Contralateral acupuncture for migraine without aura: a randomized trial protocol with multimodal MRI.

Introduction: Migraine is a common clinical disorder, ranks as the second most disabling disease worldwide, and often manifests with unilateral onset. Contralateral acupuncture (CAT), as a classical acupuncture method, has been proven to be effective in the treatment of migraine without aura (MWoA). However, its neural mechanisms have not been investigated using multimodal magnetic resonance imaging (MRI). Methods and analysis: In this multimodal neuroimaging randomized trial, a total of 96 female MWoA participants and 30 female healthy controls (HCs) will be recruited. The 96 female MWoA participants will be randomized into three groups: Group A (CAT group), Group B [ipsilateral acupuncture (IAT) group], and Group C (sham CAT group) in a 1:1:1 allocation ratio. Each group will receive 30 min of treatment every other day, three times a week, for 8 weeks, followed by an 8-week follow-up period. The primary outcome is the intensity of the migraine attack. Data will be collected at baseline (week 0), at the end of the 8-week treatment period (weeks 1-8), and during the 8-week follow-up (weeks 9-16). Adverse events will be recorded. Multimodal MRI scans will be conducted at baseline and after 8-week treatment. Discussion: This study hypothesized that CAT may treat MWoA by restoring pathological alterations in brain neural activity, particularly by restoring cross-integrated functional connectivity with periaqueductal gray (PAG) as the core pathological brain region. The findings will provide scientific evidence for CAT in the treatment of MWoA. Ethics and dissemination: The Medical Ethics Committee of the Second Affiliated Hospital of Yunnan University of Chinese Medicine has given study approval (approval no. 2022-006). This trial has been registered with the Chinese Clinical Trials Registry (registration no. ChiCTR2300069456). Peer-reviewed papers will be used to publicize the trial's findings. Clinical trial registration: https://clinicaltrials.gov/, identifier ChiCTR2300069456.

Low-cost eggshell-fly ash adsorbent for phosphate recovery: A potential slow-release phosphate fertilizer.

Fly ash (FA) and eggshells (ES) are common solid wastes with significant potential for the recovery of phosphorus from water. This study focuses on synthesizing a low-cost and environmental-friendly phosphate adsorbent called eggshell-fly ash geopolymer composite (EFG) using eggshells instead of chemicals. The CaO obtained from the high-temperature pyrolysis of eggshells provides active sites for phosphate adsorption, and CO2 serves as a pore-forming agent. The phosphate adsorption performance of EFG varied with the eggshell-fly ash ratios and achieved a maximum of 49.92 mg P/g at an eggshell-fly ash ratio of 40 %. The adsorption process was well described by the pseudo-second-order model and the Langmuir model. EFG also exhibited a good regeneration performance through six-cycle experiments and achieved the highest phosphate desorption at pH 4.0. The results of the column experiment showed that EFG can be used as a filter media for phosphorus removal in a real-scale application with low cost. Soil burial test indicated saturated EFG has a good phosphate slow-release performance (maintained for up to 60 days). Overall, EFG has demonstrated to be a promising adsorbent for phosphorus recovery.

Deep Reinforcement Learning-Based Energy Consumption Optimization for Peer-to-Peer (P2P) Communication in Wireless Sensor Networks.

The fast development of the sensors in the wireless sensor networks (WSN) brings a big challenge of low energy consumption requirements, and Peer-to-peer (P2P) communication becomes the important way to break this bottleneck. However, the interference caused by different sensors sharing the spectrum and the power limitations seriously constrains the improvement of WSN. Therefore, in this paper, we proposed a deep reinforcement learning-based energy consumption optimization for P2P communication in WSN. Specifically, P2P sensors (PUs) are considered agents to share the spectrum of authorized sensors (AUs). An authorized sensor has permission to access specific data or systems, while a P2P sensor directly communicates with other sensors without needing a central server. One involves permission, the other is direct communication between sensors. Each agent can control the power and select the resources to avoid interference. Moreover, we use a double deep Q network (DDQN) algorithm to help the agent learn more detailed features of the interference. Simulation results show that the proposed algorithm can obtain a higher performance than the deep Q network scheme and the traditional algorithm, which can effectively lower the energy consumption for P2P communication in WSN.

Simultaneous variable selection and estimation for survival data via the Gaussian seamless- L 0 $$ {L}_0 $$ penalty.

