Disrupted cerebellar structural connectome in spinocerebellar ataxia type 3 and its association with transcriptional profiles.

Spinocerebellar ataxia type 3 (SCA3) is primarily characterized by progressive cerebellar degeneration, including gray matter atrophy and disrupted anatomical and functional connectivity. The alterations of cerebellar white matter structural network in SCA3 and the underlying neurobiological mechanism remain unknown. Using a cohort of 20 patients with SCA3 and 20 healthy controls, we constructed cerebellar structural networks from diffusion MRI and investigated alterations of topological organization. Then, we mapped the alterations with transcriptome data from the Allen Human Brain Atlas to identify possible biological mechanisms for regional selective vulnerability to white matter damage. Compared with healthy controls, SCA3 patients exhibited reduced global and nodal efficiency, along with a widespread decrease in edge strength, particularly affecting edges connected to hub regions. The strength of inter-module connections was lower in SCA3 group and negatively correlated with the Scale for the Assessment and Rating of Ataxia score, International Cooperative Ataxia Rating Scale score, and cytosine-adenine-guanine repeat number. Moreover, the transcriptome-connectome association study identified the expression of genes involved in synapse-related and metabolic biological processes. These findings suggest a mechanism of white matter vulnerability and a potential image biomarker for the disease severity, providing insights into neurodegeneration and pathogenesis in this disease.

Decreased brain iron deposition based on quantitative susceptibility mapping correlates with reduced neurodevelopmental status in children with autism spectrum disorder.

To investigate potential correlations between the susceptibility values of certain brain regions and the severity of disease or neurodevelopmental status in children with autism spectrum disorder (ASD), 18 ASD children and 15 healthy controls (HCs) were recruited. The neurodevelopmental status was assessed by the Gesell Developmental Schedules (GDS) and the severity of the disease was evaluated by the Autism Behavior Checklist (ABC). Eleven brain regions were selected as regions of interest and the susceptibility values were measured by quantitative susceptibility mapping. To evaluate the diagnostic capacity of susceptibility values in distinguishing ASD and HC, the receiver operating characteristic (ROC) curve was computed. Pearson and Spearman partial correlation analysis were used to depict the correlations between the susceptibility values, the ABC scores, and the GDS scores in the ASD group. ROC curves showed that the susceptibility values of the left and right frontal white matter had a larger area under the curve in the ASD group. The susceptibility value of the right globus pallidus was positively correlated with the GDS-fine motor scale score. These findings indicated that the susceptibility value of the right globus pallidus might be a viable imaging biomarker for evaluating the neurodevelopmental status of ASD children.

Associations of quantitative susceptibility mapping with cortical atrophy and brain connectome in Alzheimer's disease: A multi-parametric study.

Aberrant susceptibility due to iron level abnormality and brain network disconnections are observed in Alzheimer's disease (AD), with disrupted iron homeostasis hypothesized to be linked to AD pathology and neuronal loss. However, whether associations exist between abnormal quantitative susceptibility mapping (QSM), brain atrophy, and altered brain connectome in AD remains unclear. Based on multi-parametric brain imaging data from 30 AD patients and 26 healthy controls enrolled at the China-Japan Friendship Hospital, we investigated the abnormality of the QSM signal and volumetric measure across 246 brain regions in AD patients. The structural and functional connectomes were constructed based on diffusion MRI tractography and functional connectivity, respectively. The network topology was quantified using graph theory analyses. We identified seven brain regions with both reduced cortical thickness and abnormal QSM (p < 0.05) in AD, including the right superior frontal gyrus, left superior temporal gyrus, right fusiform gyrus, left superior parietal lobule, right superior parietal lobule, left inferior parietal lobule, and left precuneus. Correlations between cortical thickness and network topology computed across patients in the AD group resulted in statistically significant correlations in five of these regions, with higher correlations in functional compared to structural topology. We computed the correlation between network topological metrics, QSM value and cortical thickness across regions at both individual and group-averaged levels, resulting in a measure we call spatial correlations. We found a decrease in the spatial correlation of QSM and the global efficiency of the structural network in AD patients at the individual level. These findings may provide insights into the complex relationships among QSM, brain atrophy, and brain connectome in AD.

