Large numbers of catalysts have been developed for the electrochemical reduction of CO2 to value‐added liquid fuels. However, it remains a challenge to maintain a high current efficiency in a wide ...negative potential range for achieving a high production rate of the target products. Herein, we report a 2D/0D composite catalyst composed of bismuth oxide nanosheets and nitrogen‐doped graphene quantum dots (Bi2O3‐NGQDs) for highly efficient electrochemical reduction of CO2 to formate. Bi2O3‐NGQDs demonstrates a nearly 100 % formate Faraday efficiency (FE) at a moderate overpotential of 0.7 V with a good stability. Strikingly, Bi2O3‐NGQDs exhibit a high activity (average formate FE of 95.6 %) from −0.9 V to −1.2 V vs. RHE. Additionally, DFT calculations reveal that the origin of enhanced activity in this wide negative potential range can be attributed to the increased adsorption energy of CO2(ads) and OCHO* intermediate after combination with NGQDs.
Waiting for a mate: A bismuth oxide nanosheet/nitrogen‐doped graphene quantum dot composite catalyst (NGQDs) for the highly efficient electrochemical reduction of CO2 to formate was prepared. NGQDs significantly enhance the activity of Bi2O3 nanosheets, achieving almost 100 % formate Faraday efficiency at a moderate overpotential of 0.7 V and an average formate Faraday efficiency of 95.6 % over a wide negative potential range from −0.9 V to −1.2 V vs. RHE.
Abstract
Developing effective catalysts based on earth abundant elements is critical for CO
2
electroreduction. However, simultaneously achieving a high Faradaic efficiency (FE) and high current ...density of CO (
j
CO
) remains a challenge. Herein, we prepare a Mn single-atom catalyst (SAC) with a Mn-N
3
site embedded in graphitic carbon nitride. The prepared catalyst exhibits a 98.8% CO FE with a
j
CO
of 14.0 mA cm
−2
at a low overpotential of 0.44 V in aqueous electrolyte, outperforming all reported Mn SACs. Moreover, a higher
j
CO
of 29.7 mA cm
−2
is obtained in an ionic liquid electrolyte at 0.62 V overpotential. In situ X-ray absorption spectra and density functional theory calculations demonstrate that the remarkable performance of the catalyst is attributed to the Mn-N
3
site, which facilitates the formation of the key intermediate COOH
*
through a lowered free energy barrier.
Microneedle arrays (MA) have been extensively investigated in recent decades for transdermal drug delivery due to their pain-free delivery, minimal skin trauma, and reduced risk of infection. ...However, porous MA received relatively less attention due to their complex fabrication process and ease of fracturing. Here, we present a titanium porous microneedle array (TPMA) fabricated by modified metal injection molding (MIM) technology. The sintering process is simple and suitable for mass production. TPMA was sintered at a sintering temperature of 1250°C for 2 h. The porosity of TPMA was approximately 30.1% and its average pore diameter was about 1.3 μm. The elements distributed on the surface of TPMA were only Ti and O, which may guarantee the biocompatibility of TPMA. TPMA could easily penetrate the skin of a human forearm without fracture. TPMA could diffuse dry Rhodamine B stored in micropores into rabbit skin. The cumulative permeated flux of calcein across TPMA with punctured skin was 27 times greater than that across intact skin. Thus, TPMA can continually and efficiently deliver a liquid drug through open micropores in skin.
Uniform temperature distribution during quenching thermal treatment is crucial for achieving exceptional mechanical and physical properties of alloy materials. Accurate and rapid prediction of the 3D ...transient temperature field model of large-scale aluminum alloy workpieces is key to realizing effective thermal treatment. This paper establishes a 3D transient temperature field model of large aluminum alloy workpieces and proposes a multi-loss consistency optimization-based physics-informed neural network (MCO-PINN) to realize soft sensing of the 3D temperature field model. The method is based on a MLP structure and adopts Gaussian activation functions. A surrogate model of the partial differential equation (PDE) is first constructed, and the residuals of the PDE, initial and boundary conditions, and observed data are encoded into the loss functions of the network. By establishing a Gaussian probability distribution model of each loss function and combining it with maximum likelihood estimation, the weight consistency optimization method of each loss function is then proposed to further improve the approximation ability of the model. To optimize the training speed of the network, an adaptive initial-value-eigenvector coding clustering (AIV-ECC) algorithm is finally proposed, which quickly determines the parameters of the Gaussian activation function, reduces the dependence on the initial value and improves the generalization performance of the network. Simulation and industrial experiments demonstrate that the proposed MCO-PINN can solve the 3D transient temperature field model with high precision and high time efficiency based on sparse measurements.
