7 PPR motifs but lacks a C-terminal E, E+, or DYW domain. A mild dg409 knockdown mutant displays a sickly phenotype. In this mutant, the young leaves are pale green and turn green at maturity, and the development of chloroplasts and mitochondria is severely disrupted. Complete loss of DG409 function results in defective embryos. Transcriptomic analysis of the dg409 knockdown plants showed some editing defects in genes from both organelles, including CASEINOLYTIC PROTEASE P (clpP)-559, RNA POLYMERASE SUBUNIT ALPHA (rpoA)-200, ACETYL-COA CARBOXYLASE CARBOXYL TRANSFERASE SUBUNIT BETA (accD)-1568, NADH DEHYDROGENASE SUBUNIT 7 (nad7)-1505, and RIBOSOMAL PROTEIN S3 (rps3)-1344. RNA immunoprecipitation showed that DG409 was associated with the targeted transcripts in vivo. Interaction assays revealed that DG409 directly interacted with 2 DYW-type PPR proteins (EARLY CHLOROPLAST BIOGENESIS2 [AtECB2] and DYW DOMAIN PROTEIN2 [DYW2]) and 3 multiple organellar RNA editing factors (MORF2, MORF8, and MORF9). These results indicate that DG409 is involved in RNA editing via protein complexes and is therefore essential for chloroplast and mitochondrial development.

Identification of pancreatic cancer risk factors from clinical notes using natural language processing.

OBJECTIVES: Screening for pancreatic ductal adenocarcinoma (PDAC) is considered in high-risk individuals (HRIs) with established PDAC risk factors, such as family history and germline mutations in PDAC susceptibility genes. Accurate assessment of risk factor status is provider knowledge-dependent and requires extensive manual chart review by experts. Natural Language Processing (NLP) has shown promise in automated data extraction from the electronic health record (EHR). We aimed to use NLP for automated extraction of PDAC risk factors from unstructured clinical notes in the EHR. METHODS: We first developed rule-based NLP algorithms to extract PDAC risk factors at the document-level, using an annotated corpus of 2091 clinical notes. Next, we further improved the NLP algorithms using a cohort of 1138 patients through patient-level training, validation, and testing, with comparison against a pre-specified reference standard. To minimize false-negative results we prioritized algorithm recall. RESULTS: In the test set (n = 807), the NLP algorithms achieved a recall of 0.933, precision of 0.790, and F1-score of 0.856 for family history of PDAC. For germline genetic mutations, the algorithm had a high recall of 0.851, while precision and F1-score were lower at 0.350 and 0.496 respectively. Most false positives for germline mutations resulted from erroneous recognition of tissue mutations. CONCLUSIONS: Rule-based NLP algorithms applied to unstructured clinical notes are highly sensitive for automated identification of PDAC risk factors. Further validation in a large primary-care patient population is warranted to assess real-world utility in identifying HRIs for pancreatic cancer screening.

The Lymphoma Epidemiology of Outcomes cohort study: Design, baseline characteristics, and early outcomes.

To address the current and long-term unmet health needs of the growing population of non-Hodgkin lymphoma (NHL) patients, we established the Lymphoma Epidemiology of Outcomes (LEO) cohort study (NCT02736357; https://leocohort.org/). A total of 7735 newly diagnosed patients aged 18 years and older with NHL were prospectively enrolled from 7/1/2015 to 5/31/2020 at 8 academic centers in the United States. The median age at diagnosis was 62 years (range, 18-99). Participants came from 49 US states and included 538 Black/African-Americans (AA), 822 Hispanics (regardless of race), 3386 women, 716 age <40 years, and 1513 rural residents. At study baseline, we abstracted clinical, pathology, and treatment data; banked serum/plasma (N = 5883, 76.0%) and germline DNA (N = 5465, 70.7%); constructed tissue microarrays for four major NHL subtypes (N = 1189); and collected quality of life (N = 5281, 68.3%) and epidemiologic risk factor (N = 4489, 58.0%) data. Through August 2022, there were 1492 deaths. Compared to population-based SEER data (2015-2019), LEO participants had a similar distribution of gender, AA race, Hispanic ethnicity, and NHL subtype, while LEO was underrepresented for patients who were Asian and aged 80 years and above. Observed overall survival rates for LEO at 1 and 2 years were similar to population-based SEER rates for indolent B-cell (follicular and marginal zone) and T-cell lymphomas, but were 10%-15% higher than SEER rates for aggressive B-cell subtypes (diffuse large B-cell and mantle cell). The LEO cohort is a robust and comprehensive national resource to address the role of clinical, tumor, host genetic, epidemiologic, and other biologic factors in NHL prognosis and survivorship.