Relationship between topological efficiency of white matter structural connectome and plasma biomarkers across the Alzheimer's disease continuum.

Both plasma biomarkers and brain network topology have shown great potential in the early diagnosis of Alzheimer's disease (AD). However, the specific associations between plasma AD biomarkers, structural network topology, and cognition across the AD continuum have yet to be fully elucidated. This retrospective study evaluated participants from the Sino Longitudinal Study of Cognitive Decline cohort between September 2009 and October 2022 with available blood samples or 3.0-T MRI brain scans. Plasma biomarker levels were measured using the Single Molecule Array platform, including ß-amyloid (Aß), phosphorylated tau181 (p-tau181), glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL). The topological structure of brain white matter was assessed using network efficiency. Trend analyses were carried out to evaluate the alterations of the plasma markers and network efficiency with AD progression. Correlation and mediation analyses were conducted to further explore the relationships among plasma markers, network efficiency, and cognitive performance across the AD continuum. Among the plasma markers, GFAP emerged as the most sensitive marker (linear trend: t = 11.164, p = 3.59 × 10-24 ; quadratic trend: t = 7.708, p = 2.25 × 10-13 ; adjusted R2 = 0.475), followed by NfL (linear trend: t = 6.542, p = 2.9 × 10-10 ; quadratic trend: t = 3.896, p = 1.22 × 10-4 ; adjusted R2 = 0.330), p-tau181 (linear trend: t = 8.452, p = 1.61 × 10-15 ; quadratic trend: t = 6.316, p = 1.05 × 10-9 ; adjusted R2 = 0.346) and Aß42/Aß40 (linear trend: t = -3.257, p = 1.27 × 10-3 ; quadratic trend: t = -1.662, p = 9.76 × 10-2 ; adjusted R2 = 0.101). Local efficiency decreased in brain regions across the frontal and temporal cortex and striatum. The principal component of local efficiency within these regions was correlated with GFAP (Pearson's R = -0.61, p = 6.3 × 10-7 ), NfL (R = -0.57, p = 6.4 × 10-6 ), and p-tau181 (R = -0.48, p = 2.0 × 10-4 ). Moreover, network efficiency mediated the relationship between general cognition and GFAP (ab = -0.224, 95% confidence interval [CI] = [-0.417 to -0.029], p = .0196 for MMSE; ab = -0.198, 95% CI = [-0.42 to -0.003], p = .0438 for MOCA) or NfL (ab = -0.224, 95% CI = [-0.417 to -0.029], p = .0196 for MMSE; ab = -0.198, 95% CI = [-0.42 to -0.003], p = .0438 for MOCA). Our findings suggest that network efficiency mediates the association between plasma biomarkers, specifically GFAP and NfL, and cognitive performance in the context of AD progression, thus highlighting the potential utility of network-plasma approaches for early detection, monitoring, and intervention strategies in the management of AD.

DCP: A pipeline toolbox for diffusion connectome.

The brain structural network derived from diffusion magnetic resonance imaging (dMRI) reflects the white matter connections between brain regions, which can quantitatively describe the anatomical connection pattern of the entire brain. The development of structural brain connectome leads to the emergence of a large number of dMRI processing packages and network analysis toolboxes. However, the fully automated network analysis based on dMRI data remains challenging. In this study, we developed a cross-platform MATLAB toolbox named "Diffusion Connectome Pipeline" (DCP) for automatically constructing brain structural networks and calculating topological attributes of the networks. The toolbox integrates a few developed packages, including FSL, Diffusion Toolkit, SPM, Camino, MRtrix3, and MRIcron. It can process raw dMRI data collected from any number of participants, and it is also compatible with preprocessed files from public datasets such as HCP and UK Biobank. Moreover, a friendly graphical user interface allows users to configure their processing pipeline without any programming. To prove the capacity and validity of the DCP, two tests were conducted with using DCP. The results showed that DCP can reproduce the findings in our previous studies. However, there are some limitations of DCP, such as relying on MATLAB and being unable to fixel-based metrics weighted network. Despite these limitations, overall, the DCP software provides a standardized, fully automated computational workflow for white matter network construction and analysis, which is beneficial for advancing future human brain connectomics application research.

