arXiv subjects

Qixuan Wu

Publications and source records attributed to Qixuan Wu.

4 recordsLinked to original sources

arXiv2024

High efficient sunlight-driven CO2 hydrogenation to methanol over NiZn intermetallic catalysts under atmospheric pressure

The synthesis of solar methanol through direct CO2 hydrogenation using solar energy is of great importance in advancing a sustainable energy economy. In this study, non-precious NiZn intermetallic/ZnO catalyst is reported to catalyze the hydrogenation of CO2 to methanol using sunlight irradiation (1sun). The NiZn-ZnO interface is identified as the active site to stabilize the key intermediates of HxCO*. At ambient pressure, the NiZn-ZnO catalyst demonstrates a methanol production rate of 127.5 umol g-1h-1 from solar driven CO2 hydrogenation, with a remarkable 100% selectivity towards methanol in the total organic products. Notably, this production rate stands as the highest record for photothermic CO2 hydrogenation to methanol in continuous-flow reactors with sunlight as the only requisite energy input. This discovery not only paves the way for the development of novel catalysts for CO2 hydrogenation to methanol but also marks a significant stride towards a full solar-driven chemical energy storage.

cond-mat.mtrl-sci↗

arXiv2024

Zero absorption and large negative refractive index in a left-handed four-level atomic media

In this paper,we have investigated three external fields interacting with the four-level atomic system described by the density-matrix approach.The atomic system exhibits left-handedness with zero absorption as well as large negative refractive index.Varying the parameters of the three external fields,the properties of zero absorption,large negative refractive index from the atomic system keep unvarying.Our scheme proposes an approach to obtain negative refractive medium with zero absorption. The zero absorption property of atomic system may be used to amplify the evanescent waves that have been lost in the imaging by traditional lenses.And a slab fabricated by the left-handed atomic system may be an ideal candidate for designing perfect lenses.

quant-ph↗

arXiv2023

Ni-O-Ag catalyst enables 103-m$^2$ artificial photosynthesis with >16% solar-to-chemical energy conversion efficiency

Herein, NiO nanosheets supported with Ag single atoms are synthesized for photothermal CO2 hydrogenation to achieve 1065 mmol g$^{-1}$ h$^{-1}$ of CO production rate under 1 sun irradiation, revealing the unparalleled weak sunlight driven reverse water-gas shift reaction (RWGS) activity. This performance is attributed to the coupling effect of Ag-O-Ni sites to enhance the hydrogenation of CO$_2$ and weaken the CO adsorption, resulting in 1434 mmol g$^{-1}$ h$^{-1}$ of CO yield at 300$^\circ$ C, surpassing any low-temperature RWGS performances ever reported. Building on this, we integrated the 2D Ni$_1$Ag$_{0.02}$O$_1$ supported photothermal RWGS with commercial photovoltaic electrolytic water splitting, leading to the realization of 103 m$^2$ scale artificial photosynthesis system (CO$_2$+H$_2$$\to$CO+H$_2$O) with a daily CO yield of 18.70 m$^3$, a photochemical energy conversion efficiency of >16%, over 90% H$_2$ ultilization efficiency, outperforming other types of artificial photosynthesis. The results of this research chart a promising course for designing practical, natural sunlight-driven artificial photosynthesis systems and highly efficient platinum-free CO$_2$ hydrogenation catalysts. This work is a significant step towards harnessing solar energy more efficiently and sustainably, opening exciting possibilities for future research and development in this area.

cond-mat.mtrl-sci↗

arXiv2021

Noise-Response Analysis of Deep Neural Networks Quantifies Robustness and Fingerprints Structural Malware

The ubiquity of deep neural networks (DNNs), cloud-based training, and transfer learning is giving rise to a new cybersecurity frontier in which unsecure DNNs have `structural malware' (i.e., compromised weights and activation pathways). In particular, DNNs can be designed to have backdoors that allow an adversary to easily and reliably fool an image classifier by adding a pattern of pixels called a trigger. It is generally difficult to detect backdoors, and existing detection methods are computationally expensive and require extensive resources (e.g., access to the training data). Here, we propose a rapid feature-generation technique that quantifies the robustness of a DNN, `fingerprints' its nonlinearity, and allows us to detect backdoors (if present). Our approach involves studying how a DNN responds to noise-infused images with varying noise intensity, which we summarize with titration curves. We find that DNNs with backdoors are more sensitive to input noise and respond in a characteristic way that reveals the backdoor and where it leads (its `target'). Our empirical results demonstrate that we can accurately detect backdoors with high confidence orders-of-magnitude faster than existing approaches (seconds versus hours).

cs.LG↗