Use of Hyper-Spectral Visible and Near-Infrared Satellite Data for Timely Estimates of the Earth’s Surface Reflectance in Cloudy and Aerosol Loaded Conditions: Part 1–Application to RGB Image Restoration Over Land With GOME-2

Space-based quantitative passive optical remote sensing of the Earth’s surface typically involves the detection and elimination of cloud-contaminated pixels as an initial processing step. We explore a fundamentally different approach; we use machine learning with cloud contaminated satellite hyper-spectral data to estimate underlying terrestrial surface reflectances at red, green, and blue (RGB) wavelengths. An artificial neural network (NN) reproduces land RGB reflectances with high fidelity, even in scenes with moderate to high cloud optical thicknesses. This implies that spectral features of the Earth’s surface can be detected and distinguished in the presence of clouds, even when they are partially and visibly obscured by clouds; the NN is able to separate the spectral fingerprint of the Earth’s surface from that of the clouds, aerosols, gaseous absorption, and Rayleigh scattering, provided that there are adequately different spectral features and that the clouds are not completely opaque. Once trained, the NN enables rapid estimates of RGB reflectances with little computational cost. Aside from the training data, there is no requirement of prior information regarding the land surface spectral reflectance, nor is there need for radiative transfer calculations. We test different wavelength windows and instrument configurations for reconstruction of surface reflectances. This work provides an initial example of a general approach that has many potential applications in land and ocean remote sensing as well as other practical uses such as in search and rescue, precision agriculture, and change detection.

Spectrofluorimetric Determination of Hydrochlorothiazide by a Carbon Dots-Based Probe via Inner Filtering Effect and Resonance Rayleigh Scattering

Synthesis of carbon dots (CDs) from natural resources not only enables green synthesis and production of environmentally friendly materials, but also provides a cost-effective probe as a fluorescence nanosensor. The proposed sensor introduces a unique one-pot hydrothermal CDs synthesis from alfalfa leaves, which is promising for sensing hydrochlorothiazide (HCTZ) via inner filter effect (IFE) and resonance Rayleigh scattering (RRS). The as-prepared CDs had wide emission spectra, excitation-dependent emission, high solubility, high stability, and visible fluorescence light with a quantum yield of up to 11%. The absorption of HCTZ overlapped with the excitation spectra of CDs. Therefore, CDs represented excellent quenching due to IFE when HCTZ was gradually added. Furthermore, this fluorescent sensor was successfully used to quantify HCTZ in the linear ranges (0.17-2.50 μg mL-1) with the limit of detection of 0.11 μg mL-1. The sensing system was simple as no surface functionalization was required for CDs, leading to less laborious steps and more cost-effective synthesis. The reaction time was short, i.e., less than 2 min, indicating a simple approach for rapid analysis of HCTZ. By optimizing conditions, successful measurements were carried out on pharmaceutical tablets.

Moiré Physics of One-Dimensional Related Systems and Their Measurement

Currently, the magic-angle graphene has given a tremendous boost to the study of unconventional superconductors. On the other hand, there were still limited experimental studies on superconductivity in one-dimensional (1D) carbon nanotube systems. The study of experimental systems in demonstrating superconductivity was therefore scientifically important. In this review, we have shown strategies toward demonstrating the superconductivity for the single double-wall carbon nanotube (DWCNT). In general, there have been two directions to analyse superconducting properties of one-dimensional materials: (i) strong correlated states (ii) anomalous electron transport operations. We introduced the transmission electron microscope (TEM) and Rayleigh scattering spectroscopy to describe the strong correlation. The theoretical foundations of moiré physics have also been described. Given all the methods, we concluded that the most intuitive way to demonstrate the superconductivity of single double-walled carbon nanotubes is the critical temperature. The sharp drop of the resistance could be directly observed, and the Tc could be obtained from the electrical transport data. In the last section, we also summarized the challenges that need to be addressed in future superconductivity studies of 1D carbon nanotubes.