High-Valence Metal Doping Induced Lattice Expansion for M-FeNi LDH toward Enhanced Urea Oxidation Electrocatalytic Activities.

The precise design of low-cost, efficient, and definite electrocatalysts is the key to sustainable renewable energy. The urea oxidation reaction (UOR) offers a promising alternative to the oxygen evolution reaction for energy-saving hydrogen generation. In this study, by tuning the lattice expansion, a series of M-FeNi layered double hydroxides (M-FeNi LDHs, M: Mo, Mn, V) with excellent UOR performance are synthesized. The hydrolytic transformation of Fe-MIL-88A is assisted by urea, Ni2+ and high-valence metals, to form a hollow M-FeNi LDH. Owing to the large atomic radius of the high-valence metal, lattice expansion is induced, and the electronic structure of the FeNi-LDH is regulated. Doping with high-valence metal is more favorable for the formation of the high-valence active species, NiOOH, for the UOR. Moreover, the hollow spindle structure promoted mass transport. Thus, the optimal Mo-FeNi LDH showed outstanding UOR electrocatalytic activity, with 1.32 V at 10 mA cm-2 . Remarkably, the Pt/C||Mo-FeNi LDH catalyst required a cell voltage of 1.38 V at 10 mA·cm-2 in urea-assisted water electrolysis. This study suggests a new direction for constructing nanostructures and modulating electronic structures, which is expected to ultimately lead to the development of a class of auxiliary electrocatalysts.

Modulation of p-Block Electron Orbits in Metal-Organic Frameworks for CO2 Capture and Electrochemical Reduction.

Developing catalysts with excellent CO2 capture capability and electrochemical CO2 reduction reaction (CO2RR) at a wide potential range simultaneously is significant but remains a formidable challenge. Here, two novel InMg defective trinuclear cluster-based MOFs (SNNU-41 and SNNU-42) with abundant p-block unsaturated coordinated sites were reported and exhibited good CO2 capture and CO2RR performance simultaneously. Due to the suitable micropores, SNNU-41 showed higher CO2 capture ability at different adsorption pressure conditions. On account of the rigid framework and the closer p band center to Fermi level, SNNU-42 accelerated the conversion of CO2 molecule to C1 efficiency. Notably, via adjusting the ratio of p-block metal (In) in the SNNU-42 framework, the performance of the CO2RR was promoted drastically. SNNU-42 with the InMg (1:1.8) mixed cluster delivered an excellent Faradaic efficiency of 91.3% for C1 products and high selectivity of 72.0% for HCOOH at -2.5 V (vs Ag/Ag+) with a total current density of 77.2 mA cm-2. This work provides a possibility for efficient CO2 capture and CO2RR electrocatalysts through the modulation of electronic structures and composition in MOFs.

Artificial Polymerizations in Living Organisms for Biomedical Applications.

Within living organisms, numerous nanomachines are constantly involved in complex polymerization processes, generating a diverse array of biomacromolecules for maintaining biological activities. Transporting artificial polymerizations from lab settings into biological contexts has expanded opportunities for understanding and managing biological events, creating novel cellular compartments, and introducing new functionalities. This review summarizes the recent advancements in artificial polymerizations, including those responding to external stimuli, internal environmental factors, and those that polymerize spontaneously. More importantly, the cutting-edge biomedical application scenarios of artificial polymerization, notably in safeguarding cells, modulating biological events, improving diagnostic performance, and facilitating therapeutic efficacy are highlighted. Finally, this review outlines the key challenges and technological obstacles that remain for polymerizations in biological organisms, as well as offers insights into potential directions for advancing their practical applications and clinical trials.

Activable Nano-Immunomodulator Assembled from π-Extended Naphthalenediimide for Precision Photothermal Immunotherapy.

