Self-assembly of 3D fennel-like Co3O4 with thirty-six surfaces for high performance supercapacitor

Three-dimensional (3D) fennel-like cobalt oxide (II,III) (Co3O4) particles with thirty-six surfaces on nickel foams were prepared via a simple hydrothermal synthesis method and its growth process was also researched. The crystalline structure and morphology were investigated by X-ray diffraction (XRD), scanning electron microscopy (SEM), and Raman spectroscopy. The Brunauer-Emmett Teller (BET) analysis revealed that 3D fennel-like Co3O4 particles have high specific surface area. Therefore, the special structure with thirty-six surfaces indicates the good electrochemical performance of the micron-nanometer material as electrode material for supercapacitors. The cyclic voltammetry (CV), galvanostatic charge-discharge, and electrochemical impedance spectroscopy (EIS) were conducted to evaluate the electrochemical performances. Compared with other morphological materials of the similar sizes, the Co3O4 particles on nickel foam exhibit a high specific capacitance of 384.375 F.g(-1) at the current density of 3A.g(-1) and excellent cycling stability of a capacitance retention of 96.54% after 1500 galvanostatic charge-discharge cycles in 6M potassium hydroxide (KOH) electrolyte

Hierarchical Black Silicon and Their Applications

Black silicon is expected to be a promising material for photoelectric, photothermic, photocatalytic, and microfluidic applications due to its remarkably anti-reflectivity, anti-bacterial effect, hydrophilicity, and hydrophobicity. These properties are attributed to the light trapping and surface tension interaction abilities of black silicon surface structures such as pores, pillars, cones, needles, and wires. Conventional black silicon materials mainly have nanotextures with high aspect ratios and structural density. Nanotextures can be achieved through a series of fabrication approaches, including metal-assisted chemical etching, electrochemical etching, and reactive ion etching (RIE). However, few studies have focused on the surface texturing methodology of black silicon through microstructures. Anti-reflection is the most critical factor defining the performance of black silicon in optical, photothermic, photochemical, and optoelectronic devices. The properties of these silicon-based devices under visible light illumination are commonly tuned through surface texturing, while their performance at wavelengths higher than 1100 nm requires either intrinsic lattice modification or enhancement by the addition of other materials. Although a few studies have proposed combining micro and nanostructures, their research has focused on suppressing light reflection at wavelengths lower than 1100 nm. Unfortunately, the anti-reflectance of black silicon in the near-infrared (NIR) range (over 1100 nm) is still weak due to silicon’s intrinsic bandgap of 1.12 eV. Fortunately, recent advances in microfabrication and material engineering have enabled the in-depth exploration of the synergy between surface texturing and material reinforcement. Therefore, building upon innovative fabrication approaches that enable novel black silicon with multi-scale surface structures and the investigation of their optical properties to create new silicon substrate materials for the next generation of photovoltaic, photodetector, and microfluidic devices is the motivation of this work. This Ph.D. work focuses on the following challenges: (1) Development of novel black silicon surface structure designs and relevant fabrication technologies. (2) Investigation and improvement of the optical properties of novel black silicon through the synergistic effect of surface texture and material reinforcement with localized surface plasmon resonance-inducing Au nanoparticles. (3) Exploration of the potential applications of the as-fabricated black silicon materials

Fabrication of Needle-Like Silicon Nanowires by Using a Nanoparticles-Assisted Bosch Process for Both High Hydrophobicity and Anti-Reflection

In this work, a modified Bosch etching process is developed to create silicon nanowires. Au nanoparticles (NPs) formed by magnetron sputtering film deposition and thermal annealing were employed as the hard mask to achieve controllable density and high aspect ratios. Such silicon nanowire exhibits the excellent anti-reflection ability of a reflectance value of below 2% within a broad light wave range between 220 and 1100 nm. In addition, Au NPs-induced surface plasmons significantly enhance the near-unity anti-reflection characteristics, achieving a reflectance below 3% within the wavelength range of 220 to 2600 nm. Furthermore, the nanowire array exhibits super-hydrophobic behavior with a contact angle over ~165.6° without enforcing any hydrophobic chemical treatment. Such behavior yields in water droplets bouncing off the surface many times. These properties render this silicon nanowire attractive for applications such as photothermal, photocatalysis, supercapacitor, and microfluidics

Effect of L-Ascorbic Acid Solution Concentration on the Thermoelectric Properties of Silver Selenide Flexible Films Prepared by Vacuum-Assisted Filtration

