Desirable Structure Research Articles

Operando Visualization of Redox Flow Batteries Using Fluorescence Spectroscopy with Alizarin Red S Electrolyte

Redox flow batteries (RFBs) charge and discharge by transferring electrons between two pairs of redox species separated by a proton exchange membrane. Unlike traditional batteries that store energy in redox-active materials within an enclosed cell, RFBs store energy in liquid electrolytes housed in external reservoirs1. These electrolytes are pumped through the cell, enabling redox reactions on the surface of electrodes. The energy storage capacity of RFBs is determined by the concentration of the active species and the size of the reservoirs, making them a promising technology for large-scale energy storage applications2. While RFBs possess advantages such as long lifetime, flexible design for battery capacity and power, and rapid response, they suffer from drawbacks like low energy density, power density, and energy efficiency3. Efforts to address these issues have primarily focused on modifications to the electrode materials or the flow field4 , 5. However, evidence supporting performance enhancements often relies on global parameters such as energy efficiency, energy density, and voltage drop, lacking detailed mechanistic and microscopic studies. Real-time observation of the electrode-electrolyte interface during redox reactions and the transport of redox species in the flow field holds the potential to significantly contribute to understanding the electrode structure and mass transport phenomena in RFBs6. This understanding, in turn, facilitates prediction and purposeful enhancement of battery performance by developing desirable structures for both electrode and flow fields. Microscopy, particularly confocal microscopy, serves as a powerful tool for observing microscopic structures. Confocal microscopes yield sharper images with higher resolution than widefield microscopy, enabling rapid acquisition of high-resolution images.In this study, we employed a confocal laser scanning microscope for operando visualization of RFBs. We designed a transparent framework for the battery, allowing light to pass through, and covered its surface with a glass cover to visualize the porous carbon electrode. We selected a fluorescent redox-active species, Alizarin Red S to enable visualization using fluorescence spectroscopy. Leveraging its unique fluorescence properties, we established a correlation between fluorescence intensity and the state of charge. This innovative approach enables us to observe the distribution of electrolytes, the dynamics of redox reactions on the electrode surface (with fast speed of imaging ＜0.2s), the mass transfer behavior of the electrolyte on the electrode surface, and the uniformity of electrolyte flow in the flow field, including areas of stagnation or recirculation. Zhang, J. et al. An all-aqueous redox flow battery with unprecedented energy density. Energy Environ. Sci. 11, 2010–2015 (2018).Rubio-Garcia, J., Kucernak, A. & Charleson, A. Direct visualization of reactant transport in forced convection electrochemical cells and its application to redox flow batteries. Electrochem. commun. 93, 128–132 (2018).Tolmachev, Y. V. Flow Batteries From 1879 To 2022 And Beyond. Qeios 1–79 (2023).Wang, R. et al. Gradient-distributed NiCo2O4 nanorod electrode for redox flow batteries: Establishing the ordered reaction interface to meet the anisotropic mass transport. Appl. Catal. B Environ. 332, 122773 (2023).Kumar, S. & Jayanti, S. Effect of flow field on the performance of an all-vanadium redox flow battery. J. Power Sources 307, 782–787 (2016).Wong, A. A., Rubinstein, S. M. & Aziz, M. J. Direct visualization of electrochemical reactions and heterogeneous transport within porous electrodes in operando by fluorescence microscopy. Cell Reports Phys. Sci. 2, 100388 (2021).

