Structural Features Research Articles

SAH-NET: Structure-Aware Hierarchical Network for Clustered Microcalcification Classification in Digital Breast Tomosynthesis.

Benign and malignant classification of clustered microcalcifications (MCs) in digital breast tomosynthesis (DBT) is an essential task in computer-aided diagnosis. However, due to the anisotropic resolution of DBT, three-dimensional (3-D) convolutional neural network (CNN)-based methods cannot extract hierarchical features efficiently. Moreover, the sparse distribution of MC points in the cluster makes it difficult for the CNN to extract discriminative structural information for classification. To comprehensively address these challenges, we propose a novel structure-aware hierarchical network (SAH-Net) for benign and malignant classification of clustered MC in a DBT volume. Specifically, the two-dimensional (2-D) group convolution is used to extract intraslice features. The one-to-one correspondence between group convolutions and slices ensures the independence of hierarchical feature extraction. Then, a partial deformable Transformer-based 3-D structural feature learning module is proposed to capture the long-range dependency between MC points in the cluster. We evaluate the proposed method on an in-house dataset with 495 clustered MCs collected from 462 DBT images. Experimental results confirm the validity of our proposed modules. The results also show that the proposed SAH-Net outperforms several other representative methods on this topic, and achieves the best classification result, with an area under the receiver operation curve (AUC) of 86.87%. The implementation of the proposed model is available at https://github.com/sunhaotian130911/SAHNet.

Hard Carbon Derived From Different Precursors for Sodium Storage.

Due to the almost unlimited resource and acceptable performance, Sodium-ion batteries (SIBs) have been regarded as a promising alternative for lithium-ion batteries (LIBs) for grid-scale energy storage. As the key material of SIBs, hard carbon (HC) plays a decisive role in determining the batteries' performance. Nevertheless, the micro-structure of HCs is quite complex and the random organization of turbostratically stacked graphene layers, closed pores, and defects make the structure-performance relationship insufficiently revealed. On the other hand, the impending large-scale deployment of SIBs leads to producing HCs with low-cost and abundant precursors actively pursued. In this work, the recent progress of preparing HCs from different precursors including biomass, polymers, and fossil fuels is summarized with close attention to the influences of precursors on the structural evolution of HCs. After a brief introduction of the structural features of HCs, the recent understanding of the structure-performance relationship of HCs for sodium storage is summarized. Then, the main focus is concentrated on the progress of producing HCs from distinct precursors. After that, the pros and cons of HCs derived from different precursors are comprehensively compared to conclude the selection rules of precursors. Finally, the further directions of HCs are deeply discussed to end this review.

De novo GRIN variants in M3 helix associated with neurological disorders control channel gating of NMDA receptor

N-methyl-d-aspartate receptors (NMDARs) are members of the glutamate receptor family and participate in excitatory postsynaptic transmission throughout the central nervous system. Genetic variants in GRIN genes encoding NMDAR subunits are associated with a spectrum of neurological disorders. The M3 transmembrane helices of the NMDAR couple directly to the agonist-binding domains and form a helical bundle crossing in the closed receptors that occludes the pore. The M3 functions as a transduction element whose conformational change couples ligand binding to opening of an ion conducting pore. In this study, we report the functional consequences of 48 de novo missense variants in GRIN1, GRIN2A, and GRIN2B that alter residues in the M3 transmembrane helix. These de novo variants were identified in children with neurological and neuropsychiatric disorders including epilepsy, developmental delay, intellectual disability, hypotonia and attention deficit hyperactivity disorder. All 48 variants in M3 for which comprehensive testing was completed produce a gain-of-function (28/48) compared to loss-of-function (9/48); 11 variants had an indeterminant phenotype. This supports the idea that a key structural feature of the M3 gate exists to stabilize the closed state so that agonist binding can drive channel opening. Given that most M3 variants enhance channel gating, we assessed the potency of FDA-approved NMDAR channel blockers on these variant receptors. These data provide new insight into the structure–function relationship of the NMDAR gate, and suggest that variants within the M3 transmembrane helix produce a gain-of-function.

