Exploring electrospun nanofibers for physically unclonable functions: a scalable and robust method toward unique identifiers

Optical physical unclonable functions (PUFs) have great potentials in the security identification of Internet of Things. In this work, electrospun nanofibers are proposed as a candidate for a nanoscale, robust, stable and scalable PUF. The dark-field reflectance images of the polymer fibers are quantitatively analyzed by Hough transform. We find that the fiber length and orientation distribution reach an optimal point as the fiber density grows up over 850 in 400 x 400 pixels for a polyvinylpyrrolidone nanofiber based PUF device. Subsequently, we test the robustness and randomness of the PUF pattern by using the fiber amount as an encoding feature, generating a reconstruction success rate over 80% and simultaneously an entropy of 260 bits within a mean size of 4 cm2. A scale-invariant algorithm is adopted to identify the uniqueness of each pattern on a 256-sensor device. Furthermore, thermo-, moisture as well as photostability of the authentication process are systematically investigated by comparing polyacrylonitrile to polyvinylpyrrolidone system.

Detecting cadmium contamination in loessal soils using near-infrared spectroscopy in the Xiaoqinling gold area

Loess is an important soil type that is widespread in the Loess Plateau of northwest China. However, mining exploitation, beneficiation, and metallurgy have led to inorganic contamination of soils that threatens the health of residents. The regular absorption peak shift of near-infrared (NIR) spectra in loessal soils represents a new method of soil environmental assessment based on field reflectance spectroscopy and hyperspectral remote sensing. Specifically, the NIR features of loessal soil will shift in response to changes in the soil composition and microstructure induced by heavy metal pollution. This study collected 27 samples from notable regions in the study area. Mid-infrared (MIR) spectral analysis, NIR spectral analysis, modified seven-step Tessier sequential extraction, and X-ray diffraction were used to analyze the band shift phenomenon of MIR and NIR features. The alignment of NIR bands was determined via the correlation between NIR and MIR bands associated with the vibration variations of the hydroxyl group. The correlations established by NIR band positions and exchangeable Cd cations were also analyzed. The results were then discussed according to the mineralogical characteristics of the heavy metal cations adsorbed on the surface and interlayer sites of clay minerals. These results can be used as a reference for the application of NIR technology to detecting heavy metal contamination in the soil of mining regions.

High-Q dark hyperbolic phonon-polaritons in hexagonal boron nitride nanostructures

The anisotropy of hexagonal boron nitride (hBN) gives rise to hyperbolic phonon-polaritons (HPhPs), notable for their volumetric frequency-dependent propagation and strong confinement. For frustum (truncated nanocone) structures, theory predicts five, high-order HPhPs, sets, but only one set was observed previously with far-field reflectance and scattering-type scanning near-field optical microscopy. In contrast, the photothermal induced resonance (PTIR) technique has recently permitted sampling of the full HPhP dispersion and observing such elusive predicted modes; however, the mechanism underlying PTIR sensitivity to these weakly-scattering modes, while critical to their understanding, has not yet been clarified. Here, by comparing conventional contact- and newly developed tapping-mode PTIR, we show that the PTIR sensitivity to those weakly-scattering, high-Q (up to ≈280) modes is, contrary to a previous hypothesis, unrelated to the probe operation (contact or tapping) and is instead linked to PTIR ability to detect tip-launched dark, volumetrically-confined polaritons, rather than nanostructure-launched HPhPs modes observed by other techniques. Furthermore, we show that in contrast with plasmons and surface phonon-polaritons, whose Q-factors and optical cross-sections are typically degraded by the proximity of other nanostructures, the high-Q HPhP resonances are preserved even in high-density hBN frustum arrays, which is useful in sensing and quantum emission applications.

Estimation of Arsenic Content in Soil Based on Laboratory and Field Reflectance Spectroscopy

: In this study, in order to solve the difficulty of the inversion of soil arsenic (As) content using laboratory and field reflectance spectroscopy, we examined the transferability of the prediction method. Sixty-three soil samples from the Daye city area of the Jianghan Plain region of China were taken and studied in this research. The characteristic wavelengths of soil As content were then extracted from the full bands based on iteratively retaining informative variables (IRIV) coupled with Spearman’s rank correlation analysis (SCA). Firstly, the IRIV algorithm was used to roughly select the original spectral data. Gaussian filtering (GF), first derivative (FD) filtering, and gaussian filtering again (GFA) pretreatments were then used to improve the correlation between the spectra and soil As content. A subset with absolute correlation values greater than 0.6 was then retained as the optimal subset after each pretreatment. Finally, partial least squares regression (PLSR), Bayesian ridge regression (BRR), ridge regression (RR), kernel ridge regression (KRR), support vector machine regression (SVMR), eXtreme gradient boosting (XGBoost) regression, and random forest regression (RFR) models were used to estimate the soil As values using the different characteristic variables. The results showed that, compared with the traditional method based on IRIV, using the characteristic bands selected by the IRIV-SCA method can effectively improve the prediction accuracy of the models. For the laboratory spectra experiment stage, the six most representative characteristic bands were selected. The performance of IRIV-SCA-SVMR was found to be the best, with the coefficient of determination (R2), root-mean-square error (RMSE), and mean absolute error (MAE) in the validation set being 0.97, 0.22, and 0.11, respectively. For the field spectra experiment stage, the 12 most representative characteristic bands were selected. The performance of IRIV-SCA-XGBoost was found to be the best, with the R2, RMSE, and MAE in the validation set being 0.83, 0.35, and 0.29, respectively. The accuracy and stability of the inversion of soil As content are significantly improved by the use of the proposed method, and the method could be used to provide accurate data for decision support for the treatment and recovery of As pollution over a large area.