Showing papers by "Xihua Wang published in 2022"

Surface Microlenses for Much More Efficient Photodegradation in Water Treatment

Abstract: The global need for clean water requires sustainable technology for purifying contaminated water. Highly efficient solar-driven photodegradation is a sustainable strategy for wastewater treatment. In this work, we demonstrate that the photodegradation efficiency of micropollutants in water can be improved by ∼ 2-24 times by leveraging polymeric microlenses (MLs). These microlenses (MLs) are fabricated from the in-situ polymerization of surface nanodroplets. We found that photodegradation efficiency ( η ) in water correlates approximately linearly with the sum of the intensity from all focal points of MLs, although no difference in the photodegradation pathway is detected from the chemical analysis of the byproducts. With the same overall power over a given surface area, η is doubled by using ordered arrays, compared to heterogeneous MLs on an unpatterned substrate. Higher η from ML arrays may be attributed to a coupled effect from the focal points on the same plane that creates high local concentrations of active species to further speed up the rate of photodegradation. As a proof-of-concept for ML-enhanced water treatment, MLs were formed on the inner wall of glass bottles that were used as containers for water to be treated. Three representative micropollutants (norfloxacin, sulfadiazine, and sulfamethoxazole) in the bottles functionalized by MLs were photodegraded by 30% to 170% faster than in normal bottles. Our findings suggest that the ML-enhanced photodegradation may lead to a highly efficient solar water purification approach without a large solar collector size. Such an approach may be particularly suitable for portable transparent bottles in remote regions.

Multifunctional Nd2O3/CNFs Composite for Microwave Absorption and Anti-Corrosion Applications

Abstract: To improve the limitations of traditional microwave absorbing materials (MAMs), such as high density and narrow absorption bandwidth, neodymium oxide/carbon nanofiber (Nd2O3/CNF) composites were successfully prepared by electrospinning technology combined with a high-temperature carbonization process. The three-dimensional network structure of CNFs provides a path for conducting electromagnetic waves, and its porous structure also increases the multiple reflections of electromagnetic waves in the composite fibers, which significantly attenuates the energy of electromagnetic waves. Additionally rare-earth elements have a unique electronic arrangement on the 4f electron layer. The addition of Nd2O3 can turn the dielectric constant of Nd2O3/CNF and optimize the impedance matching. Also, Nd2O3 nanoparticles (Nd2O3–NPs) resulted in numerous of heterojunction surfaces and introduced the magnetic loss mechanism. Under the combined action of the double loss mechanism, the minimum reflection loss of Nd2O3/CNF composite fiber is −66.7 dB at 9.36 GHz, and the effective absorption bandwidth is 4.4 GHz (7.68–12.08 GHz). The radar cross section simulation results also prove that the composite fibers exhibit strong consumption of electromagnetic waves. Additionally, the excellent flexibility and anticorrosion properties of the composite fibers make them satisfy the multifunctional requirements of MAMs.

Heterogeneous Integration of Colloidal Quantum Dot Inks on Silicon Enables Highly Efficient and Stable Infrared Photodetectors

Abstract: Integrating lead sulfide (PbS) colloidal quantum dots (CQDs) with crystalline silicon (c-Si) has been proven to be an effective strategy in extending the sensitivity of Si-based photodetectors into infrared regime. Here, we demonstrate the successful integration of PbS CQD inks with Si and construct a highly efficient heterojunction infrared photodiode operating in the range from 800 up to 1500 nm. Thanks to the well-passivated Si surface by a two-step chlorination/methylation method and high-quality CQD inks, the heterojunction photodiode yields a low density of trap states, as validated by transient photovoltage and photocurrent measurements. With an insertion layer of a p-type CQD capped with 1,2-ethanedithiol ligands, the built-in electric field is much enhanced, leading to improved charge extractions. As a result, we have obtained an external quantum efficiency (EQE) of 44% at the excitonic wavelength of 1280 nm. The EQE values are maintained without detectable degradation through the course of more than 600 h, achieving superior device stability. In contrast to commercial solutions, which require high-temperature epitaxial deposition of germanium (Ge) or III–V compounds, the presented single-step spin-coating process of CQD inks also enables large-area integration on Si.

Doping Colloidal Quantum Dot Materials and Devices for Photovoltaics

Abstract: Colloidal semiconductor nanocrystals have generated tremendous interest because of their solution processability and robust tunability. Among such nanocrystals, the colloidal quantum dot (CQD) draws the most attention for its well-known quantum size effects. In the last decade, applications of CQDs have been booming in electronics and optoelectronics, especially in photovoltaics. Electronically doped semiconductors are critical in the fabrication of solar cells, because carefully designed band structures are able to promote efficient charge extraction. Unlike conventional semiconductors, diffusion and ion implantation technologies are not suitable for doping CQDs. Therefore, researchers have creatively developed alternative doping methods for CQD materials and devices. In order to provide a state-of-the-art summary and comprehensive understanding to this research community, we focused on various doping techniques and their applications for photovoltaics and demystify them from different perspectives. By analyzing two classes of CQDs, lead chalcogenide CQDs and perovskite CQDs, we compared different working scenarios of each technique, summarized the development in this field, and raised our own future perspectives.

Deep learning enabled inverse design of nanocrystal-based optical diffusers for efficient white LED lighting.

Abstract: Angular color uniformity and luminous flux are the most important figures of merit for a white-light-emitting diode (WLED), and simultaneous improvement of both figures of merit is desired. The cellulose-nanocrystal (CNC)-based optical diffuser has been applied on the WLED module to enhance angular color uniformity, but it inevitably causes the reduction of luminous flux. Here we demonstrate a deep-learning-based inverse design approach to design CNC-coated WLED modules. The developed forward neural network successfully predicts two figures of merit with high accuracy, and the inverse predicting model can rapidly design the structural parameters of CNC film. Further explorations taking advantage of both forward and inverse neutral networks can effectively construct the coating layer for WLED modules to reach the best performance.