Electrochemical carbon dioxide reduction to fuels presents one of the great challenges in chemistry. Herein we present an understanding of trends in electrocatalytic activity for carbon dioxide ...reduction over different metal catalysts that rationalize a number of experimental observations including the selectivity with respect to the competing hydrogen evolution reaction. We also identify two design criteria for more active catalysts. The understanding is based on density functional theory calculations of activation energies for electrochemical carbon monoxide reduction as a basis for an electrochemical kinetic model of the process. We develop scaling relations relating transition state energies to the carbon monoxide adsorption energy and determine the optimal value of this descriptor to be very close to that of copper.
Dilute alloying is an effective strategy to tune properties of solid catalysts but is rarely leveraged in complex reactions beyond small molecule conversion. In this work, dilute dopants are ...demonstrated to serve as activating centers to construct multiatom catalytic domains in metal nitride electrocatalysts for lithium–sulfur (Li–S) batteries, of which the sulfur cathode suffers from sluggish and complex conversion reactions. With titanium nitride (TiN) as a model system, the dilute cobalt alloying is shown to greatly improve the reaction kinetics while inducing negligible catalyst reconstruction. Compared to the pristine TiN, the dilute nitride alloy catalyst enables onefold increase in the high rate (2.0 C) capacities of Li–S batteries, as well as an impressively low cyclic decay rate of 0.17% at a sulfur loading of 4.0 mgS cm−2. This work opens up new opportunities toward the rational design of Li–S electrocatalysts by dilute alloying and also enlightens the understandings of complex domain‐catalyzed reactions in energy applications.
Dilute alloying implants “activating” centers in nitride alloy electrocatalysts to boost lithium–sulfur (Li–S) batteries. Dilute Co dopants activate the surrounding N and Ti atoms to construct multiatom active domains for efficient bidirectional catalysis of S redox reactions. The corresponding dilute nitride alloy improves the reaction kinetics and electrochemical performance of Li–S batteries.
Heteroatom-doping is a practical means to boost RuO
for acidic oxygen evolution reaction (OER). However, a major drawback is conventional dopants have static electron redistribution. Here, we report ...that Re dopants in Re
Ru
O
undergo a dynamic electron accepting-donating that adaptively boosts activity and stability, which is different from conventional dopants with static dopant electron redistribution. We show Re dopants during OER, (1) accept electrons at the on-site potential to activate Ru site, and (2) donate electrons back at large overpotential and prevent Ru dissolution. We confirm via in situ characterizations and first-principle computation that the dynamic electron-interaction between Re and Ru facilitates the adsorbate evolution mechanism and lowers adsorption energies for oxygen intermediates to boost activity and stability of Re
Ru
O
. We demonstrate a high mass activity of 500 A g
(7811 A g
) and a high stability number of S-number = 4.0 × 10
n
n
to outperform most electrocatalysts. We conclude that dynamic dopants can be used to boost activity and stability of active sites and therefore guide the design of adaptive electrocatalysts for clean energy conversions.
Pulmonary monitoring is crucial for the diagnosis and management of respiratory conditions, especially after the epidemic of coronavirus disease. Electrical impedance tomography (EIT) is an ...alternative non-radioactive tomographic imaging tool for monitoring pulmonary conditions. This review proffers the current EIT technical principles and applications on pulmonary monitoring, which gives a comprehensive summary of EIT applied on the chest and encourages its extensive usage to clinical physicians. The technical principles involving EIT instrumentations and image reconstruction algorithms are explained in detail, and the conditional selection is recommended based on clinical application scenarios. For applications, specifically, the monitoring of ventilation/perfusion (V/Q) is one of the most developed EIT applications. The matching correlation of V/Q could indicate many pulmonary diseases, e.g., the acute respiratory distress syndrome, pneumothorax, pulmonary embolism, and pulmonary edema. Several recently emerging applications like lung transplantation are also briefly introduced as supplementary applications that have potential and are about to be developed in the future. In addition, the limitations, disadvantages, and developing trends of EIT are discussed, indicating that EIT will still be in a long-term development stage before large-scale clinical applications.
Abstract
The electrochemical conversion of carbon di-/monoxide into commodity chemicals paves a way towards a sustainable society but it also presents one of the great challenges in catalysis. ...Herein, we present the trends in selectivity towards specific dicarbon oxygenate/hydrocarbon products from carbon monoxide reduction on transition metal catalysts, with special focus on copper. We unveil the distinctive role of electrolyte pH in tuning the dicarbon oxygenate/hydrocarbon selectivity. The understanding is based on density functional theory calculated energetics and microkinetic modeling. We identify the critical reaction steps determining selectivity and relate their transition state energies to two simple descriptors, the carbon and hydroxide binding strengths. The atomistic insight gained enables us to rationalize a number of experimental observations and provides avenues towards the design of selective electrocatalysts for liquid fuel production from carbon di-/monoxide.