Currently, there are several thermoelectric materials, such as Ag2Te, Bi2Te3, and Sb2Te3, that have been investigated for thermoelectric applications. However, the toxicity and rarity of most of these materials make them unsuitable for practical applications. In contrast, silver selenide (Ag2Se) is an abundant and environment-friendly thermoelectric material. This study provides a facile synthetic approach for preparing high-performance, low-cost, and flexible Ag2Se thermoelectric films. Ag2Se nanomaterials were prepared based on the chemical template method, and the reaction solution concentration was varied to systematically investigate the effects of reaction solution concentration on the characterization and thermoelectric properties of Ag2Se nanomaterials. For convenience of testing, the flexible Ag2Se films were prepared on porous nylon membranes using vacuum-assisted filtration. The prepared thermoelectric films were tested using an X-ray diffractometer, scanning electron microscope, Seebeck coefficient tester, and Hall tester. The film prepared from the solution with the lowest concentration (18.0 mM) demonstrated the best thermoelectric performance, with a maximum power factor of 382.18 μW∙m−1∙K−2 at ~400 K. Additionally, a cold-pressing treatment could effectively enhance the electrical conductivity of the film, without damaging the substrate, as the conductivity of the film remained at 90% of the original value after 1500 bending cycles

Enhancement of the Transmission Performance of Piezoelectric Micromachined Ultrasound Transducers by Vibration Mode Optimization

Ultrasound is widely used in industry and the agricultural, biomedical, military, and other fields. As key components in ultrasonic applications, the characteristic parameters of ultrasonic transducers fundamentally determine the performance of ultrasonic systems. High-frequency ultrasonic transducers are small in size and require high precision, which puts forward higher requirements for sensor design, material selection, and processing methods. In this paper, a three-dimensional model of a high-frequency piezoelectric micromachined ultrasonic transducer (PMUT) is established based on the finite element method (FEM). This 3D model consists of a substrate, a silicon device layer, and a molybdenum-aluminum nitride-molybdenum (Mo-AlN-Mo) sandwich piezoelectric layer. The effect of the shape of the transducer’s vibrating membrane on the transmission performance was studied. Through a discussion of the parametric scanning of the key dimensions of the diaphragms of the three structures, it was concluded that the fundamental resonance frequency of the hexagonal diaphragm was higher than that of the circle and the square under the same size. Compared with the circular diaphragm, the sensitivity of the square diaphragm increased by 8.5%, and the sensitivity of the hexagonal diaphragm increased by 10.7%. The maximum emission sound-pressure level of the hexagonal diaphragm was 6.6 times higher than that of the circular diaphragm. The finite element results show that the hexagonal diaphragm design has great advantages for improving the transmission performance of the high-frequency PMUT

MTA, an RNA m6A Methyltransferase, Enhances Drought Tolerance by Regulating the Development of Trichomes and Roots in Poplar

N6-methyladenosine (m6A) is the most prevalent internal modification present in the mRNAs of all higher eukaryotes, where it is present within both coding and noncoding regions. In mammals, methylation requires the catalysis of a multicomponent m6A methyltransferase complex. Proposed biological functions for m6A modification include pre-mRNA splicing, RNA stability, cell fate regulation, and embryonic development. However, few studies have been conducted on m6A modification in trees. In particular, the regulation mechanism of RNA m6A in Populus development remains to be further elucidated. Here, we show that PtrMTA (Populus trichocarpa methyltransferase) was colocalized with PtrFIP37 in the nucleus. Importantly, the PtrMTA-overexpressing plants significantly increased the density of trichomes and exhibited a more developed root system than that of wild-type controls. Moreover, we found that PtrMTA-overexpressing plants had better tolerance to drought stress. We also found PtrMTA was a component of the m6A methyltransferase complex, which participated in the formation of m6A methylation in poplar. Taken together, these results demonstrate that PtrMTA is involved in drought resistance by affecting the development of trichomes and roots, which will provide new clues for the study of RNA m6A modification and expand our understanding of the epigenetic molecular mechanism in woody plants

A Hardware System for Synchronous Processing of Multiple Marine Dynamics MEMS Sensors