Neutrophil extracellular traps (NETs) and Th-2 dominant immune responses in chronic granulomatous chromobalstomycosis.

Chromoblastomycosis (CBM), a chronic, granulomatous, suppurative mycosis of the skin and subcutaneous tissue, is caused by several dematiaceous fungi. The formation of granulomas, tissue proliferation, and fibrosis in response to these pathogenic fungi is believed to be intricately linked to host immunity. To understand this complex interaction, we conducted a comprehensive analysis of immune cell infiltrates, neutrophil extracellular traps (NETs) formation, and the fibrosis mechanism in 20 CBM lesion biopsies using immunohistochemical and immunofluorescence staining methods. The results revealed a significant infiltration of mixed inflammatory cells in CBM granulomas, prominently featuring a substantial presence of Th2 cells and M2 macrophages. These cells appeared to contribute to the production of collagen I and III in the late fibrosis mechanism, as well as NETs formation. The abundance of Th2 cytokines may act as a factor promoting the bias of macrophage differentiation toward M2, which hinders efficient fungal clearance while accelerates the proliferation of fibrous tissue. Furthermore, the expression of IL-17 was noted to recruit neutrophils, facilitating subsequent NETs formation within CBM granulomas to impede the spread of sclerotic cells. Understanding of these immune mechanisms holds promise for identifying therapeutic targets for managing chronic granulomatous CBM.

FedFSA: Hybrid and federated framework for functional status ascertainment across institutions.

INTRODUCTION: Patients' functional status assesses their independence in performing activities of daily living, including basic ADLs (bADL), and more complex instrumental activities (iADL). Existing studies have discovered that patients' functional status is a strong predictor of health outcomes, particularly in older adults. Depite their usefulness, much of the functional status information is stored in electronic health records (EHRs) in either semi-structured or free text formats. This indicates the pressing need to leverage computational approaches such as natural language processing (NLP) to accelerate the curation of functional status information. In this study, we introduced FedFSA, a hybrid and federated NLP framework designed to extract functional status information from EHRs across multiple healthcare institutions. METHODS: FedFSA consists of four major components: 1) individual sites (clients) with their private local data, 2) a rule-based information extraction (IE) framework for ADL extraction, 3) a BERT model for functional status impairment classification, and 4) a concept normalizer. The framework was implemented using the OHNLP Backbone for rule-based IE and open-source Flower and PyTorch library for federated BERT components. For gold standard data generation, we carried out corpus annotation to identify functional status-related expressions based on ICF definitions. Four healthcare institutions were included in the study. To assess FedFSA, we evaluated the performance of category- and institution-specific ADL extraction across different experimental designs. RESULTS: ADL extraction performance ranges from an F1-score of 0.907 to 0.986 for bADL and 0.825 to 0.951 for iADL across the four healthcare sites. The performance for ADL extraction with impairment ranges from an F1-score of 0.722 to 0.954 for bADL and 0.674 to 0.813 for iADL across four healthcare sites. For category-specific ADL extraction, laundry and transferring yielded relatively high performance, while dressing, medication, bathing, and continence achieved moderate-high performance. Conversely, food preparation and toileting showed low performance. CONCLUSION: NLP performance varied across ADL categories and healthcare sites. Federated learning using a FedFSA framework performed higher than non-federated learning for impaired ADL extraction at all healthcare sites. Our study demonstrated the potential of the federated learning framework in functional status extraction and impairment classification in EHRs, exemplifying the importance of a large-scale, multi-institutional collaborative development effort.

The long-term effects of blood urea nitrogen levels on cardiovascular disease and all-cause mortality in diabetes: a prospective cohort study.

