A supramolecular hydrogel leveraging hierarchical multi-strength hydrogen-bonds hinged strategy achieving a striking adhesive-mechanical balance.

To obtain high-performance tissue-adhesive hydrogel embodying excellent mechanical integrity, a supramolecular hydrogel patch is fabricated through in situ copolymerization of a liquid-liquid phase separation precursor composed of self-complementary 2-2-ureido-4-pyrimidone-based monomer and acrylic acid coupled with subsequent corporation of bioactive epigallocatechin gallate. Remarkably, the prepared supramolecular hydrogel leverages hierarchical multi-strength hydrogen-bonds hinged strategy assisted by alkyl-based hydrophobic pockets, broadening the distribution of binding strength of physical junctions, striking a canonical balance between superb mechanical performance and robust adhesive capacity. Ultimately, the fabricated supramolecular hydrogel patch stands out as a high stretchability (1500 %), an excellent tensile strength (2.6 MPa), a superhigh toughness (12.6 MJ m-3), an instant and robust tissue adhesion strength (263.2 kPa for porcine skin), the considerable endurance under cyclic loading and reversible adhesion, a superior burst pressure tolerance (108 kPa) to those of commercially-available tissue sealants, and outstanding anti-swelling behavior. The resultant supramolecular hydrogel patch demonstrates the rapid hemorrhage control within 60 s in liver injury and efficient wound closure and healing effects with alleviated inflammation and reduced scarring in full-thickness skin incision, confirming its medical translation as a promising self-rescue tissue-adhesive patch for hemorrhage prevention and sutureless wound closure.

Novel characterisation of sheep flocks in Wales: A description of principal flock typologies and antimicrobial use patterns.

There is increasing pressure to reduce and refine antimicrobial use (AMU) in farmed livestock, to slow the development of antimicrobial resistance (AMR) and preserve the efficacy of antimicrobials (AMs) in both humans and animals. Developing strategies to help drive the prudent use of AMs requires an understanding of the direct and indirect factors influencing the between-farm variation in AMU typically observed. Given limited evidence of risk factors in sheep, this exploratory study aimed to investigate whether sheep flocks could be classified into farm types based on farm characteristics, health parameters and management practices, and whether important differences existed in AMU between these flock types. This study was conducted on 22 sheep flocks in Wales, United Kingdom as part of a wider longitudinal study on AMU and AMR. Comprehensive surveys were administered to flocks where 147 variables regarding farm characteristics, flock health parameters and management practices were captured. AMU data for each flock were also collated. A Multiple Correspondence Analysis (MCA), followed by a Hierarchical Clustering on Principal Components (HCPC) analysis, were used to classify the flocks. The top 10 dimensions yielded by MCA explained 67.4 % of the total variance. Nine partitions of relatively homogeneous flocks, derived from three typologies produced from the first three cut-points of the HCPC dendrogram, were visualised and described. These nine partitions were characterised by 70 variable categories, principally the implementation or neglect of best-practice lameness management practices. Partitions of flocks that neglected best-practice lameness managements - characterised by delayed treatments of lame sheep, footbathing and bleeding when foot trimming - reported higher lameness prevalence and fewer lame ewes recovering within five days of treatment. These flocks had higher total AMU (mg/PCU) and used a higher mass of injectable AMs than other partitions of flocks. Flock traits, lambing practices, disease challenges and other management factors also described partitions derived in later dendrogram cuts. Findings from this study confirm good AM stewardship in sheep flocks to be a complex picture, given the typologies of flocks described and the range of factors likely to influence AMU. Opportunities for targeted strategies for sustainable reductions in AMU can be directed towards specific flock types identified, specifically within the context of lameness treatment and control. We highlight the importance of understanding flock heterogeneity, through the construction of typologies, as a means to fine-tune appropriate interventions to specific flock types in order to help drive more prudent use of AMs.

Spectroscopic techniques and chemometrics for ink analysis: Document analysis application.