Flaw Detection in Highly Scattering Materials Using a Simple Ultrasonic Sensor Employing Adaptive Template Matching

Ultrasonic sensors have been extensively used in the nondestructive testing of materials for flaw detection. For polycrystalline materials, however, due to the scattering nature of the material, which results in strong grain noise and attenuation of the ultrasonic signal, accurate detection of flaws is particularly difficult. In this paper, a novel flaw-detection method using a simple ultrasonic sensor is proposed by exploiting time-frequency features of an ultrasonic signal. Since grain scattering mostly happens in the Rayleigh scattering region, it is possible to separate grain-scattered noise from flaw echoes in the frequency domain employing their spectral difference. We start with the spectral modeling of grain noise and flaw echo, and how the two spectra evolve with time is established. Then, a time-adaptive spectrum model for flaw echo is proposed, which serves as a template for the flaw-detection procedure. Next, a specially designed similarity measure is proposed, based on which the similarity between the template spectrum and the spectrum of the signal at each time point is evaluated sequentially, producing a series of matching coefficients termed moving window spectrum similarity (MWSS). The time-delay information of flaws is directly indicated by the peaks of MWSSs. Finally, the performance of the proposed method is validated by both simulated and experimental signals, showing satisfactory accuracy and efficiency.

Time-resolved Rayleigh scattering measurements of methane clusters for laser-cluster fusion experiments

We present a time-resolved analysis of Rayleigh scattering measurements to determine the average size of methane clusters and find the optimum timing for laser-cluster fusion experiments. We measure Rayleigh scattering and determine the average size of methane clusters varying the backing pressure (P0) from 11 bar to 69 bar. Regarding the onset of clustering, we estimate that the average size of methane clusters at the onset of clustering is Nc0≅20 at 11 bar. According to our measurements, the average cluster radius r follows the power law of r∝P01.86. Our ion time-of-flight measurements indicate that we have produced energetic deuterium ions with kT = 52±2 keV after laser-cluster interaction using CD4 gas at 50 bar. We find that this ion temperature agrees with the predicted temperature from CD4 clusters at 50 bar with r = 14 nm assuming the Coulomb explosion model.

Raman Scattering-Based Biosensing: New Prospects and Opportunities

The growing interest in the development of new platforms for the application of Raman spectroscopy techniques in biosensor technologies is driven by the potential of these techniques in identifying chemical compounds, as well as structural and functional features of biomolecules. The effect of Raman scattering is a result of inelastic light scattering processes, which lead to the emission of scattered light with a different frequency associated with molecular vibrations of the identified molecule. Spontaneous Raman scattering is usually weak, resulting in complexities with the separation of weak inelastically scattered light and intense Rayleigh scattering. These limitations have led to the development of various techniques for enhancing Raman scattering, including resonance Raman spectroscopy (RRS) and nonlinear Raman spectroscopy (coherent anti-Stokes Raman spectroscopy and stimulated Raman spectroscopy). Furthermore, the discovery of the phenomenon of enhanced Raman scattering near metallic nanostructures gave impetus to the development of the surface-enhanced Raman spectroscopy (SERS) as well as its combination with resonance Raman spectroscopy and nonlinear Raman spectroscopic techniques. The combination of nonlinear and resonant optical effects with metal substrates or nanoparticles can be used to increase speed, spatial resolution, and signal amplification in Raman spectroscopy, making these techniques promising for the analysis and characterization of biological samples. This review provides the main provisions of the listed Raman techniques and the advantages and limitations present when applied to life sciences research. The recent advances in SERS and SERS-combined techniques are summarized, such as SERRS, SE-CARS, and SE-SRS for bioimaging and the biosensing of molecules, which form the basis for potential future applications of these techniques in biosensor technology. In addition, an overview is given of the main tools for success in the development of biosensors based on Raman spectroscopy techniques, which can be achieved by choosing one or a combination of the following approaches: (i) fabrication of a reproducible SERS substrate, (ii) synthesis of the SERS nanotag, and (iii) implementation of new platforms for on-site testing.