High-Performance Flexible Broadband Photoelectrochemical Photodetector Based on Molybdenum Telluride.

Flexible broadband photodetectors are desired but challenging to be fabricated for next-generation wearable intelligent optoelectronic devices. Considering the narrow bandgap and strong light absorption, molybdenum telluride (MoTe2) based photoelectrochemical photodetectors are successfully assembled by liquid phase exfoliation accompanied with the electrophoretic deposited method. This MoTe2-based photodetector shows a broadband detection in ultraviolet-near-infrared band, long-term stability within 18000 s, and fast response in millisecond-level (response time≈19 ms, recovery time≈26 ms). More importantly, even though the MoTe2 photodetector is bent and twisted at a high degree for several hundred times, it still shows excellent flexibility with stable on-off switching characteristics. Additionally, this photodetector displays a good response for rotation angles in the range from 0° to 360°, and the extracted Iph maintain almost the same value approximately 0.97 µA cm-2, suggesting an omnidirectional detection capability. This work demonstrates the proposed flexible photoanode shows a great potential in future broadband omnidirectional detection systems.

Optimization of Mach-Zehnder interferometer of high-spectral-resolution lidar for large-scale wind measurement.

The optimization design of a quadri-channel Mach-Zehnder interferometer (QMZI) of the high-spectral-resolution lidar is presented for the large-scale wind measurement. The optimized QMZI can discriminate the Doppler frequency shift generated by atmospheric wind from aerosol Mie scattering echo signals and molecular Rayleigh scattering echo signals, and then the wind information can be retrieved. The optimal optical path differences (OPDs) of QMZI are determined by theoretical and simulation analysis. The wind measurement simulation experiments prove that the designed QMZI can measure the large-scale wind with an accuracy of meter level.

Orthogonal retrieval algorithm of atmospheric temperature profiles from pure rotational Raman lidar signals.

Aimed at the stability of calibration coefficients in a general non-orthogonal retrieval algorithm (NRA) of pure rotational Raman lidars (PRRLs), an orthogonal retrieval algorithm (ORA) of atmospheric temperature profiles based on the orthogonal basis function is proposed. This algorithm eliminates the correlation between the calibration coefficients in the NRA to reduce the influence of the number of calibration points and the selection scheme on the calibration coefficients. In this paper, the stabilities of calibration coefficients in the NRA and ORA are compared and analyzed, and the data analysis for atmospheric temperature profiles with a time resolution of minute-level are given, based on the developed Cloud Precipitation Potential Evaluation (CPPV) lidar data and the parallel radiosonde temperature data. The analysis results show that coefficients of variation (CVs) of ORA calibration coefficients are one order of magnitude smaller than those of NRA coefficients. The mean deviation of the ORA retrieval results is roughly reduced by 16.1% compared with the NRA, and the root-mean-square deviation is roughly reduced by 15.0% compared with the NRA. Therefore, the temperature retrieval performance of the ORA is better than that of the NRA.

Power-modulated integrated path differential absorption lidar for probing benzene concentration.

Aimed at the regional open-path detection of benzene (C 6 H 6) in the atmosphere, a power-modulated integrated path differential absorption (PM-IPDA) lidar is introduced and demonstrated. Two tunable interband cascade lasers (ICLs) with about 3.2 µm wavelength are utilized to generate the required PM optical signal. These two operation central wavelengths (CWs) of the PM-IPDA lidar are, respectively, 3236.6 and 3187.1 nm, which can mitigate the influence of significant gases such as H 2 O, C H 4, and HCl on the detection performance. In this work, the fast Fourier transform algorithm is used to retrieve the measured values with the time resolution of 0.1 s corresponding to 104 sampling bins at the sampling rate of 100 kSps/s. The modulated frequency of the PM-IPDA lidar is selected as 10 kHz by laboratory experiments. The slow fluctuation characteristic of the benzene absorption spectrum within the vicinity region of 3.2 µm reduces the impact of small wavelength fluctuations on the performance of PM-IPDA lidar, although a scheme modulated only the driving current causes wavelength fluctuations of ∼±0.2n m. These laboratory experiments also indicate the PM-IPDA lidar can reduce the error resulting from 1/f noise. Open-path observation experiments show that the detection limit is about 0.60m g⋅m -3 and that the PM-IPDA lidar can be used for the regional open-path real-time detection of benzene.