Bimetallic catalysts are promising for the most difficult thermal and electrochemical reactions, but modeling the many diverse active sites on polycrystalline samples is an open challenge. We present ...a general framework for addressing this complexity in a systematic and predictive fashion. Active sites for every stable low-index facet of a bimetallic crystal are enumerated and cataloged, yielding hundreds of possible active sites. The activity of these sites is explored in parallel using a neural-network-based surrogate model to share information between the many density functional theory (DFT) relaxations, resulting in activity estimates with an order of magnitude fewer explicit DFT calculations. Sites with interesting activity were found and provide targets for follow-up calculations. This process was applied to the electrochemical reduction of CO2 on nickel gallium bimetallics and indicated that most facets had similar activity to Ni surfaces, but a few exposed Ni sites with a very favorable on-top CO configuration. This motif emerged naturally from the predictive modeling and represents a class of intermetallic CO2 reduction catalysts. These sites rationalize recent experimental reports of nickel gallium activity and why previous materials screens missed this exciting material. Most importantly these methods suggest that bimetallic catalysts will be discovered by studying facet reactivity and diversity of active sites more systematically.
The development of emerging rechargeable batteries is often hindered by limited chemical understanding composing of entangled patterns in an enormous space. Herein, we propose an interpretable hybrid ...machine learning framework to untangle intractable degradation chemistries of conversion‐type batteries. Rather than being a black box, this framework not only demonstrates an ability to accurately forecast lithium‐sulfur batteries (with a test mean absolute error of 8.9 % for the end‐of‐life prediction) but also generate useful physical understandings that illuminate future battery design and optimization. The framework also enables the discovery of a previously unknown performance indicator, the ratio of electrolyte amount to high‐voltage‐region capacity at the first discharge, for lithium‐sulfur batteries complying practical merits. The present data‐driven approach is readily applicable to other energy storage systems due to its versatility and flexibility in modules and inputs.
An interpretable hybrid machine learning framework is designed for post‐lithium‐ion battery (e.g. lithium‐sulfur battery) forecast using lab‐scale battery dataset with compromised quality and tremendous variables. Key knowledge about the complex and dynamic degradation chemistries is extracted to illuminate future battery design and optimization.
•A new method for preparing mesoporous activated carbon fibers was introduced.•ACF with 2604.7m2/g specific surface area and 1.433cm3/g total pore volume.•86.8% pore volume was from the contribution ...of the small mesopores of 2–4nm.•The effects of wood charcoal on the formed mesoporous were investigated.
Activated carbon fiber (C-WACF) with super high surface area and well-developed small mesopores were prepared by liquefied wood and uses wood charcoal (WC) as additive. The characterization and properties of C-WACF were investigated by XRD, XPS and N2 adsorption. Results showed the pore development was significant at temperatures >750°C, and reached a maximum BET surface area (2604.7m2/g) and total pore volume (1.433cm3/g) at 850°C, of which 86.8% was from the contribution of the small mesopores of 2–4nm. It was also found that the mesopore volume and methylene blue adsorption of C-WACF were highly increased as the temperature increases from 750 to 850°C. Additionally, the reduction of graphitic layers, the obvious changes of functional groups and the more unstable carbons on the surface of C-WACF, which played important roles in the formation of mesopores, were also observed.
As biomarkers, DNA methylation is used to detect colorectal cancer (CRC) and make assessment of CRC prognosis. The published findings showed the association between the methylation of SFRP1, SFRP2, ...and WIF1, located in the Wnt signaling pathway, and the prognosis of CRC were not consistent. Our study aimed to explore the potential possibility of SFRP1, SFRP2, and WIF1 concomitant promoter methylation as prognostic biomarkers of postoperative CRC patients.
As a total of 307 sporadic postoperative CRC patients were followed up, we detected SFRP1, SFRP2, and WIF1 methylation obtained from tumor tissues and adjacent non-tumor tissues respectively on the basis of methylation-sensitive high resolution melting analysis. Univariate and multivariate Cox regressions were carried out so as to assess the potential possibility of SFRP1, SFRP2, and WIF1 promoter methylation as predictors of prognosis. Confounders in our study were controlled by Propensity Score (PS) analysis.
The SFRP1, SFRP2, and WIF1 methylation levels in tumor tissues were significantly higher than that in adjacent non-tumor tissues (P < 0.001). SFRP2 hypermethylation was significantly associated with a favorable clinical outcome at the hazard ratio (HR) of 0.343 95% confidence intervals (CI): 0.164-0.718, P = 0.005 and 0.410 (95% CI: 0.200-0.842, P = 0.015) in multivariate Cox regression and PS analysis, respectively. Co-hypermethylation of SFRP1 and SFRP2 was significantly associated with a favorable clinical outcome at the HR of 0.333 (95% CI: 0.159-0.694, P = 0.003) and 0.398 (95% CI: 0.192-0.821, P = 0.013) in multivariate Cox regression and PS analysis, respectively. Co-hypermethylation of SFRP1, SFRP2 and WIF1 was significantly associated with a favorable clinical outcome at the HR of 0.326 (95% CI: 0.117-0.908, P = 0.032) and 0.401 (95% CI: 0.146-1.106, P = 0.077) in multivariate Cox regression and PS analysis, respectively.
SFRP1, SFRP2, and WIF1 were frequently hypermethylated in CRC tumor tissues. It was apparent that the promoter hypermethylation of SFRP2 and co-hypermethylation of SFRP1 and SFRP2 might be considered as independent prognostic predictors for survival advantage of postoperative CRC patients.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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