Studies on foamed concrete micropores and their effects on stress distribution and heat conduction

Further research on the pore structure of foamed concrete contribute to expand the application of energy-efficient buildings. In this paper, the micropore structure features of foamed concrete were characterized by nuclear magnetic resonance. It is found that the pore size of foamed concrete ranged from 1 nm to 74.84 μm. In order to understand the effect of micropores on the performance of foamed concrete, the models with (Model I) and without micropores (Model II) were established. Besides, the heat conduction and stress distribution of foamed concrete were simulated numerically. Meanwhile, the thermal conductivity and compressive strength were tested as the reference for numerical simulations. The results show that the heat conduction flux and temperature gradient of Model I were increased by 12%–13% and the thermal conductivity was increased by 2.63% compared to Model II. Thermal conductivity of Model I was closer to the experimental results. The stress of Model I was increased by 1.32%–54.66% compared to that of Model II. This indicates that the micropores can significantly increase the stress concentration in the foamed concrete.

Substantial composition and technological treatment of potassium rocks of the Upper Pechora salt basin

The material composition of potassium and potassium-magnesium salts of the Upper Pechora salt-bearing basin was studied, their lithological variability was established over the area of productive strata, depending on the tectonic features of the foundation. The main mining and geological conditions that complicate the possibility of their extraction and the environmental conditions for organizing mining and processing production of mineral salts were established. The basic physical and technological properties of sylvinite and carnallite rocks mined by the mine method were studied for their enrichment by halurgical and flotation methods. The objectives of the work were: determining the material composition, studying textural and structural features and assessing the technological processing of potassium rocks in the Upper Pechora salt-bearing basin. When solving the problems, the following methods were used: a study of the textural and structural features of sylvinites using the petrographic method; the chemical composition of samples and solutions was analyzed by AES spectrometry; the mineral composition of the water-insoluble residue is determined by X-ray quantitative phase analysis.

G-K BertDTA: A graph representation learning and semantic embedding-based framework for drug-target affinity prediction

Developing new drugs is costly, time-consuming, and risky. Drug-target affinity (DTA), indicating the binding capability between drugs and target proteins, is a crucial indicator for drug development. Accurately predicting interaction strength between new drug-target pairs by analyzing previous experiments aids in screening potential drug molecules, repurposing them, and developing safe and effective medicines. Existing computational models for DTA prediction rely on strings or single-graph neural networks, lacking consideration of protein structure and molecular semantic information, leading to limited accuracy. Our experiments demonstrate that string-based methods may overlook protein conformations, causing a high root mean square error (RMSE) of 3.584 in affinity due to a lack of spatial context. Single graph networks also underperform on topology features, with a 6% lower confidence interval (CI) for activity classification. Absent semantic information also limits generalization across diverse compounds, resulting in 18% increment in RMSE and 5% in misclassifications within quantifications study, restricting potential drug discovery. To address these limitations, we propose G-K BertDTA, a novel framework for accurate DTA prediction incorporating protein features, molecular semantic features, and molecular structural information. In this proposed model, we represent drugs as graphs, with a GIN employed to learn the molecular topological information. For the extraction of protein structural features, we utilize a DenseNet architecture. A knowledge-based BERT semantic model is incorporated to obtain rich pre-trained semantic embeddings, thereby enhancing the feature information. We extensively evaluated our proposed approach on the publicly available benchmark datasets (i.e., KIBA and Davis), and experimental results demonstrate the promising performance of our method, which consistently outperforms previous state-of-the-art approaches. Code is available at https://github.com/AmbitYuki/G-K-BertDTA.