Optimized method for simultaneous detection of fog/aerosol particle size distribution.

We propose forward/lateral scattering of dual-wavelength (ultraviolet and short-wave near-infrared bands) radiation to simultaneously detect aerosol particles and fog droplet size distribution in an open atmosphere. The size distributions were described using a gamma distribution. A light-scattering detection system was optimized and designed, and the final wavelengths and scattering angles of ∼ 350 nm and ∼ 1100 nm, and 1°, 2°, 12°, and 35°, respectively, were selected. Numerical simulation analyses and measurements were performed for the proposed detection scheme. The results confirmed that the proposed method is feasible and can rapidly acquire the fog droplet spectrum and aerosol particle size spectrum distribution in an open environment. The system structure of the method is simple and easy to implement, with high detection results and accuracy.

Identification method of raindrops and hailstones based on digital holographic interference.

The identification of raindrops and hailstones is of great significance to the study of precipitation characteristics from the aspect of microphysics and can provide important data support for weather modification. In this paper, an identification method of raindrops and hailstones based on digital holographic interference is proposed. The grayscale gradient variance method is used to obtain the focus position of the particles. By means of binarization and morphological processing, digital holograms are processed to obtain clear profiles of the particles. Then the contour parameters of the particles are used to obtain the equivalent volume diameter and roundness. Finally, according to the equivalent volume diameter, roundness and lens-like effect of the particles, the phase states of the raindrop and hailstone are identified by the algorithm. Experiments show that the method proposed in this paper has a good identification effect on raindrops and hailstones. The research results can provide reference for the research of the identification method of raindrops and hailstones and the acquisition of accurate characteristic parameters.

Correction method for temperature measurements inside clouds using rotational Raman lidar.

Rotational Raman lidar is an important technique for detecting atmospheric temperature. However, in cloud regions with strong elastic scattering conditions, elastic scattering crosstalk (ESC) is prevalent due to insufficient out-of-band suppression of the optical filter, resulting significant deviations in temperature retrieval. To address this challenge, a temperature correction technique for optically-thin clouds based on the backscatter ratio is proposed. Using the least-squares method, a temperature correction function is formulated based on the relationship between the ESC and backscatter ratio of clouds. Subsequently, the backscatter ratio is used to correct the rotational Raman ratio of clouds, thereby obtaining the vertical distribution of atmospheric temperature within the cloud layer. The feasibility of this method was assessed through numerical simulations and experimentally validated using a temperature and aerosol detection lidar at the Xi'an University of Technology (XUT). The results indicate that the difference between the retrieved temperature profile under high signal-to-noise ratio conditions and radiosonde data is less than 1.5 K. This correction technique enables atmospheric temperature measurements under elastic scattering conditions with a backscatter ratio less than 115, advancing research on atmospheric structure and cloud microphysics.

Lateral scanning Raman scattering lidar for accurate measurement of atmospheric temperature and water vapor from ground to height of interest: publisher's note.

This publisher's note contains corrections to Opt. Lett.48, 2595 (2023).10.1364/OL.488924.

Lateral scanning Raman scattering lidar for accurate measurement of atmospheric temperature and water vapor from ground to height of interest.

A novel lateral scanning Raman scattering lidar (LSRSL) system is proposed, aiming to realize the accurate measurement of atmospheric temperature and water vapor from the ground to a height of interest and to overcome the effect of a geometrical overlap function of backward Raman scattering lidar. A configuration of the bistatic lidar is employed in the design of the LSRSL system, in which four horizontally aligned telescopes mounted on a steerable frame to construct the lateral receiving system are spatially separated to look at a vertical laser beam at a certain distance. Each telescope, combined with a narrowband interference filter, is utilized to detect the lateral scattering signals of the low- and high-quantum-number transitions of the pure rotational Raman scattering spectra and vibrational Raman scattering spectra of N2 and H2O. The profiling of lidar returns in the LSRSL system is performed by the elevation angle scanning of the lateral receiving system, in which the intensities of the lateral Raman scattering signals at each setting of elevation angles are sampled and analyzed. Preliminary experiments are carried out after the construction of a LSRSL system in Xi'an city, whose retrieval results and statistical error analyses present a good performance in the detection of atmospheric temperature and water vapor from the ground to a height of 1.11 km and show the feasibility for combination with backward Raman scattering lidar in atmospheric measurement.