This study represents a methodology to determine the falsified documents via a combined method of spectroscopic techniques and chemometrics. The techniques of UV-Vis and FTIR-ATR spectroscopies and principal component analysis methods have been applied to black, blue, and red ballpoint pens available in the Türkiye market. The analysis results of the applied spectroscopic techniques were found to be consistent with each other for the examined eleven ink samples from three different brands, and it was observed that FTIR-ATR spectroscopy had higher discriminative power. A dried sample preparation technique developed for small amounts of samples for FTIR-ATR measurements and the increased the sensitivity of FTIR-ATR technique was associated with this sample preparation method. Statistical techniques showed that the UV-Vis technique proves useful outcomes in distinguishing inks according to their colors, whereas the FTIR-ATR technique is valuable for identifying ink brands. Additional analysis of the sub-brand of black inks and falsified old-dated check sample analysis validated that our methodology has the potential to classify and identify ballpoint inks in forensic applications. Due to the limited sample quantity available for analysis in real samples, only FTIR-ATR analysis was possible, and the results demonstrated that document forgery could be detected from small parts of samples in the document. When we look at the overall outcome of this study, a comprehensive methodology enriched with statistical calculations has been proposed that can be applied even in a laboratory with limited resources, having only UV-Vis and FTIR-ATR capabilities.

Task-space tracking for networked heterogeneous robotic systems via adaptive neural fixed-time control.

The task-space distributed adaptive neural network (NN) fixed-time tracking problem is studied for networked heterogeneous robotic systems (NHRSs). In order to address this complex problem, we propose a NN-based fixed-time hierarchical control approach that transforms the problem into two sub-problems: a distributed fixed-time estimation problem and a local fixed-time tracking problem, respectively. Specifically, distributed estimators are constructed so that each follower can acquire the dynamic leader's state in a fixed time. Then, the neural networks (NNs) are employed to approximate the compounded uncertainty consisting of the unknown dynamics of robotic systems and the boundary of the compounded disturbance. More importantly, to guarantee that the tracking errors can converge into a small neighborhood of equilibrium in a fixed time independent of the initial state, the adaptive neural fixed-time local tracking controller is proposed. Another merit of the proposed controller is that the approximation errors are addressed in a novel way, eliminating the need for prior precise knowledge of uncertainties and improving the robustness and convergence speed of unknown robotic systems. Finally, the experimental results demonstrate the effectiveness and advantages of the proposed control method.

Thiol-Acrylate polyHIPEs via Facile Layer-by-Layer Photopolymerization.

A highly reactive thiol-ene high internal phase emulsion based on the monomers 1,6-hexanediol diacrylate and tris 2-(3-mercaptopropionyloxy)ethyl isocyanurate was developed for the purpose of light-driven additive manufacturing, resulting in highly porous customizable poly(high internal phase emulsion) materials. The formulation was specifically designed to facilitate short irradiation times and low amounts of photoinitiator. Furthermore, the developed emulsion does not rely on employing harmful solvents to make scale-up and industrial applications feasible. The selected thiol was added to the printing formulation as a chain-transfer agent, decreasing the brittleness of the acrylate-based system and potential of oxygen inhibition. The thickness of the printed layers lay <50 µm, and the average pore size of all samples was <5 µm.

Scalable Hierarchically Structured Materials from a Multiscale Particle System Enabled by Microscaffolds.

Structural hierarchy is the key to manufacturing multiscale particle-based composite materials. A novel manufacturing method was developed to generate scalable hierarchical structures in concrete. The new method used 3D-printed microscaffolds to interact with the multiscale particle packing in concrete, resulting in a structured lightweight composite material. The size of internal members can vary by more than two orders of magnitude, to adapt to different applications. Based on compression tests and microstructural investigation by optical microscope and quantitative nanomechanical mapping, we found that the new material is 63.93% more efficient in energy absorption capacity compared with traditional lightweight concrete. Our experimental trials also showed that introducing structural hierarchy can reduce the consumption of cementitious material in the system by up to 14% and significantly reduce the use of scaffolds. The method could be applied to a board spectrum of multiscale particle-based materials, such as dental cement and bone implant materials, to improve material performance and efficiency in medical and construction applications.

Neuroticism Is Prospectively Associated With 30-Month Changes in Broadband Internalizing Symptoms, but Not Narrowband Positive Affect or Anxious Arousal, in Emerging Adulthood.