A nano-immunomodulator (R-NPT NP) comprising a tumor microenvironment (TME) activable resiquimod (R848) and a π-extended NIR-absorbing naphthophenanthrolinetetraone (NPT) has been engineered for spatiotemporal controlled photothermal immunotherapy. R-NPT NP demonstrated excellent photostability, while R848 promoted synergistic immunity as a toll-like receptor 7/8 (TLR7/8) agonist. Upon accumulation at the tumor site, R-NPT NP released R848 in response to redox metabolite glutathione (GSH), triggering dendritic cell (DC) activation. The photothermal effect endowed by R-NPT NP can ablate tumors directly and trigger immunogenic cell death to augment immunity after photoirradiation. The synergistic effect of GSH-liable TLR7/8 agonist and released immunogenic factors leads to a robust evocation of systematic immunity through promoted DC maturation and T cell infiltration. Thus, R-NPT NP with photoirradiation achieved 99.3 % and 98.2 % growth inhibition against primary and distal tumors, respectively.

Evaluation of whole-brain oxygen metabolism in Alzheimer's disease using QSM and quantitative BOLD.

OBJECTIVE: The objective of this study was to evaluate the whole-brain pattern of oxygen extraction fraction (OEF), cerebral blood flow (CBF), and cerebral metabolic rate of oxygen consumption (CMRO2) perturbation in Alzheimer's disease (AD) and investigate the relationship between regional cerebral oxygen metabolism and global cognition. METHODS: Twenty-six AD patients and 25 age-matched healthy controls (HC) were prospectively recruited in this study. Mini-Mental State Examination (MMSE) was used to evaluate cognitive status. We applied the QQ-CCTV algorithm which combines quantitative susceptibility mapping and quantitative blood oxygen level-dependent models (QQ) for OEF calculation. CBF map was computed from arterial spin labeling and CMRO2 was generated based on Fick's principle. Whole-brain and regional OEF, CBF, and CMRO2 analyses were performed. The associations between these measures in substructures of deep brain gray matter and MMSE scores were assessed. RESULTS: Whole brain voxel-wise analysis showed that CBF and CMRO2 values significantly decreased in AD predominantly in the bilateral angular gyrus, precuneus gyrus and parieto-temporal regions. Regional analysis showed that CBF value decreased in the bilateral caudal hippocampus and left rostral hippocampus and CMRO2 value decreased in left caudal and rostral hippocampus in AD patients. Considering all subjects in the AD and HC groups combined, the mean CBF and CMRO2 values in the bilateral hippocampus positively correlated with the MMSE score. CONCLUSION: CMRO2 mapping with the QQ-CCTV method - which is readily available in MR systems for clinical practice - can be a potential biomarker for AD. In addition, CMRO2 in the hippocampus may be a useful tool for monitoring cognitive impairment.

Au Nanowires Decorated Ultrathin Co3 O4 Nanosheets toward Light-Enhanced Nitrate Electroreduction.

Nitrate is a reasonable alternative instead of nitrogen for ammonia production due to the low bond energy, large water-solubility, and high chemical polarity for good absorption. Nitrate electroreduction reaction (NO3 RR) is an effective and green strategy for both nitrate treatment and ammonia production. As an electrochemical reaction, the NO3 RR requires an efficient electrocatalyst for achieving high activity and selectivity. Inspired by the enhancement effect of heterostructure on electrocatalysis, Au nanowires decorated ultrathin Co3 O4 nanosheets (Co3 O4 -NS/Au-NWs) nanohybrids are proposed for improving the efficiency of nitrate-to-ammonia electroreduction. Theoretical calculation reveals that Au heteroatoms can effectively adjust the electron structure of Co active centers and reduce the energy barrier of the determining step (*NO → *NOH) during NO3 RR. As the result, the Co3 O4 -NS/Au-NWs nanohybrids achieve an outstanding catalytic performance with high yield rate (2.661 mg h-1 mgcat -1 ) toward nitrate-to-ammonia. Importantly, the Co3 O4 -NS/Au-NWs nanohybrids show an obviously plasmon-promoted activity for NO3 RR due to the localized surface plasmon resonance (LSPR) property of Au-NWs, which can achieve an enhanced NH3 yield rate of 4.045 mg h-1 mgcat -1 . This study reveals the structure-activity relationship of heterostructure and LSPR-promotion effect toward NO3 RR, which provide an efficient nitrate-to-ammonia reduction with high efficiency.

