Abstract
Single-atom catalysts (SACs) are promising candidates to catalyze electrochemical CO
2
reduction (ECR) due to maximized atomic utilization. However, products are usually limited to CO ...instead of hydrocarbons or oxygenates due to unfavorable high energy barrier for further electron transfer on synthesized single atom catalytic sites. Here we report a novel partial-carbonization strategy to modify the electronic structures of center atoms on SACs for lowering the overall endothermic energy of key intermediates. A carbon-dots-based SAC margined with unique CuN
2
O
2
sites was synthesized for the first time. The introduction of oxygen ligands brings remarkably high Faradaic efficiency (78%) and selectivity (99% of ECR products) for electrochemical converting CO
2
to CH
4
with current density of 40 mA·cm
-2
in aqueous electrolytes, surpassing most reported SACs which stop at two-electron reduction. Theoretical calculations further revealed that the high selectivity and activity on CuN
2
O
2
active sites are due to the proper elevated CH
4
and H
2
energy barrier and fine-tuned electronic structure of Cu active sites.
Electrochemical reduction of carbon dioxide (CO2) is an appealing approach toward tackling climate change associated with atmospheric CO2 emissions. This approach uses CO2 as the carbon feedstock to ...produce value‐added chemicals, resulting in a carbon‐neutral (or even carbon‐negative) process for chemical production. Many efforts have been devoted to the development of CO2 electrolysis devices that can be operated at industrially relevant rates; however, limited progress has been made, especially for valuable C2+ products. Herein, a nanoporous copper CO2 reduction catalyst is synthesized and integrated into a microfluidic CO2 flow cell electrolyzer. The CO2 electrolyzer exhibits a current density of 653 mA cm−2 with a C2+ product selectivity of ≈62% at an applied potential of −0.67 V (vs reversible hydrogen electrode). The highly porous electrode structure facilitates rapid gas transport across the electrode–electrolyte interface at high current densities. Further investigations on electrolyte effects reveal that the surface pH value is substantially different from the pH of bulk electrolyte, especially for nonbuffering near‐neutral electrolytes when operating at high currents.
A nanoporous copper catalyst for CO2 reduction is synthesized and integrated into a microfluidic CO2 flow cell electrolyzer with well‐engineered electrode–electrolyte interface. The CO2 electrolyzer exhibits a current density over 650 mA cm−2 with a C2+ product selectivity of ≈62% at a mild overpotential, which represents one of the highest performances that have been achieved to date.
We report a new strategy to prepare a composite catalyst for highly efficient electrochemical CO2 reduction reaction (CO2RR). The composite catalyst is made by anchoring Au nanoparticles on Cu ...nanowires via 4,4′‐bipyridine (bipy). The Au‐bipy‐Cu composite catalyzes the CO2RR in 0.1 m KHCO3 with a total Faradaic efficiency (FE) reaching 90.6 % at −0.9 V to provide C‐products, among which CH3CHO (25 % FE) dominates the liquid product (HCOO−, CH3CHO, and CH3COO−) distribution (75 %). The enhanced CO2RR catalysis demonstrated by Au‐bipy‐Cu originates from its synergistic Au (CO2 to CO) and Cu (CO to C‐products) catalysis which is further promoted by bipy. The Au‐bipy‐Cu composite represents a new catalyst system for effective CO2RR conversion to C‐products.
Joint action: Au nanoparticles were attached to Cu nanowires through a 4,4′‐bipyridine (bipy) linker to form the new composite catalyst Au‐bipy‐Cu. Thanks to the synergistic catalysis of Au (for CO2 to CO), Cu (for CO coupling), and bipy (for the CO2* stabilization and protonation), this catalyst demonstrates enhanced electrochemical reduction of CO2 to hydrocarbons.
Deep neural networks excel at finding hierarchical representations that solve complex tasks over large datasets. How can we humans understand these learned representations? In this work, we present ...network dissection, an analytic framework to systematically identify the semantics of individual hidden units within image classification and image generation networks. First, we analyze a convolutional neural network (CNN) trained on scene classification and discover units that match a diverse set of object concepts. We find evidence that the network has learned many object classes that play crucial roles in classifying scene classes. Second, we use a similar analytic method to analyze a generative adversarial network (GAN) model trained to generate scenes. By analyzing changes made when small sets of units are activated or deactivated, we find that objects can be added and removed from the output scenes while adapting to the context. Finally, we apply our analytic framework to understanding adversarial attacks and to semantic image editing.