Elevated levels of Neuroticism/Negative Emotionality (N/NE) and, less consistently, lower levels of Extraversion/Positive Emotionality (E/PE) confer risk for pathological depression and anxiety. To date, most prospective-longitudinal research has narrowly focused on traditional diagnostic categories, creating uncertainty about the precise nature of these prospective associations. Adopting an explicitly hierarchical-dimensional approach, we examined the association between baseline variation in personality and longitudinal changes in broad and narrow internalizing-symptom dimensions in 234 emerging adults followed for 2.5 years, during the transition from older adolescence to early adulthood. N/NE was uniquely associated with increases in broadband internalizing-the core cognitive and affective symptoms that cut across the emotional disorders-and unrelated to the narrower dimensions of positive affect and anxious arousal that differentiate specific internalizing presentations. Variation in E/PE and several other Big Five traits was cross-sectionally, but not prospectively, related to longitudinal changes in specific internalizing symptoms. Exploratory personality-facet-level analyses provided preliminary evidence of more granular associations between personality and longitudinal changes in internalizing symptoms. These observations enhance the precision of models linking personality to internalizing illness; highlight the centrality of N/NE to increases in transdiagnostic internalizing symptoms during a key developmental chapter; and set the stage for developing more effective prevention and treatment strategies.

ROMI: a randomized two-stage basket trial design to optimize doses for multiple indications.

Optimizing doses for multiple indications is challenging. The pooled approach of finding a single optimal biological dose (OBD) for all indications ignores that dose-response or dose-toxicity curves may differ between indications, resulting in varying OBDs. Conversely, indication-specific dose optimization often requires a large sample size. To address this challenge, we propose a Randomized two-stage basket trial design that Optimizes doses in Multiple Indications (ROMI). In stage 1, for each indication, response and toxicity are evaluated for a high dose, which may be a previously obtained maximum tolerated dose, with a rule that stops accrual to indications where the high dose is unsafe or ineffective. Indications not terminated proceed to stage 2, where patients are randomized between the high dose and a specified lower dose. A latent-cluster Bayesian hierarchical model is employed to borrow information between indications, while considering the potential heterogeneity of OBD across indications. Indication-specific utilities are used to quantify response-toxicity trade-offs. At the end of stage 2, for each indication with at least one acceptable dose, the dose with highest posterior mean utility is selected as optimal. Two versions of ROMI are presented, one using only stage 2 data for dose optimization and the other optimizing doses using data from both stages. Simulations show that both versions have desirable operating characteristics compared to designs that either ignore indications or optimize dose independently for each indication.

The statistical building blocks of animal movement simulations.

Animal movement plays a key role in many ecological processes and has a direct influence on an individual's fitness at several scales of analysis (i.e., next-step, subdiel, day-by-day, seasonal). This highlights the need to dissect movement behavior at different spatio-temporal scales and develop hierarchical movement tools for generating realistic tracks to supplement existing single-temporal-scale simulators. In reality, animal movement paths are a concatenation of fundamental movement elements (FuMEs: e.g., a step or wing flap), but these are not generally extractable from a relocation time-series track (e.g., sequential GPS fixes) from which step-length (SL, aka velocity) and turning-angle (TA) time series can be extracted. For short, fixed-length segments of track, we generate their SL and TA statistics (e.g., means, standard deviations, correlations) to obtain segment-specific vectors that can be cluster into different types. We use the centroids of these clusters to obtain a set of statistical movement elements (StaMEs; e.g.,directed fast movement versus random slow movement elements) that we use as a basis for analyzing and simulating movement tracks. Our novel concept is that sequences of StaMEs provide a basis for constructing and fitting step-selection kernels at the scale of fixed-length canonical activity modes: short fixed-length sequences of interpretable activity such as dithering, ambling, directed walking, or running. Beyond this, variable length pure or characteristic mixtures of CAMs can be interpreted as behavioral activity modes (BAMs), such as gathering resources (a sequence of dithering and walking StaMEs) or beelining (a sequence of fast directed-walk StaMEs interspersed with vigilance and navigation stops). Here we formulate a multi-modal, step-selection kernel simulation framework, and construct a 2-mode movement simulator (Numerus ANIMOVER_1), using Numerus RAMP technology. These RAMPs run as stand alone applications: they require no coding but only the input of selected parameter values. They can also be used in R programming environments as virtual R packages. We illustrate our methods for extracting StaMEs from both ANIMOVER_1 simulated data and empirical data from two barn owls (Tyto alba) in the Harod Valley, Israel. Overall, our new bottom-up approach to path segmentation allows us to both dissect real movement tracks and generate realistic synthetic ones, thereby providing a general tool for testing hypothesis in movement ecology and simulating animal movement in diverse contexts such as evaluating an individual's response to landscape changes, release of an individual into a novel environment, or identifying when individuals are sick or unusually stressed.