Monodispersed Pt Sites Supported on NiFe-LDH from Synchronous Anchoring and Reduction for High Efficiency Overall Water Splitting.

Precise design of low-cost, efficient and definite electrocatalysts is the key to sustainable renewable energy. Herein, this work develops a targeted-anchored and subsequent spontaneous-redox strategy to synthesize nickel-iron layered double hydroxide (LDH) nanosheets anchored with monodispersed platinum (Pt) sites (Pt@LDH). Intermediate metal-organic frameworks (MOF)/LDH heterostructure not only provides numerous confine points to guarantee the stability of Pt sites, but also excites the spontaneous reduction for PtII . Electronic structure, charge transfer ability and reaction kinetics of Pt@LDH can be effectively facilitated by the monodispersed Pt moieties. As a result, the optimized Pt@LDH that with the 5% ultra-low content Pt exhibits the significant increment in electrochemical water splitting performance in alkaline media, which only afford low overpotentials of 58 mV at 10 mA cm-2 for hydrogen evolution reaction (HER) and 239 mV at 10 mA cm-2 for oxygen evolution reaction (OER), respectively. In a real device, Pt@LDH can drive an overall water-splitting at low cell voltage of 1.49 V at 10 mA cm-2 , which can be superior to most reported similar LDH-based catalysts. Moreover, the versatility of the method is extended to other MOF precursors and noble metals for the design of ultrathin LDH supported monodispersed noble metal electrocatalysts promoting research interest in material design.

Substituent Engineering in Pore-Space-Partitioned Metal-Organic Frameworks for CO2 Selective Adsorption and Fixation.

Comprehensive understanding of substituent groups located on the pore surface of metal-organic frameworks (which we call substituent engineering herein) can help to promote gas adsorption and catalytic performance through ligand functionalization. In this work, pore-space-partitioned metal-organic frameworks (PSP MOFs) were selected as a platform to evaluate the effect of organic functional groups on CO2 adsorption, separation, and catalytic conversion. Twelve partitioned acs metal-organic frameworks (pacs-MOFs, named SNNU-25-Rn here) containing different functional groups were synthesized, which can be classified into electron-donor groups (-OH, -NH2, -CH3, and -OCH3) and electron-acceptor groups (-NO2, -F, -Cl, and -Br). The experimental results showed that SNNU-25-Rn with electron donors usually perform better than those with electron acceptors for the comprehensive utilization of CO2. The CO2 uptake of the 12 SNNU-25-Rn MOFs ranged from 30.9 to 183.6 cm3 g-1 at 273 K and 1 bar, depending on the organic functional groups. In particular, SNNU-25-OH showed the highest CO2 adsorption, SNNU-25-CH3 had the highest IAST of CO2/CH4 (36.1), and SNNU-25-(OH)2 showed the best catalytic activity for the CO2 cycloaddition reaction. The -OH functionalized MOFs with excellent performance may be attributed to the Lewis acid-base and hydrogen-bonding interactions between -OH groups and the CO2 molecules. This work modulated the effect of the microenvironment of MOFs on CO2 adsorption, separation, and catalysis in terms of substituents, providing valuable information for the precise design of porous MOFs with a comprehensive utilization of CO2.

Aurantio-obtusin ameliorates obesity by activating PPARα-dependent mitochondrial thermogenesis in brown adipose tissues.