A framework for simulating genotype-by-environment interaction using multiplicative models

Key messageThe simulation of genotype-by-environment interaction using multiplicative models provides a general and scalable framework to generate realistic multi-environment datasets and model plant breeding programmes.Plant breeding has been historically shaped by genotype-by-environment interaction (GEI). Despite its importance, however, many current simulations do not adequately capture the complexity of GEI inherent to plant breeding. The framework developed in this paper simulates GEI with desirable structure using multiplicative models. The framework can be used to simulate a hypothetical target population of environments (TPE), from which many different multi-environment trial (MET) datasets can be sampled. Measures of variance explained and expected accuracy are developed to tune the simulation of non-crossover and crossover GEI and quantify the MET-TPE alignment. The framework has been implemented within the R package FieldSimR, and is demonstrated here using two working examples supported by R code. The first example embeds the framework into a linear mixed model to generate MET datasets with low, moderate and high GEI, which are used to compare several popular statistical models applied to plant breeding. The prediction accuracy generally increases as the level of GEI decreases or the number of environments sampled in the MET increases. The second example integrates the framework into a breeding programme simulation to compare genomic and phenotypic selection strategies over time. Genomic selection outperforms phenotypic selection by ∼\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sim $$\\end{document}50–70% in the TPE, depending on the level of GEI. These examples demonstrate how the new framework can be used to generate realistic MET datasets and model plant breeding programmes that better reflect the complexity of real-world settings, making it a valuable tool for optimising a wide range of breeding methodologies.

Numerical and experimental demonstrations of optical trapping and manipulation of a selective red blood cell using a photonic hook based on broken symmetry tapered fiber probe

Optical tweezers and trapping technologies have a crucial impact on scientific research by precisely manipulating mesoscale objects at wavelength scales. The development of simple fiber-based optical tweezers foreseeing sorting and manipulation of a single cell has been firstly encouraged by biomedical requirements. The ability to manipulate mesoscale objects with high flexibility and precision is essential for a wide class of different fields. The ability to precisely arrange cells into desired structures is important for numerous biomedical and physical applications. This study experimentally reports for the first time a simple and low-cost strategy to produce an optical fiber-based photonic hook for achieving new unprecedented functionalities to selective particle manipulation in a lab-on-tip platform. We show perspectives opened by combining the fields of structured light in the form of the photonic hook and optical manipulation method. This enables new applications for optical trapping setups based on structured fields in the form of the photonic hook produced by an optical fiber tip with broken symmetry, where it becomes possible to manipulate and sort cells selectively.

Effect of Pore Architecture of 3D Printed Open Porosity Cellular Structures on Their Resistance to Mechanical Loading: Part I – Experimental Studies

Abstract The development of additive manufacturing (AM) techniques has sparked interest in porous structures that can be customized in terms of size, shape, and arrangement of pores. Porous lattice structure (LS, called also lattice struct) offer superior specific stiffness and strength, making them ideal components for lightweight products with energy absorption and heat transfer capabilities. They find applications in industries such as aerospace, aeronautics, automotive, and bone ingrowth applications. One of the main advantages of additive manufacturing is the freedom of design, control over geometry and architecture, cost and time savings, waste reduction, and product customization. However, the designation of appropriate struct/pore geometry to achieve the desired properties and structure remains a challenge. In this part of the study, five lattice structs with various pore sizes, with two volume fractions for each, and shapes (ellipsoidal, helical, X-shape, trapezoidal, and triangular) were designed and manufactured using selective laser sintering (SLS) additive manufacturing technology. Mechanical properties were tested through uniaxial compression, and the apparent stress-strain curves were analyzed. The results showed that the compression tests revealed both monotonic and non-monotonic stress-strain curves, indicating different compression behaviors among the structures. The helical structure exhibited the highest resistance to compression, while other structures showed similarities in their mechanical properties. In Part II of this study provides a comprehensive analysis of these findings, emphasizing the potential of purpose-designed porous structures for various engineering applications.

Machine-Learning-Based Characterization and Inverse Design of Metamaterials.