Denoising method for colonic pressure signals based on improved wavelet threshold.

The colonic peristaltic pressure signal is helpful for the diagnosis of intestinal diseases, but it is difficult to reflect the real situation of colonic peristalsis due to the interference of various factors. To solve this problem, an improved wavelet threshold denoising method based on discrete wavelet transform is proposed in this paper. This algorithm can effectively extract colonic peristaltic pressure signals and filter out noise. Firstly, a threshold function with three shape adjustment factors is constructed to give the function continuity and better flexibility. Then, a threshold calculation method based on different decomposition levels is designed. By adjusting the three preset shape factors, an appropriate threshold function is determined, and denoising of colonic pressure signals is achieved through hierarchical thresholding. In addition, the experimental analysis of bumps signal verifies that the proposed denoising method has good reliability and stability when dealing with non-stationary signals. Finally, the denoising performance of the proposed method was validated using colonic pressure signals. The experimental results indicate that, compared to other methods, this approach performs better in denoising and extracting colonic peristaltic pressure signals, aiding in further identification and treatment of colonic peristalsis disorders.

Enhancing Hierarchical Transformers for Whole Brain Segmentation with Intracranial Measurements Integration.

Whole brain segmentation with magnetic resonance imaging (MRI) enables the non-invasive measurement of brain regions, including total intracranial volume (TICV) and posterior fossa volume (PFV). Enhancing the existing whole brain segmentation methodology to incorporate intracranial measurements offers a heightened level of comprehensiveness in the analysis of brain structures. Despite its potential, the task of generalizing deep learning techniques for intracranial measurements faces data availability constraints due to limited manually annotated atlases encompassing whole brain and TICV/PFV labels. In this paper, we enhancing the hierarchical transformer UNesT for whole brain segmentation to achieve segmenting whole brain with 133 classes and TICV/PFV simultaneously. To address the problem of data scarcity, the model is first pretrained on 4859 T1-weighted (T1w) 3D volumes sourced from 8 different sites. These volumes are processed through a multi-atlas segmentation pipeline for label generation, while TICV/PFV labels are unavailable. Subsequently, the model is finetuned with 45 T1w 3D volumes from Open Access Series Imaging Studies (OASIS) where both 133 whole brain classes and TICV/PFV labels are available. We evaluate our method with Dice similarity coefficients(DSC). We show that our model is able to conduct precise TICV/PFV estimation while maintaining the 132 brain regions performance at a comparable level. Code and trained model are available at: https://github.com/MASILab/UNesT/wholebrainSeg.

Phosphorus vacancies regulation and heterogeneous interfacial engineering of coral-like ZnO/FeCoPv@N-doped carbon hierarchical microspheres to boost overall water splitting.