We propose a new simultaneous variable selection and estimation procedure with the Gaussian seamless- L 0 $$ {L}_0 $$ (GSELO) penalty for Cox proportional hazard model and additive hazards model. The GSELO procedure shows good potential to improve the existing variable selection methods by taking strength from both best subset selection (BSS) and regularization. In addition, we develop an iterative algorithm to implement the proposed procedure in a computationally efficient way. Theoretically, we establish the convergence properties of the algorithm and asymptotic theoretical properties of the proposed procedure. Since parameter tuning is crucial to the performance of the GSELO procedure, we also propose an extended Bayesian information criteria (EBIC) parameter selector for the GSELO procedure. Simulated and real data studies have demonstrated the prediction performance and effectiveness of the proposed method over several state-of-the-art methods.

Deep learning and big data mining for Metal-Organic frameworks with high performance for simultaneous desulfurization and carbon capture.

Carbon capture and desulfurization of flue gases are crucial for the achievement of carbon neutrality and sustainable development. In this work, the "one-step" adsorption technology with high-performance metal-organic frameworks (MOFs) was proposed to simultaneously capture the SO2 and CO2. Four machine learning algorithms were used to predict the performance indicators (NCO2+SO2, SCO2+SO2/N2, and TSN) of MOFs, with Multi-Layer Perceptron Regression (MLPR) showing better performance (R2 = 0.93). To address sparse data of MOF chemical descriptors, we introduced the Deep Factorization Machines (DeepFM) model, outperforming MLPR with a higher R2 of 0.95. Then, sensitivity analysis was employed to find that the adsorption heat and porosity were the key factors for SO2 and CO2 capture performance of MOF, while the influence of open alkali metal sites also stood out. Furthermore, we established a kinetic model to batch simulate the breakthrough curves of TOP 1000 MOFs to investigate their dynamic adsorption separation performance for SO2/CO2/N2. The TOP 20 MOFs screened by the dynamic performance highly overlap with those screened by the static performance, with 76 % containing open alkali metal sites. This integrated approach of computational screening, machine learning, and dynamic analysis significantly advances the development of efficient MOF adsorbents for flue gas treatment.

Convert Harm into Benefit: The Role of the Al10CaFe2 Phase in Al-Ca Wrought Aluminum Alloys Having High Compatibility with Fe.

The compatibility of the wrought Al-Ca alloy with the element Fe was investigated in the present study. In this work, both the Al-Ca alloy and Al-Ca-Fe alloy were synthesized through melting, casting, heat treatment, and rolling. A new ternary Al-Ca-Fe eutectic phase, identified as Al10CaFe2 with an orthorhombic structure, demonstrated enhanced performance, as revealed by nanoindentation tests. Combining the results of the nanoindentation and EBSD, it can be inferred that during the rolling and heat treatment process, the divorced eutectic phases were broken and spheroidized, and the structure of the Fe-rich alloy became finer, which promotes the formation of fine grains during the process of dynamic recrystallization and effectively hindered the grain growth during thermal treatment. Consequently, the strength of the as-rolled Al-Ca alloy was improved with the addition of 1 wt.% Fe while the ductility of the alloy was maintained. Therefore, adding Ca into the high-Fe content recycled aluminum altered the form of the Fe-containing phases in the alloy, effectively expanding the application scope of recycled aluminum alloy manufacturing. This approach also offered a method for strengthening the Al-Ca aluminum alloys. Compared to the traditional approach of reducing Fe content in alloys through metallurgical means, this study opened a new avenue for designing novel, renewable aluminum alloys highly compatible with impurity iron in scrap.

Alzheimer's Disease; Mechanism, Mutations, and Applications of Nano-Medicine.

BACKGROUND: In the past 10 years, significant progress has been made in understanding the pathogenic chain of events that causes Alzheimer's disease (AD). According to the most widely accepted concept, the production and aggregation of ß-amyloid (Aß) peptides play a critical role in AD. As a result, therapeutic intervention with these processes is the focus of intense research. The Aß peptide is cleaved by the α-secretase, ß-secretase, and γ-secretase enzymes in a region near the pathogenic amyloid precursor protein (APP) and mutations occurring site. METHODS: In the current review, a complete picture of the risk factors behind AD has been investigated. Mutations involved in AD progression have also been screened in various studies. RESULTS: Most of the mutations in the amyloid precursor protein (APP) can lead to the accumulation of APP oligomers in the brain, leading to AD. Several point mutations in APP can cause familial AD (FAD), including the Swedish mutation (K>M670/671N>L) and the A673>V mutation. The pathogenic A673>V mutation and Swedish mutation (M670>K/N671>L) are present in the same region of amyloid precursor protein (APP). However, the A673>T mutation has been shown to confer protection against AD. CONCLUSION: More investigations are needed from geographically distinct regions on mutations associated with AD development and applications of nanomedicines for better management of the disease burden in the future. Nanotechnology-produced metal nanoparticles (NPs) have gotten much attention because of their wide range of uses in the medicinal and agricultural industries. Nanomedicine containing potential phytochemicals, including GX-50 and curcumin conjugated with NPs, maybe a potential candidate for treating AD.