BACKGROUND: The long-term effects of blood urea nitrogen(BUN) in patients with diabetes remain unknown. Current studies reporting the target BUN level in patients with diabetes are also limited. Hence, this prospective study aimed to explore the relationship of BUN with all-cause and cardiovascular mortalities in patients with diabetes. METHODS: In total, 10,507 participants with diabetes from the National Health and Nutrition Examination Survey (1999-2018) were enrolled. The causes and numbers of deaths were determined based on the National Death Index mortality data from the date of NHANES interview until follow-up (December 31, 2019). Multivariate Cox proportional hazard regression models were used to calculate the hazard ratios (HRs) and 95% confidence interval (CIs) of mortality. RESULTS: Of the adult participants with diabetes, 4963 (47.2%) were female. The median (interquartile range) BUN level of participants was 5 (3.93-6.43) mmol/L. After 86,601 person-years of follow-up, 2,441 deaths were documented. After adjusting for variables, the HRs of cardiovascular disease (CVD) and all-cause mortality in the highest BUN level group were 1.52 and 1.35, respectively, compared with those in the lowest BUN level group. With a one-unit increment in BUN levels, the HRs of all-cause and CVD mortality rates were 1.07 and 1.08, respectively. The results remained robust when several sensitivity and stratified analyses were performed. Moreover, BUN showed a nonlinear association with all-cause and CVD mortality. Their curves all showed that the inflection points were close to the BUN level of 5 mmol/L. CONCLUSION: BUN had a nonlinear association with all-cause and CVD mortality in patients with diabetes. The inflection point was at 5 mmol/L.

Successful management of chromoblastomycosis utilizing conventional antifungal agents and imiquimod therapy.

Chromoblastomycosis (CBM), a chronic fungal infection affecting the skin and subcutaneous tissues, is predominantly caused by dematiaceous fungi in tropical and subtropical areas. Characteristically, CBM presents as plaques and nodules, often leading to scarring post-healing. Besides traditional diagnostic methods such as fungal microscopy, culture, and histopathology, dermatoscopy and reflectance confocal microscopy can aid in diagnosis. The treatment of CBM is an extended and protracted process. Imiquimod, acting as an immune response modifier, boosts the host's immune response against CBM, and controls scar hyperplasia, thereby reducing the treatment duration. We present a case of CBM in Guangdong with characteristic reflectance confocal microscopy manifestations, effectively managed through a combination of itraconazole, terbinafine, and imiquimod, shedding light on novel strategies for managing this challenging condition.

Bacterial adhesion and corrosion behavior of different pure metals induced by sulfate reducing bacteria.

The corrosion behaviors of four pure metals (Fe, Ni, Mo and Cr) in the presence of sulfate reducing bacteria (SRB) were investigated in enriched artificial seawater (EASW) after 14-day incubation. Metal Fe and metal Ni experienced weight losses of 1.96 mg cm-2 and 1.26 mg cm-2, respectively. In contrast, metal Mo and metal Cr exhibited minimal weight losses, with values of only 0.05 mg cm-2 and 0.03 mg cm-2, respectively. In comparison to Mo (2.2 × 106 cells cm-2) or Cr (1.4 × 106 cells cm-2) surface, the sessile cell counts on Fe (4.0 × 107 cells cm-2) or Ni (3.1 × 107 cells cm-2) surface was higher.

Epidemiologic features and therapeutic strategies of kerion: A nationwide multicentre study.

BACKGROUND: Kerion is a severe type of tinea capitis that is difficult to treat and remains a public health problem. OBJECTIVES: To evaluate the epidemiologic features and efficacy of different treatment schemes from real-world experience. METHODS: From 2019 to 2021, 316 patients diagnosed with kerion at 32 tertiary Chinese hospitals were enrolled. We analysed the data of each patient, including clinical characteristics, causative pathogens, treatments and outcomes. RESULTS: Preschool children were predominantly affected and were more likely to have zoophilic infection. The most common pathogen in China was Microsporum canis. Atopic dermatitis (AD), animal contact, endothrix infection and geophilic pathogens were linked with kerion occurrence. In terms of treatment, itraconazole was the most applied antifungal agent and reduced the time to mycological cure. A total of 22.5% of patients received systemic glucocorticoids simultaneously, which reduced the time to complete symptom relief. Furthermore, glucocorticoids combined with itraconazole had better treatment efficacy, with a higher rate and shorter time to achieving mycological cure. CONCLUSIONS: Kerion often affects preschoolers and leads to serious sequelae, with AD, animal contact, and endothrix infection as potential risk factors. Glucocorticoids, especially those combined with itraconazole, had better treatment efficacy.