Application of co-crystallization method for the production of ammonium perchlorate/ammonium nitrate oxidizer for solid rocket propellants

Efforts in the quest for greener alternatives to ammonium perchlorate (AP) as a solid rocket propellant oxidizer have intensified due to the associated health and environmental concerns. AP exceptional performance in propulsion is accompagnied by its environmental concerns, necessitating the exploration of greener alternatives. Ammonium nitrate (AN) has emerged as a promising substitute, offering cleaner combustion products and cost-effectiveness. AN-based propellants face challenges such as hygroscopicity and structural instability. This study aims to reconcile the latter challenges by employing a co-crystallization process that embodies a synergistic association of AP and AN, culminating in a novel molecule that harmonizes their distinct advantages and mitigates their individual shortcomings. Optimizing the solvent/antisolvent co-crystallization process through a variation of the antisolvent temperature Ta and antisolvent-to-solvent ratio Ra/s allows to assess their impact on the co-crystallization process of AP and AN and to attain the desired and optimal co-crystal formation conditions. Various characterization techniques were applied to gain deep insights into the co-crystal formation mechanism, its thermal behavior, morphological and structural features together with its thermal decomposition kinetics. Maintaining a low Ta and a high Ra/s (Ta = 15 °C, Ra/s = 11) yielded the design of AP/AN that demonstrates typical indications of the cocrystal formation. The AP/AN cocrystal formation elevated the decomposition temperature by about 18 °C compared to the physical mixture. Furthermore, the combination of AP and AN demonstrates a non-negligible decrease in the HTD temperature compared to that of pure AP by about 110 °C. The kinetic study demonstrated a 50 kJ/mol increase in the activation energy of the AP/AN cocrystal compared to that of the physical mixture. The present research addresses the critical gap in the literature by investigating the co-crystallization of AP and AN that not only improves the safety and environmental impact of solid propellants but also enhances their overall performance and stability.

Oxalate-mediated synthesis of hybrid nickel cobalt-based nanostructures for boosting water and urea electrooxidation efficiency

The utilization of urea during electrolysis presents an appealing option for anodic oxidation, offering environmental benefits by converting waste into energy. The present work focuses on synthesizing mixed transition metal oxide (MMO) nanostructures composed of nickel rich nickel cobaltite electrode materials. Interesting, cobalt rich nickel cobaltite have been widely studied; however, the nickel-rich nickel cobaltite remains relatively unexplored. For stabilization of nickel rich nickel cobaltite, we have designed a process for synthesizing three-dimensional (3D) Ni-Co oxalate as a precursor via the coprecipitation method using diammonium oxalate monohydrate as a precipitating agent. The as obtained precursor was further subjected to thermal decomposition which results in the formation of Ni2CoO4. Microstructural analysis of Ni-Co oxalate indicates that oxalate-assisted synthesis leads to anisotropic ultrafine nanoparticle growth, forming hierarchically arranged 3D microflowers. Interestingly, the use of NaOH as a precipitating agent induces a significant change in the morphology of Ni2CoO4, (nanoparticles). Furthermore, the effect of morphology changes over the electrochemical performance was investigated. Ni2CoO4 microflower demonstrates an overpotential of 330 mV@25 mA/cm2 for O2 and −190 mV@10 mA/cm2 for H2 production during water oxidation in 1 M KOH, exhibiting excellent stability for up to 24 h. During urea oxidation, the potential required by Ni2CoO4 microflowers was 1.31 V@10 mA/cm2. Ni2CoO4 nanoparticles' performance is significantly poor during water and urea oxidation compared to the microflower counterpart. Therefore, this work provides detailed insights into designing MMOs with high porosity, increased exposed active sites, and unique structural features that enhance electrocatalytic activity.

Polysubstitution Induced Centrosymmetric-to-Noncentrosymmetric Structural Transformation and Nonlinear-Optical Behavior: The Case of Na0.45Ag0.55Ga3Se5.

Obtaining compounds with large nonlinear-optical (NLO) coefficients and wide band gaps is challenging due to their competitive requirements for chemical bonds. Herein, the first member with mixed cations on the A site in the A-M3-Q5 or A-Ag-M6-Q10 (A = alkali metal; M = Ga, In; Q = S, Se, Te) family, viz. Na0.45Ag0.55Ga3Se5 (NAGSe), was obtained by a solid-state reaction. Its structure features [GaSe4] tetrahedra built three-dimensional {[Ga3Se5]-}∞ network, with Na and Na/Ag cations located at the octahedral cavities. Noncentrosymmetric (R32) NAGSe can also be transformed from centrosymmetric RbGa3S5 (P21/c) via multiple-site cosubstitution. NAGSe exhibits the highest NLO response (1.9 × AGS) in the A-Ag-M-Q family. Crystal structure analysis and theoretical calculations suggest that the NLO response is mainly contributed by the regularly arranged [GaSe4] units. This work enriches the exploration of the undeveloped A-M3-Q5 or A-Ag-M6-Q10 family as potential infrared NLO materials.