Immediate analgesic effect of acupuncture intervention within 10 min during acute migraine attacks: protocol of a randomised controlled trial.

INTRODUCTION: Migraine is a widespread neurological disorder characterised by recurrent moderate-to-severe headaches. These headaches can seriously affect patients' daily life and work, especially during acute attacks when patients often need immediate pain relief. This study aims to assess the immediate analgesic effect of acupuncture for 10 min during acute migraine attacks. METHODS AND ANALYSIS: The study will randomly divide 80 participants into either the acupuncture group or the sham acupuncture group with an allocation ratio of 1:1. Each group will receive 10 min of treatment, and the post-treatment evaluation will be performed after 0, 0-2, 4, 6, 8 and 10 min of acupuncture. The primary outcome is the pain Visual Analogue Scale (VAS) score assessed before and after treatment at 10 min. Additionally, secondary outcomes include the pain VAS score assessed at 0-2, 4, 6 and 8 min, blinding assessment and treatment effectiveness expectations scale. Data will be collected at baseline time and the end of treatment (after 10 min). Adverse events during each treatment period will be collected and recorded. ETHICS AND DISSEMINATION: Ethics approval was obtained from the Ethics Committee of the Second Affiliated Hospital of Yunnan University of Chinese Medicine (2022-008). All participants will provide written informed consent before randomisation. The results of this study will be published in a peer-reviewed journal and presented at conferences. TRIAL REGISTRATION NUMBER: Chinese Clinical Trial Registration Center (ChiCTR2200066976).

The Effect of Catgut Embedding at Acupoints Versus Nonacupoints in Abdominal Obesity: Protocol for a Multicenter, Double-Blind, 16-Week Randomized Controlled Trial.

BACKGROUND: Obesity is an increasing problem worldwide. The effective treatments for obesity mainly include diet, physical activity, behavioral intervention, pharmacotherapy, and bariatric surgery, which all have certain limitations. As a specific type of acupuncture therapy, acupoint catgut embedding (ACE) has gained substantial attention in the management of obesity in recent years. Previous studies suggested that ACE may be an effective obesity treatment. However, the evidence for the efficacy of ACE in abdominal obesity (AO) remains inadequate due to the paucity of high-quality studies. OBJECTIVE: This study aims to investigate the difference in the effectiveness of catgut embedding at acupoints and catgut embedding at nonacupoints in patients with AO and to further validate the efficacy and safety of ACE for AO. METHODS: This is a multicenter, double-blind, 16-week randomized controlled trial. A total of 92 eligible participants with AO will be randomly divided into 2 groups (1:1 allocation ratio). The ACE group will receive catgut embedding at acupoints and the control group will receive catgut embedding at nonacupoints. The intervention will be performed every 2 weeks for a total of 6 sessions. Follow-up will be performed every 2 weeks for a total of 2 visits. The primary outcome is waist circumference. Secondary outcomes include body weight, BMI, hip circumference, and the visual analog scale of appetite. Upon the completion of the trial, we will evaluate the effect of catgut embedding at acupoints or nonacupoints on obesity indicators in patients with AO. For treatment outcomes, an intention-to-treat analysis will be performed. RESULTS: The start of recruitment began in August 2019 and is expected to end in September 2023. CONCLUSIONS: Although studies have been conducted to demonstrate the effectiveness of ACE in the treatment of obesity, the evidence for the efficacy of ACE in AO remains insufficient due to the quality of the studies. This rigorous normative randomized controlled trial will verify the effect of catgut embedding at acupoints or nonacupoints in patients with AO. The findings will provide credible evidence as to whether ACE is an effective and safe treatment for AO. TRIAL REGISTRATION: Chinese Clinical Trial Registry ChiCTR1800016947; https://tinyurl.com/2p82257p. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/46863.

Overexpression of Sirt6 ameliorates sleep deprivation induced-cognitive impairment by modulating glutamatergic neuron function.