Obesity contributes to the progression of various chronic diseases, and shortens life expectancy. With abundant mitochondria, brown adipose tissue (BAT) dissipates energy through heat to limit weight gain and metabolic dysfunction in obesity. Our previous studies have shown that aurantio-obtusin (AO), a bioactive ingredient in Chinese traditional medicine Cassiae semen significantly improves hepatic lipid metabolism in a steatotic mouse model. In the current study we investigated the effects of AO on lipid metabolism in the BAT of diet-induced obesity mice and in oleic acid and palmitic acid (OAPA)-stimulated primary mature BAT adipocytes. Obese mice were established by feeding a HFHS diet for 4 weeks, and then administered AO (10 mg/kg, i.g.) for another 4 weeks. We showed that AO administration significantly increased the weight of BAT and accelerated energy expenditure to protect the weight increase in the obese mice. Using RNA sequencing and molecular biology analysis we found that AO significantly enhanced mitochondrial metabolism and UCP1 expression by activating PPARα both in vivo and in vitro in the primary BAT adipocytes. Interestingly, AO administration did not improve metabolic dysfunction in the liver and white adipose tissue of obese mice after interscapular BAT excision. We demonstrated that low temperature, a trigger of BAT thermogenesis, was not a decisive factor for AO to stimulate the growth and activation of BATs. This study uncovers a regulatory network of AO in activating BAT-dependent lipid consumption and brings up a new avenue for the pharmaceutical intervention in obesity and related comorbidities.

Neurobiological Mechanisms of Cognitive Decline Correlated with Brain Aging.

Cognitive decline has emerged as one of the greatest health threats of old age. Meanwhile, aging is the primary risk factor for Alzheimer's disease (AD) and other prevalent neurodegenerative disorders. Developing therapeutic interventions for such conditions demands a greater understanding of the processes underlying normal and pathological brain aging. Despite playing an important role in the pathogenesis and incidence of disease, brain aging has not been well understood at a molecular level. Recent advances in the biology of aging in model organisms, together with molecular- and systems-level studies of the brain, are beginning to shed light on these mechanisms and their potential roles in cognitive decline. This chapter seeks to integrate the knowledge about the neurological mechanisms of age-related cognitive changes that underlie aging.

The overlapping modular organization of human brain functional networks across the adult lifespan.

Previous studies have demonstrated that the brain functional modular organization, which is a fundamental feature of the human brain, would change along the adult lifespan. However, these studies assumed that each brain region belonged to a single functional module, although there has been convergent evidence supporting the existence of overlap among functional modules in the human brain. To reveal how age affects the overlapping functional modular organization, this study applied an overlapping module detection algorithm that requires no prior knowledge to the resting-state fMRI data of a healthy cohort (N = 570) aged from 18 to 88 years old. A series of measures were derived to delineate the characteristics of the overlapping modular structure and the set of overlapping nodes (brain regions participating in two or more modules) identified from each participant. Age-related regression analyses on these measures found linearly decreasing trends in the overlapping modularity and the modular similarity. The number of overlapping nodes was found increasing with age, but the increment was not even over the brain. In addition, across the adult lifespan and within each age group, the nodal overlapping probability consistently had positive correlations with both functional gradient and flexibility. Further, by correlation and mediation analyses, we showed that the influence of age on memory-related cognitive performance might be explained by the change in the overlapping functional modular organization. Together, our results revealed age-related decreased segregation from the brain functional overlapping modular organization perspective, which could provide new insight into the adult lifespan changes in brain function and the influence of such changes on cognitive performance.

Methodological evaluation of individual cognitive prediction based on the brain white matter structural connectome.

An emerging trend is to use regression-based machine learning approaches to predict cognitive functions at the individual level from neuroimaging data. However, individual prediction models are inherently influenced by the vast options for network construction and model selection in machine learning pipelines. In particular, the brain white matter (WM) structural connectome lacks a systematic evaluation of the effects of different options in the pipeline on predictive performance. Here, we focused on the methodological evaluation of brain structural connectome-based predictions. For network construction, we considered two parcellation schemes for defining nodes and seven strategies for defining edges. For the regression algorithms, we used eight regression models. Four cognitive domains and brain age were targeted as predictive tasks based on two independent datasets (Beijing Aging Brain Rejuvenation Initiative [BABRI]: 633 healthy older adults; Human Connectome Projects in Aging [HCP-A]: 560 healthy older adults). Based on the results, the WM structural connectome provided a satisfying predictive ability for individual age and cognitive functions, especially for executive function and attention. Second, different parcellation schemes induce a significant difference in predictive performance. Third, prediction results from different data sets showed that dMRI with distinct acquisition parameters may plausibly result in a preference for proper fiber reconstruction algorithms and different weighting options. Finally, deep learning and Elastic-Net models are more accurate and robust in connectome-based predictions. Together, significant effects of different options in WM network construction and regression algorithms on the predictive performances are identified in this study, which may provide important references and guidelines to select suitable options for future studies in this field.