Structure and agency in resistance to schooling: Class, race, and the reproduction of unequal outcomes

For low-income and marginalized racialized minority youth, declining prospects of mobility can undermine favorable attitudes toward schooling if adolescents anticipate limited utility in schooling. We find that adolescents' awareness of race and class inequality affects a complex set of attitudes toward schooling, and that these attitudes contribute to outcomes varying by race/ethnicity and class. We capitalize on a unique longitudinal dataset with a random stratified sample of 1428 Black and White high school graduates from a large school system. Using surveys and administrative data, we show how structural factors and student characteristics shape educational attitudes; and then how these attitudes, school structural features, student, family, and neighborhood factors predict educational outcomes. We find the common ground between Willis' resistance theory emphasizing class and Ogbu’s cultural-ecological model focusing on race. Results provide greater conceptual clarity for core constructs associated with both theories of resistance.

Tailoring thin film composite membranes for clean water production: A study on structural variations and predictive insights using machine learning

This study focuses on the fabrication of three thin film composite membranes by interfacial polymerization on polyacrylonitrile support. The objective is to explore the influence of different monomers on structural and functional features of resultant membranes for treating saline water and removing micropollutants. A new linear aliphatic amine 4A–2E was used as an aqueous monomer for the first time. Trimesoyl chloride, isophthaloyl chloride, and terephthaloyl chloride were used as organic phase crosslinkers. The resulting membranes exhibited distinctive structural features such as varied roughness and hydrophilicity, which correlated with their functional performance. The membrane roughness for 4A–2E-TPC@PAN/PET reached the highest value of Ra = 56 nm among the fabricated membranes. Moreover, permeate flux of 32, 56, and 49 L m−2 h−1 at 25 bar was measured for 4A–2E-TPC@PAN/PET, 4A–2E-IPC@PAN/PET, and 4A–2E-TMC@PAN/PET, respectively. Notably, 4A–2E-TPC@PAN/PET membrane exhibited 98% rejection of MgCl2 among the tested divalent salts. Furthermore, the membrane displayed high rejections of micropollutants, with 85% rejection for Amitriptyline. Upon chlorine exposure, the 4A–2E-TPC@PAN/PET membrane proved more resistant than both 4A–2E-IPC@PAN/PET and 4A–2E-TMC@PAN/PET membranes. Machine learning predictive insight was developed using Matérn Gaussian Process Regression model to simulate flux before and after chlorination of membranes. The results in both training and testing proved reliable predictive insight with 99% accuracy in terms of Nash Sutcliffe Efficiency. Hence, current work highlights the use of a linear amine 4A–2E and TPC crosslinker to fabricate a dense polyamide membrane and the application of ML algorithms to desalination data makes it possible to predict the membrane performance.

DRFNet: a deep radiomic fusion network for nAMD/PCV differentiation in OCT images.

Objective.Neovascular age-related macular degeneration (nAMD) and polypoidal choroidal vasculopathy (PCV) present many similar clinical features. However, there are significant differences in the progression of nAMD and PCV. and it is crucial to make accurate diagnosis for treatment. In this paper, we propose a structure-radiomic fusion network (DRFNet) to differentiate PCV and nAMD in optical coherence tomography (OCT) images.Approach.The subnetwork (RIMNet) is designed to automatically segment the lesion of nAMD and PCV. Another subnetwork (StrEncoder) is designed to extract deep structural features of the segmented lesion. The subnetwork (RadEncoder) is designed to extract radiomic features from the segmented lesions based on radiomics. 305 eyes (155 with nAMD and 150 with PCV) are included and manually annotated CNV region in this study. The proposed method was trained and evaluated by 4-fold cross validation using the collected data and was compared with the advanced differentiation methods.Main results.The proposed method achieved high classification performace of nAMD/PCV differentiation in OCT images, which was an improvement of 4.68 compared with other best method.Significance. The presented structure-radiomic fusion network (DRFNet) has great performance of diagnosing nAMD and PCV and high clinical value by using OCT instead of indocyanine green angiography.