Metamaterials, characterized by unique structures, exhibit exceptional properties applicable across various domains. Traditional methods like experiments and finite-element methods (FEM) have been extensively utilized to characterize these properties. However, exploring an extensive range of structures using these methods for designing desired structures with excellent properties can be time-intensive. This paper formulates a machine-learning-based approach to expedite predicting effective metamaterial properties, leading to the discovery of microstructures with diverse and outstanding characteristics. The process involves constructing 2D and 3D microstructures, encompassing porous materials, solid-solid-based materials, and fluid-solid-based materials. Finite-element methods are then employed to determine the effective properties of metamaterials. Subsequently, the Random Forest (RF) algorithm is applied for training and predicting effective properties. Additionally, the Aquila Optimizer (AO) method is employed for a multiple optimization task in inverse design. The regression model generates accurate estimation with a coefficient of determination higher than 0.98, a mean absolute percentage error lower than 0.088, and a root mean square error lower than 0.03, indicating that the machine-learning-based method can accurately characterize the metamaterial properties. An optimized structure with a high Young's modulus and low thermal conductivity is designed by AO within the first 30 iterations. This approach accelerates simulating the effective properties of metamaterials and can design microstructures with multiple excellent performances. The work offers guidance to design microstructures in various practical applications such as vibration energy absorbers.

Artificial intelligence methods in dental radiography

Introduction: The application of artificial intelligence in dentistry can significantly improve the outcome of patient treatment and reduce human error. Computer systems and certain methods that use complex algorithms are able to encode digital data about the radiographic image, obtained by X-ray radiation, and transfer them to a computer language for processing a large set of data. Applied as an auxiliary tool, it can give information about the patient and diagnostics of teeth and surrounding dental structures on radiographic images (two-dimensional and three-dimensional). The aim of the paper: The aim of the paper is to present the development and application of artificial intelligence in dentistry and at the same time emphasizing the importance of segmentation methods and methods of improving the quality of dental radiographic images. Discussion: The common goal of the development and application of artificial intelligence methods in dental radiography is directed on obtaining radiograms from which diagnostically valuable information will be collected faster and easier for the purpose of successful treatment of the patients. Methods of segmentation of a dental structures have been developed to automatically number and localize the tooth or independently mark and display the desired structures on the image. Methods of image quality enhancement tend to work on, for example, improving image resolution or reducing metal artifacts. Research has shown that artificial intelligence can be applied in everyday clinical practice, but it has certain limitations. Conclusion: Artificial intelligence methods in dental radiography are an useful auxiliary tool in clinical dental practice because they can speed up the process of retrieving patient data. Automatic localization of dental structure reduces the dentist's time for manual image analysis, but cannot completely replace human knowledge. For the full implementation of the mentioned methods, more development standards and resources are needed to overcome the ethical and legal problems of replacing humans with computer systems.

Epidermal growth factor-loaded, dehydrated physical microgel-formed adhesive hydrogel enables integrated care of wet wounds

Integrated wound care, a sequential process of promoting wound hemostasis, sealing, and healing, is of great clinical significance. However, the wet environment of wounds poses formidable challenges for integrated care. Herein, we developed an epidermal growth factor (EGF)-loaded, dehydrated physical microgel (DPM)-formed adhesive hydrogel for the integrated care of wet wounds. The DPMs were designed using the rational combination of hygroscopicity and reversible crosslinking of physical hydrogels. Unlike regular bioadhesives, which consider interfacial water as a barrier to adhesion, DPMs utilize water to form desirable adhesive structures. The hygroscopicity allowed the DPMs to absorb interfacial water and subsequently, the interfacial adhesion was realized by the interactions between tissue and DPMs. The reversible crosslinks further enabled DPMs to integrate into hydrogels (DPM-Gels), thus achieving wet adhesion. Importantly, the water-absorbing gelation mode of DPMs enabled facile loading of biologically active EGF to promote wound healing. We demonstrated that the DPM-Gels possessed wet tissue adhesive performance, with about 40 times the wet adhesive strength of fibrin glue and about 4 times the burst pressure of human blood pressure. Upon application at the injury site, the EGF-loaded DPM-Gels sequentially promoted efficient wound hemostasis, stable sealing, and quick healing, achieving integrated care of wet wounds.