Sleep benefits the restoration of energy metabolism and thereby supports neuronal plasticity and cognitive behaviors. Sirt6 is a NAD+-dependent protein deacetylase that has been recognized as an essential regulator of energy metabolism because it modulates various transcriptional regulators and metabolic enzymes. The aim of this study was to investigate the influence of Sirt6 on cerebral function after chronic sleep deprivation (CSD). We assigned C57BL/6J mice to control or two CSD groups and subjected them to AAV2/9-CMV-EGFP or AAV2/9-CMV-Sirt6-EGFP infection in the prelimbic cortex (PrL). We then assessed cerebral functional connectivity (FC) using resting-state functional MRI, neuron/astrocyte metabolism using a metabolic kinetics analysis; dendritic spine densities using sparse-labeling; and miniature excitatory postsynaptic currents (mEPSCs) and action potential (AP) firing rates using whole-cell patch-clamp recordings. In addition, we evaluated cognition via a comprehensive set of behavioral tests. Compared with controls, Sirt6 was significantly decreased (P < 0.05) in the PrL after CSD, accompanied by cognitive deficits and decreased FC between the PrL and accumbens nucleus, piriform cortex, motor cortex, somatosensory cortex, olfactory tubercle, insular cortex, and cerebellum. Sirt6 overexpression reversed CSD-induced cognitive impairment and reduced FC. Our analysis of metabolic kinetics using [1-13C] glucose and [2-13C] acetate showed that CSD reduced neuronal Glu4 and GABA2 synthesis, which could be fully restored via forced Sirt6 expression. Furthermore, Sirt6 overexpression reversed CSD-induced decreases in AP firing rates as well as the frequency and amplitude of mEPSCs in PrL pyramidal neurons. These data indicate that Sirt6 can improve cognitive impairment after CSD by regulating the PrL-associated FC network, neuronal glucose metabolism, and glutamatergic neurotransmission. Thus, Sirt6 activation may have potential as a novel strategy for treating sleep disorder-related diseases.

Interpretable Machine-Learning and Big Data Mining to Predict Gas Diffusivity in Metal-Organic Frameworks.

For gas separation and catalysis by metal-organic frameworks (MOFs), gas diffusion has a substantial impact on the process' overall rate, so it is necessary to determine the molecular diffusion behavior within the MOFs. In this study, an interpretable machine learing (ML) model, light gradient boosting machine (LGBM), is trained to predict the molecular diffusivity and selectivity of 9 gases (Kr, Xe, CH4 , N2 , H2 S, O2 , CO2 , H2 , and He). For these 9 gases, LGBM displays high accuracy (average R2 = 0.962) and superior extrapolation for the diffusivity of C2 H6 . And this model calculation is five orders of magnitude faster than molecular dynamics (MD) simulations. Subsequently, using the trained LGBM model, an interactive desktop application is developed that can help researchers quickly and accurately calculate the diffusion of molecules in porous crystal materials. Finally, the authors find the difference in the molecular polarizability (ΔPol) is the key factor governing the diffusion selectivity by combining the trained LGBM model with the Shapley additive explanation (SHAP). By the calculation of interpretable ML, the optimal MOFs are selected for separating binary gas mixtures and CO2 methanation. This work provides a new direction for exploring the structure-property relationships of MOFs and realizing the rapid calculation of molecular diffusivity.

Structural basis of amine odorant perception by a mammal olfactory receptor.

Odorants are detected as smell in the nasal epithelium of mammals by two G-protein-coupled receptor families, the odorant receptors and the trace amine-associated receptors1,2 (TAARs). TAARs emerged following the divergence of jawed and jawless fish, and comprise a large monophyletic family of receptors that recognize volatile amine odorants to elicit both intraspecific and interspecific innate behaviours such as attraction and aversion3-5. Here we report cryo-electron microscopy structures of mouse TAAR9 (mTAAR9) and mTAAR9-Gs or mTAAR9-Golf trimers in complex with ß-phenylethylamine, N,N-dimethylcyclohexylamine or spermidine. The mTAAR9 structures contain a deep and tight ligand-binding pocket decorated with a conserved D3.32W6.48Y7.43 motif, which is essential for amine odorant recognition. In the mTAAR9 structure, a unique disulfide bond connecting the N terminus to ECL2 is required for agonist-induced receptor activation. We identify key structural motifs of TAAR family members for detecting monoamines and polyamines and the shared sequence of different TAAR members that are responsible for recognition of the same odour chemical. We elucidate the molecular basis of mTAAR9 coupling to Gs and Golf by structural characterization and mutational analysis. Collectively, our results provide a structural basis for odorant detection, receptor activation and Golf coupling of an amine olfactory receptor.