Bacterial hemophilin homologs and their specific type eleven secretor proteins have conserved roles in heme capture and are diversifying as a family.

Cellular life relies on enzymes that require metals, which must be acquired from extracellular sources. Bacteria utilize surface and secreted proteins to acquire such valuable nutrients from their environment. These include the cargo proteins of the type eleven secretion system (T11SS), which have been connected to host specificity, metal homeostasis, and nutritional immunity evasion. This Sec-dependent, Gram-negative secretion system is encoded by organisms throughout the phylum Proteobacteria, including human pathogens Neisseria meningitidis, Proteus mirabilis, Acinetobacter baumannii, and Haemophilus influenzae. Experimentally verified T11SS-dependent cargo include transferrin-binding protein B (TbpB), the hemophilin homologs heme receptor protein C (HrpC), hemophilin A (HphA), the immune evasion protein factor-H binding protein (fHbp), and the host symbiosis factor nematode intestinal localization protein C (NilC). Here, we examined the specificity of T11SS systems for their cognate cargo proteins using taxonomically distributed homolog pairs of T11SS and hemophilin cargo and explored the ligand binding ability of those hemophilin cargo homologs. In vivo expression in Escherichia coli of hemophilin homologs revealed that each is secreted in a specific manner by its cognate T11SS protein. Sequence analysis and structural modeling suggest that all hemophilin homologs share an N-terminal ligand-binding domain with the same topology as the ligand-binding domains of the Haemophilus haemolyticus heme binding protein (Hpl) and HphA. We term this signature feature of this group of proteins the hemophilin ligand-binding domain. Network analysis of hemophilin homologs revealed five subclusters and representatives from four of these showed variable heme-binding activities, which, combined with sequence-structure variation, suggests that hemophilins are diversifying in function.IMPORTANCEThe secreted protein hemophilin and its homologs contribute to the survival of several bacterial symbionts within their respective host environments. Here, we compared taxonomically diverse hemophilin homologs and their paired Type 11 secretion systems (T11SS) to determine if heme binding and T11SS secretion are conserved characteristics of this family. We establish the existence of divergent hemophilin sub-families and describe structural features that contribute to distinct ligand-binding behaviors. Furthermore, we demonstrate that T11SS are specific for their cognate hemophilin family cargo proteins. Our work establishes that hemophilin homolog-T11SS pairs are diverging from each other, potentially evolving into novel ligand acquisition systems that provide competitive benefits in host niches.

A cellulosomal double-dockerin module from Clostridium thermocellum shows distinct structural and cohesin-binding features.

Cellulosomes are intricate cellulose-degrading multi-enzymatic complexes produced by anaerobic bacteria, which are valuable for bioenergy development and biotechnology. Cellulosome assembly relies on the selective interaction between cohesin modules in structural scaffolding proteins (scaffoldins) and dockerin modules in enzymes. Although the number of tandem cohesins in the scaffoldins is believed to determine the complexity of the cellulosomes, tandem dockerins also exist, albeit very rare, in some cellulosomal components whose assembly and functional roles are currently unclear. In this study, we characterized the structure and mode of assembly of a tandem bimodular double-dockerin, which is connected to a putative S8 protease in the cellulosome-producing bacterium, Clostridium thermocellum. Crystal and NMR structures of the double-dockerin revealed two typical type I dockerin folds with significant interactions between them. Interaction analysis by isothermal titration calorimetry and NMR titration experiments revealed that the double-dockerin displays a preference for binding to the cell-wall anchoring scaffoldin ScaD through the first dockerin with a canonical dual-binding mode, while the second dockerin module was unable to bind to any of the tested cohesins. Surprisingly, the double-dockerin showed a much higher affinity to a cohesin from the CipC scaffoldin of Clostridium cellulolyticum than to the resident cohesins from C. thermocellum. These results contribute valuable insights into the structure and assembly of the double-dockerin module, and provide the basis for further functional studies on multiple-dockerin modules and cellulosomal proteases, thus highlighting the complexity and diversity of cellulosomal components.