Template-Assisted in situ synthesis of superaerophobic bimetallic MOF composites with tunable morphology for boosted oxygen evolution reaction

CoFe bimetallic organic frameworks (CoFe-MOFs) with tunable morphology and electronic structure are synthesized in situ utilizing cobalt hydroxide (Co(OH)2) as a semi-sacrificial template and different anionic iron salts as modifying factors in a non-calcined synthesis method. This work defines the impact of three different anionic metallic iron salts (FeCl3, Fe(NO3)3, and Fe2(SO4)3) on the morphology of MOF materials and their resulting oxygen evolution reaction (OER) catalytic activity. Employing ferric chloride (FeCl3) as the metallic iron source, heterostructured electrocatalysts (BN-CoFe-MOF) with nanoparticles decorated nanoneedle tips are obtained, exhibiting a low overpotential (230 mV at 10 mA cm−2) and a Tafel slope of 105.6 mV dec−1 in 1.0 M KOH. It also demonstrates long time stability for at least 50 h at a current density of 10 mA cm−2. The investigation uncovers that the splendid OER activity and stability of the BN-CoFe-MOF heterojunction can be attributed to its large specific surface area, desirable mesoporous structure, superaerophobic characteristic, and high exposure of active centers. This work not only provides an efficient and cost-effective MOF based OER electrocatalyst but also serves as a valuable reference for future research on morphology control and strategies to enhance the OER activity of MOF catalysts.

Synthesis, Jahn-Teller labeled via crystal structure in trans-(ClO4)2CuII(Me2N-Py)4 complex: S9/S6/C-H…O synthons, thermal, physicochemical and 1BNA-docking

The synthesis of the trans-(ClO4)2CuII(Me2N-Py)4 complex (where Me2N-Py = N,N-dimethylpyridin-4-amine ligand) in significant yield involved the reaction of excess Me2N-Py ligand with Cu(ClO4)2·6H2O in MeOH solvent. The progression of the complex synthesis was monitored by UV–Vis, FT-IR, EDX, XRD, MS, SEM, CHN-EA, and TG/DTG. The resulting complex crystallized in the Triclinic space P1¯. In the solid state, the desired complex structure discrete a neutral trans-(ClO4)2CuII(Me2N-Py)4 formula, with copper(II) adopting a distorted octahedral environment formed by four Me2N-Py ligands occupying the square planar position Cu(Py)4, meanwhile, the two ClO4 are in linear trans-position. The XRD measurements proved the existence of elongation Jahn-Teller distortion in the synthesized complex. Furthermore, a comprehensive analysis of the desired complex, including. XRD/HSA interactions revealed the presence of two S9 and S6 main 2D-synthons primarily formed through Cph-H….OClO3 and CMe-H…OClO3 non-classical H-bonding interactions. Thermal stability assessments through TG/DTG indicated that the complexes with the most stable behavior underwent two degradation steps. The molecular docking of the desired complex against 1BNA-DNA was also evaluated.

Analysis of False Negative Rates for Recycling Bloom Filters (Yes, They Happen!)

Bloom Filters are a desirable data structure for distinguishing new values in sequences of data (i.e., messages), due to their space efficiency, their low false positive rates (incorrectly classifying a new value as a repeat), and never producing false negatives (classifying a repeat value as new). However, as the Bloom Filter's bits are filled, false positive rates creep upward. To keep false positive rates below a reasonable threshold, applications periodically "recycle" the Bloom Filter, clearing the memory and then resuming the tracking of data. After a recycle point, subsequent arrivals of recycled messages are likely to be misclassified as new; recycling induces false negatives. Despite numerous applications of recycling, the corresponding false negative rates have never been analyzed. In this paper, we derive approximations, upper bounds, and lower bounds of false negative rates for several variants of recycling Bloom Filters. These approximations and bounds are functions of the size of memory used to store the Bloom Filter and the distributions on new arrivals and repeat messages, and can be efficiently computed on conventional hardware. We show, via comparison to simulation, that our upper bounds and approximations are extremely tight, and can be efficiently computed for megabyte-sized Bloom Filters on conventional hardware.