The Use of Visualizations to Improve Bayesian Reasoning: A Literature Review.

Decisions are often made under uncertainty. The most that one can do is use prior knowledge (e.g., base rates, prior probabilities, etc.) and make the most probable choice given the information we have. Unfortunately, most people struggle with Bayesian reasoning. Poor performance within Bayesian reasoning problems has led researchers to investigate ways to improve Bayesian reasoning. Many have found success in using natural frequencies instead of probabilities to frame problems. Beyond the quantitative format, there is growing literature on the use of visualizations or visual representations to improve Bayesian reasoning, which will be the focus of this review. In this review, we discuss studies that have found visualizations to be effective for improving Bayesian reasoning in a lab or classroom setting and discuss the considerations for using visualizations, paying special attention to individual differences. In addition, we will review the factors that influence Bayesian reasoning, such as natural frequencies vs. probabilities, problem format, individual differences, and interactivity. We also provide general and specific suggestions for future research.

Re-assessing the role of culture on the visual orientation perception of the rod and frame test.

In recent research of culture and ethnicity in visual perception, some researchers have found support for the hypothesis that more collectivistic cultures tend to be more influenced by surrounding contextual visual information than more individualistic cultures. This hypothesis suggests that even low-level visual perception may not be universal and has great implications on how vision research should be conducted. The current study reexamines this hypothesis in the rod and frame task, which tests the influence of a tilted contextual frame on orientation perception of the rod. We found no difference between participants of East Asian and Western European descent in this task. Despite not finding the cultural effect, we found a well-reported gender effect in which women were more influenced by the tilt of the frame than men, helping to ensure the quality of the data collected. Our results suggest that contextual influence on visual perception does not affect East Asians and white Western Europeans differently.

Causality modulates perception of apparent motion stimuli.

Previous studies have shown that contextual information can alter judgments of apparent motion. Specifically, the presence of causal information can even override the shortest-path bias, if the shortest path is inconsistent with a causal interpretation of the motion event. While these results demonstrate that judgments of apparent motion are affected by causality, how causality modulates lower-level spatiotemporal processing is not yet understood. Moreover, it is unclear whether subjects' judgments of apparent motion are the result of perceptual processing or higher-level reasoning. Addressing these questions, we investigated whether causal information could influence detection sensitivity in an apparent motion display. Our apparent motion displays involved two vertically stacked semicircular tubes, and contextual information suggested either motion through the top or bottom tube. Each tube contained a small aperture that would flash, appearing as if the launched object was briefly visible along the motion-path. In addition, contextual information could also be inconsistent with the flash location. In our first experiment, participants judged the location of the target flash under causal and non-causal conditions. In experiment 2, we compared the effect of causality with motion priming, a noncausal cue that may covary with the causal cue. In both experiments, detection was most influenced by causal information, being most accurate when causality was consistent with the target flash and least accurate when causal information acted as a distractor- suggesting that the visual system generates spatiotemporal predictions of object motion during perceived causal interactions.

Motion opponency at the middle temporal cortex: Preserved motion information and the effect of perceptual learning.

Motion opponency, first observed within the primate middle temporal cortex (MT), refers to the suppressing effect of opposite motion directions on neuronal activity. Namely, when opposing motion directional signals stimulate an MT neuron's receptive field, this neuron's response is comparable with that induced by flicker noise. Under such suppression, it is unknown whether any directional information is still represented at MT. In this study, we applied support vector machine (SVM) learning to human functional magnetic resonance imaging data to investigate if any motion defined orientation information was still available from suppressed MT. We found that, at least at the level of ±45° discrimination, such orientation information was still available. Interestingly, after behavioural perceptual learning that improved human discrimination of fine orientation discrimination (e.g. 42° vs. 48°) using the MT-suppressive motion stimuli, the SVM discrimination of ±45° worsened when functional magnetic resonance imaging (fMRI) signals at post-learning MT were used. This result is consistent with findings in Thompson et al. (2013) that, post-perceptual learning, MT suppression was not released, suggesting that motion opponency was perhaps functionally too important for perceptual learning to overcome.

Investigation of novel de novo KCNC2 variants causing severe developmental and early-onset epileptic encephalopathy.