Error simulation of atmospheric scattered radiance and its influence on slant visibility based on the SBDART model and Monte Carlo method.

Atmospheric scattered radiance is an important factor affecting slant visibility measurement in the daytime. This paper explores atmospheric scattered radiance errors and their influence on slant visibility measurements. Considering the difficulty in error synthesis of the radiative transfer equation, an error simulation scheme based on the Monte Carlo method is proposed. An error simulation and error analysis for atmospheric scattered radiance was carried out based on the Santa Barbara DISTORT atmospheric radiative transfer (SBDART) model and the Monte Carlo method. The error in aerosol parameters including the single-scattering albedo (SSA), the asymmetry factor, and the aerosol optical depth (AOD), was simulated by a random number and random error under different normal distributions, and the error influence of aerosol parameters on the error in the solar irradiance and 33-layer atmosphere scattered radiance is discussed in detail. The maximum relative deviations of the output scattered radiance at a certain slant direction are 5.98%, 1.47%, and 2.35%, when SSA, the asymmetry factor, and the AOD obey the normal distribution of (0, 5). The error sensitivity analysis also confirms that the SSA is the most sensitive factor affecting atmospheric scattered radiance and the total solar irradiance. Then, according to the error synthesis theory, we investigated the error transfer effect of three error sources related to the atmosphere based on the contrast ratio between the object and the background. The simulation results show that the error in the contrast ratio caused by solar irradiance and scattered radiance is lower than 6.2% and 2.84%, indicating the main role in contributing to the error transfer of slant visibility. Further, the comprehensive process of the error transfer in slant visibility measurements was demonstrated by a set of lidar experiments and the SBDART model. The results provide a reliable theoretical basis for the measurement of atmospheric scattered radiance and slant visibility, which is of great significance to improve the measurement accuracy of slant visibility.

Investigation of a Raman scattering spectral model for seawater containing a composite salt solute.

To satisfy the demand for active remote sensing of ocean salinity, this paper proposes a Raman spectra, salinity, and temperature model for seawater. Seawater is a solution containing a composite salt solute, changes in the solute, temperature, and salinity of seawater can affect the intensity of Raman spectra. It is difficult to directly analyze the influence of various factors on the Raman spectra of seawater. Therefore, the Raman spectra of solutions containing a single solute and mixed solutions were detected, and the effect of solutions containing different solutes on the spectra was analyzed. The experimental results revealed the variation in the low- and high-frequency spectral intensities of the Raman spectra with salinity and temperature. The Raman spectra of seawater were modeled as a function of temperature and salinity using the low- and high-frequency area ratios, and the spectra of seawater at different salinities were obtained; the model calculation results are consistent with the experimental results within the entire range of seawater temperature and salinity. Because the Raman spectra were a function of temperature and salinity. To achieve high precision remote sensing of ocean salinity, it is necessary to use Brillouin scattering for remote sensing of ocean temperature.

Temperature measurement of cloud or haze layers based on Raman rotational and vibrational spectra.

Pure rotational Raman lidar is often used for atmospheric temperature profile measurements. However, high elastic scattering suppression ratios (>107) are required for temperature measurement in clouds and haze, which imposes stringent requirements on spectral separation techniques. To solve this problem, a lidar measurement technique based on vibrational and rotational Raman spectra is proposed. Using nitrogen vibrational and rotational Raman scattering to obtain temperature profiles under strong elastic scattering, combined with the dual-rotational Raman temperature measurements under weak elastic scattering, a vertical distribution of atmospheric temperature including cloud and haze layers, can be obtained. The feasibility of the method was verified by numerical simulation. The Raman lidar for temperature measurements was established in Xi'an University of Technology, and the obtained temperature results show good agreement with the radiosonde measurements. The proposed method combines the high sensitivity of the dual-rotational Raman method and the high Mie-scattering suppression of the vibrational Raman method, thus further improving the adaptability of Raman lidar to cloudy and hazy air conditions and supporting atmospheric and cloud physics research.

Retrieval of the planetary boundary layer height from lidar measurements by a deep-learning method based on the wavelet covariance transform.