Gel‐forming properties of Bambara groundnut globulin and protein subfractions: structural and rheological characterisation

SummaryThe gel‐forming ability of the pulse proteins influences the formation of food products with desirable texture, structure, and stability. In this study, Bambara globulin was fractionated into legumin and vicilin for structural and physicochemical analyses, and heat‐induced gel rheological characterisation. Bambara globulin showed major vicilin (7S, Mw: 120 kDa) and minor legumin (11S, Mw: 410 kDa) components by size exclusion chromatography and gel electrophoresis analyses. Legumin had the lowest amount of all the charged amino acids. One basic subunit (22 kDa) was identified in the legumin fraction with predominant helical structures. The sol–gel transition temperatures increased in the order of globulin (40 °C) < legumin (50 °C) < vicilin (80 °C). The G′ and G″ of globulin showed relatively low dependency on heating time beyond the gel point compared to legumin and vicilin subfractions, suggesting a more rapid establishment of its gel network during gelation. Vicilin gel consisted of a microporous structure with a small lath sheet‐like structure compared to others. Bambara vicilin could be a prospective ingredient in the development of new products with defined textural characteristics.

Synthesis and characterization of Al-doped ZnO thin-films for photovoltaic applications

The investigation reports on the effect of aluminum-doped zin oxide (Al:ZnO), using sol–gel dip coating method for deposing thin films at different Al doping concentration values ​​(i.e., 0, 2, 3, 4, 5, and 10 atomic %).The as-grown films have been examined through structural and optical investigations. The X-ray pattern reveals the highly desired hexagonal wurtzite crystal structure that is orientated in a direction of (002). When the Al doping concentration rises from 0 % to 10 %, the average size of the crystallites decreases (from 22 nm to 19 nm). The optical properties measured using UV–visible spectroscopy indicated significant enhancement with Al doping concentration due to light scattering. Dopants have a slight effect on the band gap energy; fluctuations are seen between 3.21 and 3.25 eV. Measurements of ZnO and ZnO:Al thin films’ extinction coefficient and refractive index revealed that the doping concentration of Al had a small effect on both. When the Al content reaches 10 % at. %, the optical properties gradually approach their highest value, encouraging further exploration of its viability in solar cell applications.

Dual Information Enhanced Multiview Attributed Graph Clustering.

Multiview attributed graph clustering is an important approach to partition multiview data based on the attribute characteristics and adjacent matrices from different views. Some attempts have been made in using graph neural network (GNN), which have achieved promising clustering performance. Despite this, few of them pay attention to the inherent specific information embedded in multiple views. Meanwhile, they are incapable of recovering the latent high-level representation from the low-level ones, greatly limiting the downstream clustering performance. To fill these gaps, a novel dual information enhanced multiview attributed graph clustering (DIAGC) method is proposed in this article. Specifically, the proposed method introduces the specific information reconstruction (SIR) module to disentangle the explorations of the consensus and specific information from multiple views, which enables graph convolutional network (GCN) to capture the more essential low-level representations. Besides, the contrastive learning (CL) module maximizes the agreement between the latent high-level representation and low-level ones and enables the high-level representation to satisfy the desired clustering structure with the help of the self-supervised clustering (SC) module. Extensive experiments on several real-world benchmarks demonstrate the effectiveness of the proposed DIAGC method compared with the state-of-the-art baselines.