Improving Alkaline Hydrogen Oxidation through Dynamic Lattice Hydrogen Migration in Pd@Pt Core-Shell Electrocatalysts.

Tracking the trajectory of hydrogen intermediates during hydrogen electro-catalysis is beneficial for designing synergetic multi-component catalysts with division of chemical labor. Herein, we demonstrate a novel dynamic lattice hydrogen (LH) migration mechanism that leads to two orders of magnitude increase in the alkaline hydrogen oxidation reaction (HOR) activity on Pd@Pt over pure Pd, even ≈31.8 times mass activity enhancement than commercial Pt. Specifically, the polarization-driven electrochemical hydrogenation process from Pd@Pt to PdHx @Pt by incorporating LH allows more surface vacancy Pt sites to increase the surface H coverage. The inverse dehydrogenation process makes PdHx as an H reservoir, providing LH migrates to the surface of Pt and participates in the HOR. Meanwhile, the formation of PdHx induces electronic effect, lowering the energy barrier of rate-determining Volmer step, thus resulting in the HOR kinetics on Pd@Pt being proportional to the LH concentration in the in situ formed PdHx @Pt. Moreover, this dynamic catalysis mechanism would open up the catalysts scope for hydrogen electro-catalysis.

MedTator: a serverless annotation tool for corpus development.

SUMMARY: Building a high-quality annotation corpus requires expenditure of considerable time and expertise, particularly for biomedical and clinical research applications. Most existing annotation tools provide many advanced features to cover a variety of needs where the installation, integration and difficulty of use present a significant burden for actual annotation tasks. Here, we present MedTator, a serverless annotation tool, aiming to provide an intuitive and interactive user interface that focuses on the core steps related to corpus annotation, such as document annotation, corpus summarization, annotation export and annotation adjudication. AVAILABILITY AND IMPLEMENTATION: MedTator and its tutorial are freely available from https://ohnlp.github.io/MedTator. MedTator source code is available under the Apache 2.0 license: https://github.com/OHNLP/MedTator. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Leveraging a pharmacogenomics knowledgebase to formulate a drug response phenotype terminology for genomic medicine.

MOTIVATION: Despite the increasing evidence of utility of genomic medicine in clinical practice, systematically integrating genomic medicine information and knowledge into clinical systems with a high-level of consistency, scalability and computability remains challenging. A comprehensive terminology is required for relevant concepts and the associated knowledge model for representing relationships. In this study, we leveraged PharmGKB, a comprehensive pharmacogenomics (PGx) knowledgebase, to formulate a terminology for drug response phenotypes that can represent relationships between genetic variants and treatments. We evaluated coverage of the terminology through manual review of a randomly selected subset of 200 sentences extracted from genetic reports that contained concepts for 'Genes and Gene Products' and 'Treatments'. RESULTS: Results showed that our proposed drug response phenotype terminology could cover 96% of the drug response phenotypes in genetic reports. Among 18 653 sentences that contained both 'Genes and Gene Products' and 'Treatments', 3011 sentences were able to be mapped to a drug response phenotype in our proposed terminology, among which the most discussed drug response phenotypes were response (994), sensitivity (829) and survival (332). In addition, we were able to re-analyze genetic report context incorporating the proposed terminology and enrich our previously proposed PGx knowledge model to reveal relationships between genetic variants and treatments. In conclusion, we proposed a drug response phenotype terminology that enhanced structured knowledge representation of genomic medicine. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Development of Parkinson Disease and Its Relationship with Incidentally Discovered White Matter Disease and Covert Brain Infarction in a Real-World Cohort.

