Effects of surface nanotexturing on the wickability of microtextured metal surfaces.

HYPOTHESIS: Achieving spontaneous, rapid, and long-distance liquid transport is crucial for many practical applications such as phase change heat transfer and reactions at solid-liquid interfaces. Surface nanotexturing has been widely reported to improve the wickability of microtextured metal surfaces. Although surface nanotextures show high capillary pressure, the high fluid flow resistance through nanotextures prevents fluid transport. The underlying mechanisms responsible for the enhanced wickability of nanotextured surfaces are still unclear. EXPERIMENTS: Herein, we prepared a variety of microtextures and nanotextures on copper surfaces by femtosecond laser micromachining and chemical oxidation, respectively. The wickability of these textured surfaces was quantitively compared by measuring wicking coefficient and capillary rise speed. We designed experiments to eliminate any possible effects of surface oxidation and metal composition on wickability. A theoretical model describing the vertical and horizontal capillary flow in V-shaped microgrooves was proposed and utilized to analyze the experimental results. The effects of time-dependent wettability on wickability were also examined. FINDINGS: Surface nanotexturing can enhance surface wettability while altering the micrometer-scale structural characteristics. The greatly enhanced wickability of nanotextured surfaces can only be observed when the surface microtextures have a very small aspect ratio. Otherwise, for metal surfaces with fine microgrooves, the latter effect is more pronounced, and thus the surface wickability may deteriorate after preparing surface nanotextures; for surfaces with wide microgrooves, both effects are minimal, and the surface wickability enhances only marginally after surface nanotexturing. Furthermore, the wickability of microtextured surfaces will decay rapidly due to the adsorption of airborne organics, whereas adding surface nanotextures can significantly inhibit this degradation. The anti-contamination capability of surface nanotextures is considered likely to be a potential mechanism responsible for the greatly enhanced wickability of nanotextured surfaces noted in some studies.

A universal approach to recover the original superhydrophilicity of micro/nano-textured metal or metal oxide surfaces.

Micro/nano-textured metal or metal oxide surfaces that are naturally superhydrophilic will spontaneously transform into hydrophobic even superhydrophobic after being exposed to ambient air due to the adsorption of airborne organics. This fast wettability transition not only affects the true evaluation of surface wettability but also deteriorates the application performance. Albeit the mechanisms responsible for the wettability transition have been clarified, there is no universal method to recover the initial superhydrophilicity, and how the surface morphology affects the wettability transition is still unclear. Herein, we observe and compare the wettability transition of a wide variety of micro/nano-textured metal or metal oxide surfaces and propose a solvent cleaning method to recover their original superhydrophilicity. We prove that the spontaneously adsorbed organics can be removed by our proposed cleaning method while maintaining the original surface morphology and composition. Our proposed cleaning method is valid for both micro/nano-textured metal and metal oxide surfaces. We also prove that the rate of the wettability transition is not primarily affected by the specific area of surface structures but by the closeness of structural arrangement. Densely packed surface nanostructures can significantly delay the wettability transition by suppressing the diffusion of organic molecules. Our results help the true evaluation of surface wettability and provide a route for the design and preparation of long-lasting superhydrophilic surfaces.

[Selection of Characteristic Wavelengths Using SPA and Qualitative Discrimination of Mildew Degree of Corn Kernels Based on SVM].

The feasibility of Fourier transform near infrared (FT-NIR) spectroscopy with spectral range between 833 and 2 500 nm to detect the moldy corn kernels with different levels of mildew was verified in this paper. Firstly, to avoid the influence of noise, moving average smoothing was used for spectral data preprocessing after four common pretreatment methods were compared. Then to improve the prediction performance of the model, SPXY (sample set partitioning based on joint x-y distance) was selected and used for sample set partition. Furthermore, in order to reduce the dimensions of the original spectral data, successive projection algorithm (SPA) was adopted and ultimately 7 characteristic wavelengths were extracted, the characteristic wave-lengths were 833, 927, 1 208, 1 337, 1 454, 1 861, 2 280 nm. The experimental results showed when the spectrum data of the 7 characteristic wavelengths were taken as the input of SVM, the radial basic function (RBF) used as the kernel function, and kernel parameter C = 7 760 469, γ = 0.017 003, the classification accuracies of the established SVM model were 97.78% and 93.33% for the training and testing sets respectively. In addition, the independent validation set was selected in the same standard, and used to verify the model. At last, the classification accuracy of 91.11% for the independent validation set was achieved. The result indicated that it is feasible to identify and classify different degree of moldy corn grain kernels using SPA and SVM, and characteristic wavelengths selected by SPA in this paper also lay a foundation for the online NIR detection of mildew corn kernels.