Direct Regeneration of Spent LiCoO2 Black Mass Based on Fluorenone‐Mediated Lithium Supplementation and Energy‐Saving Structural Restoration

AbstractDegraded LiCoO2 cathode from retired Li‐ion batteries is urgently required to be recycled in a greener way for economic and environmental considerations. The coarse metallurgy technologies for Li/Co extraction with massive CO2 emission and energy consumption cannot satisfy the requirements of carbon neutralization. Herein, it is proposed that direct regeneration of degraded LiCoO2 cathode could be realized via 9‐fluorenone‐mediated Li supplementation and follow‐up structural restoration. The 9‐fluorenone‐lithium reagent is elaborately selected to compensate for the missing Li+ into lattice with targeted stoichiometry owing to its compatible redox potential of 1.95 V versus Li+/Li, which is located just between the reversible intercalation (3.8 V) and irreversible conversion (1.2 V) potentials of LiCoO2 electrode. Then, thermal energy‐driven structure reorganization enables Li/Co atoms to occupy the right sites, accomplishing desirable structure healing within a short annealing time of 4 h. The regenerated LiCoO2 cathode exhibits comparable Li‐storage capability to commercial LiCoO2, benefiting from the non‐destructive direct regeneration technology. In addition, the regeneration route is regarded as environmentally (0.13 kg CO2 kg−1 cell) and economically (10.07 $ kg−1 cell) superior to conventional recycling routes based on life‐cycle analysis. The precise surgery on spent LiCoO2 cathode provides a promising solution for the forthcoming retirement rush of Li‐ion batteries.

Analysis of False Negative Rates for Recycling Bloom Filters (Yes, They Happen!)

Bloom Filters are a desirable data structure for distinguishing new values in sequences of data (i.e., messages), due to their space efficiency, their low false positive rates (incorrectly classifying a new value as a repeat), and never producing false negatives (classifying a repeat value as new). However, as the Bloom Filter's bits are filled, false positive rates creep upward. To keep false positive rates below a reasonable threshold, applications periodically "recycle" the Bloom Filter, clearing the memory and then resuming the tracking of data. After a recycle point, subsequent arrivals of recycled messages are likely to be misclassified as new; recycling induces false negatives. Despite numerous applications of recycling, the corresponding false negative rates have never been analyzed. In this paper, we derive approximations, upper bounds, and lower bounds of false negative rates for several variants of recycling Bloom Filters. These approximations and bounds are functions of the size of memory used to store the Bloom Filter and the distributions on new arrivals and repeat messages, and can be efficiently computed on conventional hardware. We show, via comparison to simulation, that our upper bounds and approximations are extremely tight, and can be efficiently computed for megabyte-sized Bloom Filters on conventional hardware.

Optimizing the Sintering Conditions of (Fe,Co)1.95(P,Si) Compounds for Permanent Magnet Applications.

(Fe,Co)2(P,Si) quaternary compounds combine large uniaxial magnetocrystalline anisotropy, significant saturation magnetization and tunable Curie temperature, making them attractive for permanent magnet applications. Single crystals or conventionally prepared bulk polycrystalline (Fe,Co)2(P,Si) samples do not, however, show a significant coercivity. Here, after a ball-milling stage of elemental precursors, we optimize the sintering temperature and duration during the solid-state synthesis of bulk Fe1.85Co0.1P0.8Si0.2 compounds so as to obtain coercivity in bulk samples. We pay special attention to shortening the heat treatment in order to limit grain growth. Powder X-ray diffraction experiments demonstrate that a sintering of a few minutes is sufficient to form the desired Fe2P-type hexagonal structure with limited secondary-phase content (~5 wt.%). Coercivity is achieved in bulk Fe1.85Co0.1P0.8Si0.2 quaternary compounds by shortening the heat treatment. Surprisingly, the largest coercivities are observed in the samples presenting large amounts of secondary-phase content (>5 wt.%). In addition to the shape of the virgin magnetization curve, this may indicate a dominant wall-pining coercivity mechanism. Despite a tenfold improvement of the coercive fields for bulk samples, the achieved performances remain modest (HC ≈ 0.6 kOe at room temperature). These results nonetheless establish a benchmark for future developments of (Fe,Co)2(P,Si) compounds as permanent magnets.