Real-time, continuous and accurate blast furnace burden level information is of great significance for controlling the charging process, ensuring a smooth operation of a blast furnace, reducing ...energy consumption and emissions and improving blast furnace output. However, the burden level information measured by conventional mechanical stock rods and radar probes exhibit problems of weak anti-interference ability, large fluctuations in accuracy, poor stability and discontinuity. Therefore, a space-time fusion prediction and detection method of burden level based on a long-term focus memory network (LFMN) and an efficient structure self-tuning RBF neural network (ESST-RBFNN) is proposed. First, the space dimensional features are extracted by the space regression model based on radar data. Then, the LFMN is designed to predict the burden level and extract the time dimensional features. Finally, the ESST-RBFNN based on a proposed fast eigenvector space clustering algorithm (ESC) is constructed to obtain reliable and continuous burden level information with high accuracy. Both the simulation results and industrial verification indicate that the proposed method can provide real-time and continuous burden level information in real-time, which has great practical value for industrial production.
In this work, a novel composite adsorbent was successfully prepared by zeolite imidazolate framework-8/fluorinated graphene layer-by-layer covalently bonded on SiO2 microspheres, and followed to be ...packed into micro pipette tip for extraction of trace chlorophenols prior to their detection by high performance liquid chromatography (HPLC). The morphology and structure of adsorbent material was characterized by field emission scanning electron microscopy with energy dispersive spectrometer, X-ray diffraction, and N2 adsorption. The parameters including the amount of adsorbent, sampling volume, sampling rate, sample pH, and desorption solvent affected the extraction performance was systematically investigated by pipette tip solid-phase extraction (PT-SPE) coupled with HPLC analysis. Under the optimized condition, the linearity of this method ranged from 20 to 2000 ng mL−1 for chlorophenols (CPs) with determination coefficient higher than 0.99. The limit of detection (at a signal-to-noise ratio of 3) were in the range 2–20 ng mL−1 for tap water and black tea drinks, 0.2–2 μg g−1 for honey. The relative recoveries of the CPs from spiked samples ranged from 71.8% to 104.7%, with relative standard deviations less than 6.2%. The filled extraction tube exhibited good stability and reproducibility. The proposed method has been successfully used to detect CPs in water and drinks with satisfactory recoveries.
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•ZIF-8/FG was layer-by-layer covalently bonded on SiO2 microspheres.•Incorporation of FG induced growth of ZIF-8 for enhanced extraction.•Excellent extraction performance was obtained towards chlorophenols by PT-SPE.
The real-time, continuity, and accuracy of blast furnace stockline information are of great significance in reducing energy consumption and improving smelting efficiency. However, the traditional ...mechanical measurement method has the problem of measuring point discontinuity, while the radar measurement method exhibits problems such as weak anti-interference ability, low accuracy, and poor stability. Therefore, a high-dimensional, spatial feature stockline detection method based on the maximum likelihood radial basis function model (MLRBFM) and structural dynamic self-optimization RBF neural network (SDSO-RBFNN) is proposed. Firstly, the discrete time series joint partition method is used to extract the time dimension periodic features of the blast furnace stockline. Based on MLRBFM, the high-dimensional spatial features of the stockline are then obtained. Finally, an SDSO-RBFNN is constructed based on an eigen orthogonal matrix and a right triangular matrix decomposition (QR) direct clustering algorithm with spatial–temporal features as input, so as to obtain continuous, high-precision stockline information. Both the simulation results and industrial validation indicate that the proposed method can provide real-time and accurate stockline information, and has great practical value for industrial production.