Iron-Doped Cobalt Phosphide Nanohoops for Electroreduction of Nitrate to Ammonia.

Heteroatom doping can effectively tune the electronic structure of an electrocatalyst to accelerate the adsorption/desorption of reaction intermediates, which sharply increases their intrinsic electroactivity. Herein, we successfully prepare iron (Fe)-doped cobalt phosphide (CoP) nanohoops (Fe/CoP NHs) with different Fe/Co atomic ratios as highly active electrocatalysts for the nitrate electrocatalytic reduction reaction (NIT-ERR). Electrochemical measurements reveal that appropriate Fe doping can improve the electroactivity of cobalt phosphide nanohoops for the NIT-ERR. In a 1 M KOH electrolyte, the Fe/CoP NHs with the optimized chemical composition can achieve an efficient ammonia (NH3) generation rate of 27.6 mg h-1 mgcat-1 for the conversion of NO3- into NH3 and a Faradaic efficiency of 93.3% at a -0.25 V potential, which exceed the values of various previously reported nanomaterials in an alkaline electrolyte.

Oxygen-Vacancy-Rich Cu2O Hollow Nanocubes for Nitrate Electroreduction Reaction to Ammonia in a Neutral Electrolyte.

The electrocatalytic nitrate reduction reaction (NO3--ERR) to ammonia (NH3) is a promising strategy for NH3 production. Cu-based nanomaterials have been regarded as a kind of effective NO3--ERR catalysts. In this work, high-quality hollow Cu2O nanocubes (Cu2O h-NCs) are facilely synthesized by a simple one-step reduction method. The as-prepared Cu2O h-NCs reveal high selectivity and activity for NO3--ERR, which is ascribed to abundant oxygen vacancies, high surface area, hollow architecture, low mass transfer resistance, and strong adsorbing ability toward NO3-. In fact, Cu2O h-NCs can achieve a Faradic efficiency of 92.9% and an NH3 yield of 56.2 mg h-1 mgcat-1 for NH3 production at -0.85 V (vs RHE) potential, which exceeds those of other transition-metal-based NO3--ERR electrocatalysts.

Micropore Regulation in Ultrastable [Sc3O]-Organic Frameworks for Acetylene Storage and Purification.

High storage capacity, high separation selectivity, and high structure stability are essential for an idea gas adsorbent. However, it is not easy to achieve all three at the same time, even for the promising metal-organic framework (MOF) adsorbents. We demonstrate herein that robust [Sc3O]-organic frameworks could be regulated by a micropore combination strategy for high-performance acetylene adsorption. Under the same solvent system with formic acid as a modulator, similar tritopic ligands extend [Sc3O(COO)6] trigonal-prismatic clusters to generate SNNU-5-Sc and SNNU-150-Sc adsorbents. Notably, the two Sc-MOFs can keep their architectures over 24 h in water at different pH values (2-12) or at 90 °C. Modulated by the linker symmetry, the final stacking metal-organic polyhedral cages produce open window sizes of about 10 Å for SNNU-5-Sc and 5 Å + 7 Å for SNNU-150-Sc. Due to such micropore combinations, SNNU-5-Sc exhibits a top-level C2H2 uptake of 211.2 cm3 g-1 (1 atm and 273 K) and SNNU-150-Sc shows high C2H2/CH4, C2H2/C2H4, and C2H2/CO2 selectivities of 80.65, 4.03, and 8.19, respectively, under ambient conditions. Dynamic breakthrough curves obtained on a fixed-bed column and grand canonical Monte Carlo (GCMC) simulations further support their prominent acetylene storage and purification performance. High framework stability, storage capacity, and separation selectivity make SNNU-5-Sc and SNNU-150-Sc ideal acetylene adsorbents in practical applications.