On the selective formation of cubic tetrastack crystals from tetravalent patchy particles.

Achieving the formation of target open crystalline lattices from colloidal particles is of paramount importance for their potential application in photonics. Examples of such desired structures are the diamond, tetrastack, and pyrochlore lattices. Here, we demonstrate that the self-assembly of tetravalent patchy particles results in the selective formation of cubic tetrastack crystals, both in the bulk and in the systems subjected to external fields exerted by the solid substrate. It is demonstrated that the presence of an external field allows for the formation of well-defined single crystals with a low density of defects. Moreover, depending on the strength of the applied external field, the mechanism of epitaxial growth changes. For weakly attractive external fields, the crystallization occurs in a similar manner as in the bulk, since the fluid does not wet the substrate. Nonetheless, the formed crystal is considerably better ordered than the crystals formed in bulk, since the surface induces the ordering in the first layer. On the other hand, it is demonstrated that the formation of well-ordered cubic tetrastack crystals is considerably enhanced by the increase in external field strength, and the formation of the thick crystalline film occurs via a series of layering transitions.

Highly bioactive hydroxyapatite coating made by flame spray technique

This research involves fabrication and characterization of flame sprayed hydroxyapatite (HA) coatings on 316 stainless steel substrates where the desirable structure with high porosity can be obtained. Nano-sized HA powder was synthesized and calcined at 600 °C. The subsequent powder was used as feedstock powder for flame spray technique (FS). In-flight particles of HA were spherical with the range of particle size between 3–37 µm. The average coating thickness was 128 µm and the crystalline phases were a mixture of HA and tricalcium phosphate (TCP). Simulated body fluid (SBF) test showed that HA layer was formed after 7 days. Highly bioactive properties were due to high surface area from rough coating and porous structure, which would be beneficial from the flame spray technique. The coating is promising for applications in biomedical field.

Efficient design of non-shrinkage xerogels via metal coordination and triazine steric hindrance for activated carbon xerogels with interconnected hierarchical structure

Designing xerogels at the molecular level to overcome volume shrinkage is a promising strategy for carbon xerogels with desirable structure and performance. Here, we design a xerogel with non-shrinkage by introducing ZnCl2 into resorcinol-melamine-formaldehyde polymerization. The gel network consisting of micrometer pores and large particles (0.26–1.35 μm) is constructed by the coordination of Zn2+ with oxygen/nitrogen-containing groups, which is attributed to the structural support of the rigid triazine skeleton with large steric hindrance. Therefore, the reinforced gel network possesses enough strength to withstand capillary forces during atmospheric drying, and special drying and solvent exchange are avoided. The xerogels show non-shrinkage and a short preparation time of 24 h. The resulted activated carbon xerogels with interconnected hierarchically micro‑meso-macropores exhibit an optimal specific surface area of 1520 m2/g (through xerogels pyrolysis and the pore-forming of ZnCl2), high adsorption (methylene blue, I–, Cu2+, etc.), and repeated adsorption ability. This work provides novel thought for porous nanomaterials with non-shrinkage and desirable structures in adsorption and energy storage.

Desirable banking competition and stability

Every financial crisis raises questions about how the banking market structure affects the real economy. Although low bank concentration may reduce markups and foster riskier behavior, concentrated banking systems appear more resilient to financial shocks. We use a nonlinear dynamic stochastic general equilibrium model with financial frictions to compare the transmissions of shocks under different competition and concentration configurations. The results reveal that oligopolistic competition amplifies the effects of the shocks relative to monopolistic competition. The transmission mechanism works through the markups, which are amplified when banking concentration is increased. The desirable banking market structure is determined according to financial stability and social welfare objectives. Moreover, we find that depending on policymakers’ preferences, a banking concentration of five to eight banks balances social welfare and bank stability objectives in the United States.