Tumor hypoxia, the “Achilles’ heel” of current cancer therapies, is indispensable to drug resistance and poor therapeutic outcomes especially for radiotherapy. Here we propose an in situ catalytic ...oxygenation strategy in tumor using porphyrinic metal‐organic framework (MOF)‐gold nanoparticles (AuNPs) nanohybrid as a therapeutic platform to achieve O2‐evolving chemoradiotherapy. The AuNPs decorated on the surface of MOF effectively stabilize the nanocomposite and serve as radiosensitizers, whereas the MOF scaffold acts as a container to encapsulate chemotherapeutic drug doxorubicin. In vitro and in vivo studies verify that the catalase‐like nanohybrid significantly enhances the radiotherapy effect, alleviating tumor hypoxia and achieving synergistic anticancer efficacy. This hybrid nanomaterial remarkably suppresses the tumor growth with minimized systemic toxicity, opening new horizons for the next generation of theranostic nanomedicines.
A catalase‐like nanohybrid based on AuNPs/gold(III) porphyrinic metal‐organic frameworks is fabricated for O2 self‐supported chemoradiotherapy. Such an all‐in‐one nanohybrid holds advantages of modulating tumor hypoxia, amplifying radiation effect, regulating drug release and combining chemotherapy with radiotherapy, which will be a paradigm in O2‐elevated radiochemotherapy that offers a novel strategy in multimodal cancer therapy.
We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but ...also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. We demonstrate that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks. Moreover, since the release of the pi×2pi× software associated with this paper, hundreds of twitter users have posted their own artistic experiments using our system. As a community, we no longer hand-engineer our mapping functions, and this work suggests we can achieve reasonable results without handengineering our loss functions either.
Bearing remaining useful life (RUL) prediction plays a crucial role in guaranteeing safe operation of machinery and reducing maintenance loss. In this paper, we present a new deep feature learning ...method for RUL estimation approach through time frequency representation (TFR) and multiscale convolutional neural network (MSCNN). TFR can reveal nonstationary property of a bearing degradation signal effectively. After acquiring time-series degradation signals, we get TFRs, which contain plenty of useful information using wavelet transform. Owing to high dimensionality, the size of these TFRs is reduced by bilinear interpolation, which are further regarded as inputs for deep learning models. Here, we introduce an MSCNN model structure, which keeps the global and local information synchronously compared to a traditional convolutional neural network (CNN). The salient features, which contribute for RUL estimation, can be learned automatically by MSCNN. The effectiveness of the presented method is validated by the experiment data. Compared to traditional data-driven and different CNN-based feature extraction methods, the proposed method shows enhanced performance in the prediction accuracy.
Gut microbiota and their major metabolites, short-chain fatty acids (SCFAs), are recognized as important players in gut homeostasis and metabolic disease occurance. A convenient and sensitive ...detection method is needed to profile SCFAs in limited and complex biological samples. The gas chromatography/mass spectrometry (GC/MS) is the most common method for SCFAs profiling in biological samples. Trimethylsilyl (TMS) derivatization reagents such as N, O-bis(trimethyl-silyl)-trifluoroacetamide (BSTFA) are commonly used in GC/MS analysis to improve sensitivity and accuracy, but they were barely used in SCFA analysis due to their sensitivity to moisture and the volatility of SCFAs. Here, we developed a rapid, convenient and reliable method for SCFAs profiling in small amounts of fecal and serum samples by GC/MS using BSTFA in combination with sodium sulfate dehydration pretreatment. SCFAs were extracted with anhydrous ether from acidified fecal water extract or serum samples, followed by dehydration with sodium sulfate and BSTFA derivatization at a reduced temperature. Select ion monitoring mode was used for highly sensitive quantification of SCFAs by GC/MS. The derivation with BSTFA at 37 °C or 22 °C showed an excellent linearity (R2 > 0.999), good recoveries (81.27–128.42%), high repeatability (RSD < 2%) and low limit of detections (LODs) of different SCFAs ranging from 0.064 to 0.067 µM. All major SCFAs including acetic acid, propionic acid, isobutyric acid, butyric acid, isovaleric acid and valeric acid were identified and quantified accurately in fecal and serum samples. In conclusions, a reliable, convenient and sensitive method wasdeveloped for the measurement of SCFA and other volatile compounds in small biological samples using sodium sulfate dehydration pretreatment and BSTFA derivatization-based GC/MS analyses.
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•A GC/MS SCFA profiling method combining BSTFA derivatization and Na2SO4 dehydration.•Na2SO4 dehydration breaks the moisture's limit on the use of BSTFA in SCFA analysis.•The method showed excellent performance and profiled mice fecal or serum SCFA.
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