OBJECTIVE: This study aimed to examine the relationship between covert cerebrovascular disease, comprised of covert brain infarction and white matter disease, discovered incidentally in routine care, and subsequent Parkinson disease. METHODS: Patients were ≥50 years and received neuroimaging for non-stroke indications in the Kaiser Permanente Southern California system from 2009 to 2019. Natural language processing identified incidentally discovered covert brain infarction and white matter disease and classified white matter disease severity. The Parkinson disease outcome was defined as 2 ICD diagnosis codes. RESULTS: 230,062 patients were included (median follow-up 3.72 years). A total of 1,941 Parkinson disease cases were identified (median time-to-event 2.35 years). Natural language processing identified covert cerebrovascular disease in 70,592 (30.7%) patients, 10,622 (4.6%) with covert brain infarction and 65,814 (28.6%) with white matter disease. After adjustment for known risk factors, white matter disease was associated with Parkinson disease (hazard ratio 1.67 [95%CI, 1.44, 1.93] for patients <70 years and 1.33 [1.18, 1.50] for those ≥70 years). Greater severity of white matter disease was associated with increased incidence of Parkinson disease(/1,000 person-years), from 1.52 (1.43, 1.61) in patients without white matter disease to 4.90 (3.86, 6.13) in those with severe disease. Findings were robust when more specific definitions of Parkinson disease were used. Covert brain infarction was not associated with Parkinson disease (adjusted hazard ratio = 1.05 [0.88, 1.24]). INTERPRETATION: Incidentally discovered white matter disease was associated with subsequent Parkinson disease, an association strengthened with younger age and increased white matter disease severity. Incidentally discovered covert brain infarction did not appear to be associated with subsequent Parkinson disease. ANN NEUROL 2022;92:620-630.

Exciting-frequency-adaptive amplitude/phase hybrid holographic inscription in plasmonic polymers.

Plasmonic holography is generally regarded as an effective technology for 3D display that meets the requirements of the human visual system. However, low readout stability and large cross talk in the frequency field during a plasmonic photo-dissolution reaction set a huge obstacle for application of color holography. Herein, we propose a new, to the best of our knowledge, route toward producing exciting frequency sensitive holographic-inscription based on plasmonic nano-Ag adaptive growth. Donor-molecule-doped plasmonic polymers on polyethylene terephthalate substrates exhibit wide spectral response range, accurate optical frequency sensing, and bending durability. The resonant plasmonic particles act as optical antennas and transfer energy to surrounding organic matrices for nanocluster production and non-resonant particle growth. The surface relief hologram is also highly dependent on the excitation frequency, so we successfully obtain a controllable cross-periodic structure with amplitude/phase mixed information, as well as color holographic display. This work provides a bright way to high-density storage, information steganography, and virtual/augmented reality.

Semi-spontaneous temporal evolution of relief/fluorescence hybrid gratings for holographic encryption.

Holographic systems can reconstruct the entire wavefront of light which are developed as an excellent platform of information encryption. Although holography has utilized multiple modulation dimensions, little attention is given to its combination with fluorescence emitting. Herein, we propose a semi-spontaneous time-dependent encryption strategy of hybrid holographic fringes with surface relief and fluorescent emission mediated by a plasmonic polymer doped with fluorescent dyes. It is found that the two kinds of optical characteristic regions exhibit unique temporal evolution from the overlapped mode to the staggered one. The mode switching is closely related to the strong quenching effect of gold ions and nanoparticles which are dominant at the early and later recording stages, respectively. Thus, the real and deceptive information are recorded at different holographic writing periods. High-secret information of texts or images is constructed by the array of different sets of holographic fringes and is identified by comparing the dual-channel results of confocal laser scanning microscopes. This work puts a bright way to dynamic holographic encryption.

Destruction of a Copper Metal-Organic Framework to Induce CuPt Growth as a Heterojunction Catalyst for Hydrogen Peroxide Sensing.

Assembling bimetallic alloys (BAs) with metal-organic frameworks (MOFs) to form heterojunctions has emerged as a promising strategy for the construction of highly active electrocatalysts. However, the current approaches to prepare BA@MOF heterojunctions suffer from poor controllability. In this work, a fascinating method involving partial thermal reduction and galvanic replacement to induce CuPt growth on a CuHHTP MOF (HHTP=2,3,6,7,10,11-hexahydroxytriphenylene) is reported in order to construct a CuPt@CuHHTP heterojunction. The size of the CuPt nanoparticles can be effectively regulated by modifying the reduction temperature. The resultant CuPt NP@CuHHTP heterojunction nanoarrays exhibit high electrocatalytic activity and potential as an electrochemical H2 O2 sensor with a low detection limit (5 nM), high sensitivity (6.942 mA ⋅ mM-1 ⋅ cm-2 ), and outstanding selectivity. This in situ approach provides not only new insights into the preparation of BA@MOF-based heterojunctions but also an effective approach for the optimization of the catalytic performance of MOFs and related materials.