Vacancy engineering and heterostructure construction are regarded as potent approaches for synergistically boosting hydrogen production in renewable energy conversion. Herein, a selective phosphorization strategy was implemented to fabricate coral-like ZnO/FeCoP@N-doped carbon hierarchical microspheres (ZnO/FeCoP@NCHMS) via only controllably phosphorizing the Co and Fe atoms in a precursor, which was formed by generating ZnCoFe LDH on the surface of a zinc cobalt coordination polymer microsphere. Then, by adopting a reduction treatment for ZnO/FeCoP@NCHMS, the innovative ZnO/FeCoPv@NCHMS with abundant phosphorus vacancies (Pv) was realized. The introduction of phosphorus vacancy could optimize the electronic structures of metal phosphides and accelerate the reconstruction of active species, thus speeding up the reaction kinetic. Likewise, the plentiful heterointerfaces greatly expedite the transfer of electrons and protons, exposing ultra-high active sites. By virtue of these fascinating characters and the unique coral-like hierarchical architecture, the as-prepared ZnO/FeCoPv@NCHMS reveal preeminent electrocatalytic activities, and the overpotentials for the oxygen evolution reaction (OER) and the hydrogen evolution reaction (HER) are as low as 177 and 173 mV at 10 mA cm-2 in alkaline medium, respectively. Impressively, the water electrolysis device assembled by ZnO/FeCoPv@NCHMS requires a mere cell voltage of 1.508 V to attain a current density of 10 mA cm-2. Furthermore, the ZnO/FeCoPv@NCHMS also demonstrate extraordinary durability, sustaining operation for at least 28 h (at 100 mA cm-2) during the water splitting process. This study provides novel insights into defect regulation and heterointerface construction for overall water splitting.

Preparation of novel hierarchical catalysts by simultaneous generation of ß-zeolite and mesoporous silica for catalytic cracking.

The gel skeletal reinforcement (GSR) method was applied at the preparation stage of ß-zeolite to prepare a novel hierarchical catalyst. A solution of hexamethyldisiloxane (HMDS) and acetic anhydride, a GSR reagent, was added to the mixture of colloidal silica, sodium aluminate, tetraethylammonium hydroxide, sodium hydroxide and water, and successive aging and hydrothermal treatment gave microporous ß-zeolite surrounded by mesoporous silica like core-shell structure. Its properties were characterized by XRD, nitrogen adsorption and desorption, NH3-TPD, TEM, and TG-DTA measurements, and further characteristics of the catalysts produced were clarified by the catalytic cracking of n-dodecane. The hierarchical structure of microporous zeolite and mesoporous silica was shown from GSR-2.9HS-H-Beta to GSR-3.2HS-H-Beta, where the molar ratio of HMDS and silica source of ß-zeolite was 2.9~3.2:100. It was found that in the catalytic cracking of n-dodecane, the relative activity (the conversion per the amount of zeolite crystals) increased with the increase in mesopore volume and surface area. The result indicated that the introduction of mesopores was effective even in catalytic cracking of small molecule of n-dodecane.

Active crocodiles are less sociable.

How animals move and associate with conspecifics is rarely random, with a population's spatial structure forming the foundation on which the social behaviours of individuals form. Studies examining the spatial-social interface typically measure averaged behavioural differences between individuals; however, this neglects the inherent variation present within individuals and how it may impact the spatial-social interface. Here, we investigated differences in among-individual (co)variance in sociability, activity and site fidelity in a population of wild estuarine crocodiles, Crocodylus porosus, across a 10-year period. By monitoring 118 crocodiles using coded acoustic transmitters and an array of fixed underwater receivers, we discovered that not only did individual crocodiles repeatably differ (among-individual variation) in each behaviour measured but also in how consistently they expressed these behaviours through time (within-individual variation). As expected, crocodile activity and sociability formed a behavioural syndrome, with more active individuals being less sociable. Interestingly, we also found that individuals that were either more sociable or displayed greater site fidelity were also more specialized (lower within-individual variation) in these behaviours. Together, our results provide important empirical evidence for the interplay between spatial, temporal and social individual-level behavioural variation and how these contribute to forming behavioural niches. This article is part of the theme issue 'The spatial-social interface: a theoretical and empirical integration'.

Hierarchical nested honeycomb-based energy absorbers: design factors and tailorable mechanical properties.

This study presents a novel hierarchical nested honeycomb drawing inspiration from the hierarchical structures found in energy-absorbing citrus peels. Our investigation reveals that integrating secondary hierarchical units into primary honeycomb cells results in energy absorption profiles featuring two distinct plateaus. Notably, we found that these profiles can be finely tuned by adjusting the thickness of primary and secondary cell walls. Additionally, our study demonstrates a strategic removal of cell walls at key positions, reducing material consumption without compromising specific energy absorption. By establishing comprehensive structure-property relationships, we offer valuable insights into the design and optimization of hierarchical cellular materials. Compared with traditional honeycomb structures, the nested honeycomb structure shows a twofold increase in compressive strength and a fivefold increase in specific energy absorption, positioning them as promising candidates for applications requiring two-step impact protection and tunable performance, ranging from packaging to high-speed automobiles.