Temperature, depth, conductivity, and turbulence are fundamental parameters of marine dynamics in the field of ocean science. These closely correlated parameters require time-synchronized observations to provide feedback on marine environmental problems, which requires using sensors with synchronized power supply, multi-path data solving, recording, and storage performances. To address this challenge, this work proposes a hardware system capable of synchronously processing temperature, depth, conductivity, and turbulence data on marine dynamics collected by sensors. The proposed system uses constant voltage sources to excite temperature and turbulence sensors, a constant current source to drive a depth sensor, and an alternating current (AC) constant voltage source to drive a conductivity sensor. In addition, the proposed system uses a high-precision analog-digital converter to acquire the direct current (DC) signals from temperature, depth, and turbulence sensors, as well as the AC signals from conductivity sensors. Since the sampling frequency of turbulence sensors is different from that of the other sensors, the proposed system stores the generated data at different storage rates as multiple-files. Further, the proposed hardware system manages these files through a file system (file allocation tab) to reduce the data parsing difficulty. The proposed sensing and hardware logic system is verified and compared with the standard conductivity-temperature-depth measurement system in the National Center of Ocean Standards and Metrology. The results indicate that the proposed system achieved National Verification Level II Standard. In addition, the proposed system has a temperature indication error smaller than 0.02 °C, a conductivity error less than 0.073 mS/cm, and a pressure error lower than 0.8‰ FS. The turbulence sensor shows good response and consistency. Therefore, for observation methods based on a single point, single line, and single profile, it is necessary to study multi-parameter data synchronous acquisition and processing in the time and spatial domains to collect fundamental physical quantities of temperature, salt, depth, and turbulence. The four basic physical parameters collected by the proposed system are beneficial to the in-depth research on physical ocean motion, heat transfer, energy transfer, mass transfer, and heat-energy-mass coupling and can help to realize accurate simulation, inversion, and prediction of ocean phenomena

Enhancement of the Transmission Performance of Piezoelectric Micromachined Ultrasound Transducers by Vibration Mode Optimization

Ultrasound is widely used in industry and the agricultural, biomedical, military, and other fields. As key components in ultrasonic applications, the characteristic parameters of ultrasonic transducers fundamentally determine the performance of ultrasonic systems. High-frequency ultrasonic transducers are small in size and require high precision, which puts forward higher requirements for sensor design, material selection, and processing methods. In this paper, a three-dimensional model of a high-frequency piezoelectric micromachined ultrasonic transducer (PMUT) is established based on the finite element method (FEM). This 3D model consists of a substrate, a silicon device layer, and a molybdenum-aluminum nitride-molybdenum (Mo-AlN-Mo) sandwich piezoelectric layer. The effect of the shape of the transducer’s vibrating membrane on the transmission performance was studied. Through a discussion of the parametric scanning of the key dimensions of the diaphragms of the three structures, it was concluded that the fundamental resonance frequency of the hexagonal diaphragm was higher than that of the circle and the square under the same size. Compared with the circular diaphragm, the sensitivity of the square diaphragm increased by 8.5%, and the sensitivity of the hexagonal diaphragm increased by 10.7%. The maximum emission sound-pressure level of the hexagonal diaphragm was 6.6 times higher than that of the circular diaphragm. The finite element results show that the hexagonal diaphragm design has great advantages for improving the transmission performance of the high-frequency PMUT

Robust Meteorological Drought Prediction Using Antecedent SST Fluctuations and Machine Learning

While reliable drought prediction is fundamental for drought mitigation and water resources management, it is still a challenge to develop robust drought prediction models due to complex local hydro-climatic conditions and various predictors. Sea surface temperature (SST) is considered as the fundamental predictor to develop drought prediction models. However, traditional models usually extract SST signals from one or several specific sea zones within a given time span, which limits full use of SST signals for drought prediction. Here, we introduce a new meteorological drought prediction approach by using the antecedent SST fluctuation pattern (ASFP) and machine learning techniques (e.g., support vector regression (SVR), random forest (RF), and extreme learning machine (ELM)). Three models (i.e., ASFP-SVR, ASFP-ELM, and ASFP-RF) are developed for ensemble, probability, and deterministic drought predictions. The Colorado, Danube, Orange, and Pearl River basins with frequent droughts over different continents are selected, as the cases, where standardized precipitation evapotranspiration index (SPEI) are predicted at the 1° × 1° resolution with 1- and 3-month lead times. Results show that the ASFP-ELM model can effectively predict space-time evolutions of drought events with satisfactory skills, outperforming the ASFP-SVR and ASFP-RF models. Our study has potential to provide a reliable tool for drought prediction, which further supports the development of drought early warning systems