Purpose The voltage-gated potassium channel Kv3.2, encoded by KCNC2, facilitates fast-spiking GABAergic interneurons to fire action potentials at high frequencies. It is pivotal to maintaining excitation/inhibition balance in mammalian brains. This study identified two novel de novo KCNC2 variants, p.Pro470Ser (P470S) and p.Phe382Leu (F382L), in patients with early onset developmental and epileptic encephalopathy (DEE). Methods To examine the molecular basis of DEE, we studied the functional characteristics of variant channels using patch-clamp techniques and computational modeling. Results Whole-cell patch clamp recordings from infected HEK293 cells revealed that channel activation and deactivation kinetics strongly decreased in both Kv3.2 P470S and F382L variant channels. This decrease also occurred in Kv3.2 p.Val471Leu (V471L) channels, known to be associated with DEE. In addition, Kv3.2 F382L and V471L variants exhibited a significant increase in channel conductance and a ∼20 mV negative shift in the threshold for voltage-dependent activation. Simulations of model GABAergic interneurons revealed that all variants decreased neuronal firing frequency. Thus, the variants' net loss-of-function effects disinhibited neural networks. Conclusion Our findings provide compelling evidence supporting the role of KCNC2 as a disease-causing gene in human neurodevelopmental delay and epilepsy.

Zeolite Imidazolate Frameworks (ZIFs) Derived Nanomaterials and their Hybrids for Advanced Secondary Batteries and Electrocatalysis.

Zeolite imidazolate frameworks (ZIFs), as a typical class of metal-organic frameworks (MOFs), have attracted a great deal of attention in the field of energy storage and conversation due to their chemical structure stability, facile synthesis and environmental friendliness. Among of ZIFs family, the zinc-based imidazolate framework (ZIF-8) and cobalt-based imidazolate framework (ZIF-67) have considered as promising ZIFs materials, which attributed to their tunable porosity, stable structure, and desirable electrical conductivity. To date, various ZIF-8 and ZIF67 derived materials, including carbon materials, metal oxides, sulfides, selenides, carbides and phosphides, have been successfully synthesized using ZIFs as templates and evaluated as promising electrode materials for secondary batteries and electrocatalysis. This review provides an effective guide for the comprehension of the performance optimization and application prospects of ZIFs derivatives, specifically focusing on the optimization of structure and their application in secondary batteries and electrocatalysis. In detail, we present recent advances in the improvement of electrochemical performance of ZIF-8, ZIF-67 and ZIF-8@ZIF-67 derived nanomaterials and their hybrids, including carbon materials, metal oxides, carbides, oxides, sulfides, selenides, and phosphides for high-performance secondary batteries and electrocatalysis.

Co-substitution in a Prussian blue analog with a hollow heterostructure for ultrahigh capacity and rate capability aqueous Zn2+ batteries.

Nickel and manganese co-substitution into hollow Prussian blue nanocubes (H-PBMN) has been successfully carried out via utilizing a high-concentration polymer template to grow manganese-Prussian blue (PBM) and nickel-Prussian blue (PBN) through a slow nucleation process. Due to the hollow structure and double metal co-substitution, the properties of the electrode material have been optimized, and it presents an ultrahigh capacity of 138.4 mA h g-1 at 0.05 A g-1.

Selective Hydrogenation of 5-Hydroxymethylfurfural to 2,5-Dimethylfuran Over Popcorn-Like Nitrogen-Doped Carbon-Confined CuCo Bimetallic Catalyst.

A new type of biomass-based liquid fuel, 2,5-dimethylfuran (DMF), has attracted significant attention owing to its unique physical properties and carbon neutrality. It can be obtained from the hydrogenation of 5-hydroxymethylfurfural (HMF), an important biomass platform compound. In this study, we developed a nitrogen-doped carbon-confined CuCo bimetallic catalyst with a popcorn-like structure for the selective hydrogenation of HMF with high efficiency and adequate stability. Under optimized conditions, 100% HMF conversion and 93.7% DMF selectivity were achieved. The structure of the catalyst was characterized using XRD, XPS, SEM, and TEM. It was observed that carbon spheres, which were covered by nitrogen-doped carbon nanotubes, uniformly formed, while metal particles were confined in the nitrogen-doped carbon nanotubes. The popcorn-like structure exhibited a larger surface area and provided more contact sites, while the confined metal particles were the main active sites. The synergistic effect between Cu and Co was beneficial for DMF selectivity.