Development of a Novel Hierarchically Biofabricated Blood Vessel Mimic Decorated with Three Vascular Cell Populations for the Reconstruction of Small-Diameter Arteries.

The availability of grafts to replace small-diameter arteries remains an unmet clinical need. Here, the validated methodology is reported for a novel hybrid tissue-engineered vascular graft that aims to match the natural structure of small-size arteries. The blood vessel mimic (BVM) comprises an internal conduit of co-electrospun gelatin and polycaprolactone (PCL) nanofibers (corresponding to the tunica intima of an artery), reinforced by an additional layer of PCL aligned fibers (the internal elastic membrane). Endothelial cells are deposited onto the luminal surface using a rotative bioreactor. A bioprinting system extrudes two concentric cell-laden hydrogel layers containing respectively vascular smooth muscle cells and pericytes to create the tunica media and adventitia. The semi-automated cellularization process reduces the production and maturation time to 6 days. After the evaluation of mechanical properties, cellular viability, hemocompatibility, and suturability, the BVM is successfully implanted in the left pulmonary artery of swine. Here, the BVM showed good hemostatic properties, capability to withstand blood pressure, and patency at 5 weeks post-implantation. These promising data open a new avenue to developing an artery-like product for reconstructing small-diameter blood vessels.

Construction of amino acids reduced alphabets from molecular descriptors for interpretation of N-carbamylase, luciferase and PI3K mutations.

The classification of amino acids has proven to be a useful tool for understanding the importance of sequence in protein function. The reduced amino acid alphabets are an example of these classifications, which, when built from physicochemical, structural and quantum characteristics of the amino acids, allow it to simplify the representation of the sequences, being useful in the modelling, design and understanding of proteins. So, an objective selection of amino acids properties is important, due classes formed in a reduced alphabet depend on the descriptors used for classification. In this research, based on a careful selection of descriptors for the 20 amino acids, through techniques such as the information content index and hierarchical cluster analysis with ties in proximity, 20,871,586 reduced amino acid alphabets were constructed. This large collection of reduced alphabets was been used to interpret alterations in the function of three proteins: N-carbamylase, Luciferase, and PI3K, caused by amino acid changes in their sequences. For this, the similar and different descriptors linked to these mutations were studied. Properties such as volume, hydrophobicity, charge and autocorrelation can be associated with variations in the behaviour of these proteins, while the frequency in specific secondary structures, the Gibbs free energy and some topological and quantum properties can be considered as the causes of preventing the deactivation of protein function. This work offers the most complete collection of reduced alphabets that promise to be a useful tool for the interpretation of alterations caused by amino acid mutations in the protein sequence.

Hierarchically spherical assembly of carbon nanorods derived from metal-organic framework as solid-phase microextraction coating for nitrated polycyclic aromatic hydrocarbon analysis.

Nitrated polycyclic aromatic hydrocarbons (nitro-PAHs) are pervasive contaminants in aquatic environments. They are characterized by persistence, toxicity, bioaccumulation, and long-range transport, significantly threatening human health. The development of sensitive methods for nitro-PAH analysis in environmental samples is in great need. This study developed a novel carbonaceous SPME coating derived from metal-organic framework (MOF), namely a spherical assembly consisting of carbon nanorods with hierarchical porosity (HP-MOF-C), for the extraction and determination of nitro-PAHs in waters. The HP-MOF-C coated fiber demonstrated superior nitro-PAH extraction efficiencies, with enrichment factors 2∼70 times higher than commercial fibers. This enhancement was due to the strong hydrophobic, π-π electron coupling/stacking, and π-π electron donor-acceptor interactions between the carbonaceous framework of HP-MOF-C and the nitro-PAHs. Moreover, the unique hierarchical porous structure of HP-MOF-C accelerated the diffusion of nitro-PAHs, further facilitating their enrichment. The fiber also exhibited good thermal stability, remarkable chemical stabilities against common acid, base, and polar/non-polar solvents, and long service life (> 150 SPME cycles). The nitro-PAH determination method based on HP-MOF-C coating yielded wide linear ranges, low detection limits (0.4∼5.0 ng L-1), satisfactory repeatability and reproducibility, and good recoveries in real water samples. The proposed method was considered to be green according to the Analytical GREEnness assessment. The present study not only offers an efficient SPME coating for the enrichment of nitro-PAHs, but also provides insights into the design of porous coating materials.