In Situ Flash Synthesis of Ultra-High-Performance Metal Oxide Anode through Shunting Current-Based Electrothermal Shock.

The synthesis of anode materials plays an important role in determining the production efficiency, cost, and performance of lithium-ion batteries (LIBs). However, a low-cost, high-speed, scalable manufacturing process of the anode with the desired structural feature for practical technology adoption remains elusive. In this study, we propose a novel method called in situ flash shunt-electrothermal shock (SETS) which is controllable, fast, and energy-saving for synthesizing metal oxide-based materials. By using the example of direct electrothermal decomposition of ZIF-67 precursor loaded onto copper foil support, we achieve rapid (0.1-0.3 s) pyrolysis and generate porous hollow cubic structure material consisting of carbon-coated ultrasmall (10-15 nm) subcrystalline CoO/Co nanoparticles with controllable morphology. It was shown that CoO/Co@N-C exhibits prominent electrochemical performance with a high reversible capacity up to 1503.7 mA h g-1 after 150 cycles at 0.2 A g-1and stable capacities up to 434.1 mA h g-1 after 400 cycles at a high current density of 6 A g-1. This fabrication technique integrates the synthesis of active materials and the formation of electrode sheets into one process, thus simplifying the preparation of electrodes. Due to the simplicity and scalability of this process, it can be envisaged to apply it to the synthesis of metal oxide-based materials and to achieve large-scale production in a nanomanufacturing process.

Protein Homology Modeling in the Low Sequence Similarity Regime

Predicting the 3-D structure of a protein from its sequence based on a template protein structure is still one of the most exact modeling techniques present today. However, template-based modeling is heavily dependent on the selection of a single template structure and the sequence alignment between target and template. Mainly when the target and template sequence identity is low, the error from the alignment introduces larger errors to the model structure. An iterative method to correct such alignment mistakes is used in this study with a benchmark set from CASP in the extremely low sequence-identity regime. This is a protocol developed and tested before and it evaluates the alignment quality by building rough 3-D models for each alignment. Then by using a genetic algorithm it iteratively creates a new set of alignments. Since the method evaluates models, not sequence alignments, structural features are automatically incorporated into the alignment protocol. In the current study, models from structural alignment have been built by Modeller program to show the maximum possible quality of the model that can be obtained from that template structure with the iterative modeling protocol. Then the results and correctly aligned segments from the iterative modeling protocol are analyzed. Finally, it has been shown that if a good local fragment assessment scoring function is developed, the correctly aligned segments exist in the pool of alignments created by the protocol. Thus, the improvement of modeling in the low sequence identity regime is conceivable.

A deep learning approach for quantum dots sizing from wide-angle X-ray scattering data

AbstractDisclosing the full potential of functional nanomaterials requires the optimization of synthetic protocols and an effective size screening tool, aiming at triggering their size-dependent properties. Here we demonstrate the successful combination of a wide-angle X-ray total scattering approach with a deep learning classifier for quantum dots sizing in both colloidal and dry states. This work offers a compelling alternative to the lengthy process of deriving sizing curves from transmission electron microscopy coupled with spectroscopic measurements, especially in the ultra-small size regime, where empirical functions exhibit larger discrepancies. The core of our algorithm is an all-convolutional neural network trained on Debye scattering equation simulations, incorporating atomistic models to capture structural and morphological features, and augmented with physics-informed perturbations to account for different predictable experimental conditions. The model performances are evaluated using both wide-angle X-ray total scattering simulations and experimental datasets collected on lead sulfide quantum dots, resulting in size classification accuracies surpassing 97%. With the developed deep learning size classifier, we overcome the need for calibration curves for quantum dots sizing and thanks to the unified modeling approach at the basis of the total scattering method implemented, we include simultaneously structural and microstructural aspects in the classification process. This algorithm can be complemented by incorporating input information from other experimental observations (e.g., small angle X-ray scattering data) and, after proper training with the pertinent simulations, can be extended to other classes of quantum dots, providing the nanoscience community with a powerful and broad tool to accelerate the development of functional (nano)materials.