Abstract
Soft magneto-active machines capable of magnetically controllable shape-morphing and locomotion have diverse promising applications such as untethered biomedical robots. However, existing ...soft magneto-active machines often have simple structures with limited functionalities and do not grant high-throughput production due to the convoluted fabrication technology. Here, we propose a facile fabrication strategy that transforms 2D magnetic sheets into 3D soft magneto-active machines with customized geometries by incorporating origami folding. Based on automated roll-to-roll processing, this approach allows for the high-throughput fabrication of soft magneto-origami machines with a variety of characteristics, including large-magnitude deploying, sequential folding into predesigned shapes, and multivariant actuation modes (e.g., contraction, bending, rotation, and rolling locomotion). We leverage these abilities to demonstrate a few potential applications: an electronic robot capable of on-demand deploying and wireless charging, a mechanical 8-3 encoder, a quadruped robot for cargo-release tasks, and a magneto-origami arts/craft. Our work contributes for the high-throughput fabrication of soft magneto-active machines with multi-functionalities.
The present study aimed to optimize the process for extracting cellulose nanocrystals (CNCs) from sugarcane bagasse through ultrasonic-assisted sulfuric acid hydrolysis and its subsequent ...modification with L-malic acid and silane coupling agent KH-550. The effects of the different modification methods and the order of modification on the structures and properties of bagasse CNCs were explored. The results indicated that the optimal process conditions were achieved at an acid-digestion temperature of 50 °C, a reaction time of 70 min, an ultrasonic power of 250 W, and a volume fraction of 55%. The modified CNCs were analyzed using infrared spectral, X-ray diffraction, and thermogravimetric techniques, which revealed that L-malic acid was attached to the hydroxyl group on the CNCs via ester bond formations, and the silane coupling agent KH-550 was adsorbed effectively on the CNCs' surfaces. Moreover, it was observed that the modification of the CNCs by L-malic acid and the KH-550 silane coupling agent occurred only on the surface, and the esterification-crosslinking modification method provided the best thermal stability. The performance of self-made CNC was found to be superior to that of purchased CNC based on the transmission electron microscopy analysis. Furthermore, the modified esterified-crosslinked CNCs exhibited the best structure and performance, thereby offering a potential avenue for the high-value utilization of sugarcane bagasse, a byproduct of sugarcane sugar production, and the expansion of the comprehensive utilization of sugarcane bagasse.
Small molecular PD‐1 inhibitors are lacking in current immuno‐oncology clinic. PD‐1/PD‐L1 antibody inhibitors currently approved for clinical usage block interaction between PD‐L1 and PD‐1 to enhance ...cytotoxicity of CD8+ cytotoxic T lymphocyte (CTL). Whether other steps along the PD‐1 signaling pathway can be targeted remains to be determined. Here, we report that methylene blue (MB), an FDA‐approved chemical for treating methemoglobinemia, potently inhibits PD‐1 signaling. MB enhances the cytotoxicity, activation, cell proliferation, and cytokine‐secreting activity of CTL inhibited by PD‐1. Mechanistically, MB blocks interaction between Y248‐phosphorylated immunoreceptor tyrosine‐based switch motif (ITSM) of human PD‐1 and SHP2. MB enables activated CTL to shrink PD‐L1 expressing tumor allografts and autochthonous lung cancers in a transgenic mouse model. MB also effectively counteracts the PD‐1 signaling on human T cells isolated from peripheral blood of healthy donors. Thus, we identify an FDA‐approved chemical capable of potently inhibiting the function of PD‐1. Equally important, our work sheds light on a novel strategy to develop inhibitors targeting PD‐1 signaling axis.
Synopsis
PD‐1 inhibitors that are currently used in the clinic exhibit toxicity and limited patient response rate. This study identifies methylene blue (MB), an FDA‐approved chemical for treating methemoglobinemia, as a new potent PD‐1 inhibitor.
MB activates T‐cell functions through inhibiting the recruitment of SHP2 to PD‐1.
MB treatment effectively shrinks tumors in both an allograft mouse model and an autochthonous mouse model for lung cancer.
MB activates human CD8+ T cells that are otherwise suppressed by PD‐1 signaling.
PD‐1 inhibitors that are currently used in the clinic exhibit toxicity and limited patient response rate. This study identifies methylene blue (MB), an FDA‐approved chemical for treating methemoglobinemia, as a new potent PD‐1 inhibitor.
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