In situ Construction of Hierarchical Ni-Co Hydroxides Derived from Metal-Organic Frameworks for High-Performance Ni-Zn Batteries.

Rechargeable nickel-zinc (Ni-Zn) batteries hold great promise for large-scale applications due to their relatively high voltage, cost-efficient zinc anode, and good safety. However, the commonly used cathode materials are susceptible to overcharging and experience irreversible capacity degradation, primarily as a result of low electrical conductivity and substantial limitations in volume-constrained proton diffusion. Here, we present a robust methodology for synthesizing hierarchical nickel-cobalt (Ni-Co) hydroxides characterized by hollow interiors and interconnected nanosheet shells with the help of in situ formed metal-organic frameworks (MOFs). The templating effect of the MOF induced the hierarchical structure, while the chemical etching of MOFs by Ni2+ ions resulted in a hollow structure, thereby enhancing the surface area. Theoretical calculations suggested that incorporation of cobalt reduces the band gap, thereby improving electronic conductivity, and lowered the deprotonation energy, which mitigated overcharge issues. These advantages conferred improved specific capacity, rate capability, and cyclic stability to the Ni-Co hydroxide. The Ni-Zn cell delivered specific energy values of 338 Wh kg-1 at 1.62 kW kg-1 and 142 Wh kg-1 at 29.89 kW kg-1. Our investigations undercoreed the critical role of MOFs as intermediates in the preparation of multi-component hydroxide and the construction of hiearchical structures to achieve superior performance.

The impact of accounting conservatism on enterprise innovation investment.

This study investigates the influence of accounting conservatism on corporate innovation investment through the lens of information asymmetry theory. While existing literature acknowledges the importance of accounting conservatism in corporate decision-making, there remains a gap in understanding how it specifically affects innovation investment, particularly in varied market environments and regulatory contexts. Specifically, current research often overlooks the heterogeneity of the impact of accounting conservatism on innovation investment under different market environments and regulatory frameworks. Additionally, there is a lack of specialized studies on the unique group of Chinese listed companies. This study fills this gap by empirically analyzing data from Chinese A-share listed companies, revealing a negative correlation between accounting conservatism and corporate innovation investment. Through empirical analysis of the financial reports and research and development (R&D) investment data of Chinese A-share listed companies from 2015 to 2022, this study finds a significant negative correlation between accounting conservatism and corporate innovation investment. Specifically, as accounting conservatism increases, corporate investment in R&D shows a decreasing trend, with a correlation coefficient of -0.364. This result is further validated by hierarchical regression analysis, where the regression coefficient is -0.465, indicating that accounting conservatism has a significant inhibitory effect on corporate innovation investment. This study is pioneering in its examination of the relationship between accounting conservatism and corporate innovation investment within the unique market environment of China, taking into account its distinctive characteristics and rapidly evolving technological industry background. To quantify accounting conservatism, the research employs the C-Score and G-Score models, while employing a range of indicators to measure corporate innovation investment, including proportions of R&D expenditure, number of new products or services, patent applications, total R&D personnel, capital investments, and progress in innovation projects. This comprehensive evaluation method enhances the accuracy and reliability of the study. The contribution of this study is significant as it offers a fresh perspective on how accounting conservatism influences corporate innovation investment. By providing empirical data support, it assists investors and corporate managers in making informed financial decisions and shaping innovation strategies. Through hierarchical regression analysis, the study substantiates the detrimental impact of accounting conservatism on corporate innovation investment, thereby establishing new theoretical and practical foundations for further research and application in related fields.