Surface modification of ZrC dispersion-strengthened W under low energy He plasma irradiation

ZrC dispersion-strengthened W exhibits high strength/ductility, low ductile-to-brittle transition temperature, and excellent thermal shock resistance, making it a promising candidate plasma-facing material for future fusion devices. In this study, surface modification of 0.5 wt.% ZrC dispersion-strengthened W (WZrC) under low energy and high fluence He plasma irradiation at high temperature was presented. Under the energy of 90 eV and fluence ranging from 6 × 1024 He·m−2–2 × 1026 He·m−2 He irradiation at 920 °C, a typical fuzz nanostructure appeared on the W matrix of WZrC. The thickness of fuzz layer is proportional to the square root of He irradiation fluence. The fuzz showed comparable thickness and structural features to pure W, indicating limited effects of the particle’s addition on resistance to high fluence He irradiation at high temperatures. Under continuous He injection, the fuzz would grow extending onto the particle area, making the particle obscured. Besides, the erosion behavior of particles under He plasma irradiation has been investigated, which is thought to be dominated by a sputtering process. Under the He influence of 6 × 1024 He·m−2, only nanopores were observed in the surface region. With fluence increasing to 5 × 1025 He·m−2, the surface became relatively uneven with larger holes. W aggregated in spots and distributed on the surface of the particle, which might be the result of subthreshold sputtering and deposition. When fluence further increased to 2 × 1026 He·m−2, the particles were eroded completely and covered by the extended fuzz, forming cavities. In addition, distinctive layered nanotendrils were observed above the cavities, which were characterized to consist of inner W-riched skeletons and outer Zr-riched layers. It indicates that the layered nanotendrils should be the result of fuzz extension combined with particle sputtering/deposition.

Mixed‐Linker Strategy for the Construction of Sulfone‐Containing D–A–A Covalent Organic Frameworks for Efficient Photocatalytic Hydrogen Peroxide Production

AbstractThe solar‐driven photocatalytic production of hydrogen peroxide (H2O2) from water and oxygen using semiconductor catalysts offers a promising approach for converting solar energy into storable chemical energy. However, the efficiency of photocatalytic H2O2 production is often restricted by the low photo‐generated charge separation, slow surface reactions and inadequate stability. Here, we developed a mixed‐linker strategy to build a donor‐acceptor‐acceptor (D–A–A) type covalent organic framework (COF) photocatalyst, FS‐OHOMe‐COF. The FS‐OHOMe‐COF structure features extended π–π conjugation that improves charge mobility, while the introduction of sulfone units not only as active sites facilitates surface reactions with water but also bolsters stability through increased interlayer forces. The resulting FS‐OHOMe‐COF has a low exciton binding energy, long excited‐state lifetime and high photo‐stability that leads to high performance for photocatalytic H2O2 production (up to 1.0 mM h−1) with an H2O2 output of 19 mM after 72 hours of irradiation. Furthermore, the catalyst demonstrates high stability, which sustained activity over 192 hours of photocatalytic experiment.

Therapeutic Peptides, Proteins and their Nanostructures for Drug Delivery and Precision Medicine.

Peptide and protein nanostructures with tunable structural features, multifunctionality, biocompatibility and biomolecular recognition capacity enable development of efficient targeted drug delivery tools for precision medicine applications. In this review article, we present various techniques employed for the synthesis and self-assembly of peptides and proteins into nanostructures. We discuss design strategies utilized to enhance their stability, drug-loading capacity, and controlled release properties, in addition to the mechanisms by which peptide nanostructures interact with target cells, including receptor-mediated endocytosis and cell-penetrating capabilities. We also explore the potential of peptide and protein nanostructures for precision medicine, focusing on applications in personalized therapies and disease-specific targeting for diagnostics and therapeutics in diseases such as cancer.