Understanding and characterization of the planetary boundary layer (PBL) are of great importance in terms of air pollution management, weather forecasting, modelling of climate change, etc. Although many lidar-based approaches have been proposed for the retrieval of the PBL height (PBLH) in case studies, development of a robust lidar-based algorithm without human intervention is still of great challenging. In this work, we have demonstrated a novel deep-learning method based on the wavelet covariance transform (WCT) for the PBLH evaluation from atmospheric lidar measurements. Lidar profiles are evaluated according to the WCT with a series of dilation values from 200 m to 505 m to generate 2-dimensional wavelet images. A large number of wavelet images and the corresponding PBLH-labelled images are created as the training set for a convolutional neural network (CNN), which is implemented based on a modified VGG16 (VGG - Visual Geometry Group) convolutional neural network. Wavelet images obtained from lidar profiles have also been prepared as the test set to investigate the performance of the CNN. The PBLH is finally retrieved by evaluating the predicted PBLH-labelled image and the wavelet coefficients. Comparison studies with radiosonde data and the Micro-Pulse-Lidar Network (MPLNET) PBLH product have successfully validated the promising performance of the deep-learning method for the PBLH retrieval in practical atmospheric sensing.

Visible, near-infrared dual-polarization lidar based on polarization cameras: system design, evaluation and atmospheric measurements.

A visible, near-infrared (VIS-NIR) dual-polarization lidar technique employing laser diodes and polarization cameras has been designed and implemented for all-day unattended field measurements of atmospheric aerosols. The linear volume depolarization ratios (LVDR) and the offset angles can be retrieved from four-directional polarized backscattering signals at wavelengths of 458 nm and 808 nm without additional optical components and sophisticated system adjustments. Evaluations on the polarization crosstalk of the polarization camera and the offset angle have been performed in detail. A rotating linear polarizer (RLP) method based on the Stokes-Mueller formalism has been proposed and demonstrated for measuring extinction ratios of the polarization camera, which can be used to eliminate the polarization crosstalk between different polarization signals. The offset angles can be online measured with a precision of 0.1°, leading to negligible measurement errors on the LVDR. One-month statistical analysis revealed a small temporal variation of the offset angles, namely -0.13°±0.07° at 458 nm and 0.33°±0.09° at 808 nm, indicating good system stability for long-term measurement. Atmospheric measurements have been carried out to verify the system performance and investigate aerosol optical properties. The spectral characteristics of the aerosol extinction coefficient, the color ratio, the linear particle polarization ratio (LPDR) and the ratio of LPDR were retrieved and evaluated based on one-month continuous atmospheric measurements, from which different types of aerosols can be classified. The promising results showed great potential of employing the VIS-NIR dual-polarization lidar in characterizing aerosol optical properties, discriminating aerosol types and analyzing long-range aerosol transportation.

Investigation of cloud droplets velocity extraction based on depth expansion and self-fusion of reconstructed hologram.

The velocity of cloud droplets has a significant effect on the investigation of the turbulence-cloud microphysics interaction mechanism. The paper proposes an in-line digital holographic interferometry (DHI) technique based on depth expansion and self-fusion algorithm to simultaneously extract particle velocity from eight holograms. In comparison to the two-frame exposure method, the extraction efficiency of velocity is raised by threefold, and the number of reference particles used for particle registration is increased to eight. The experimental results obtained in the cloud chamber show that the velocity of cloud droplets increases fourfold from the stabilization phase to the dissipation phase. The measurement deviations of two phases are 1.138 and 1.153 mm/s, respectively. Additionally, this method provides a rapid solution for three-dimensional particle velocimetry investigation of turbulent field stacking and cloud droplets collisions.

Atmospheric visibility prediction by using the DBN deep learning model and principal component analysis.

Measuring and predicting atmospheric visibility is important scientific research that has practical significance for urban air pollution control and public transport safety. We propose a deep learning model that uses principal component analysis and a deep belief network (DBN) to effectively predict atmospheric visibility in short- and long-term sequences. First, using a visibility meter, particle spectrometer, and ground meteorological station data from 2016 to 2019, the principal component analysis method was adopted to determine the influence of atmospheric meteorological and environmental parameters on atmospheric visibility, and an input dataset applicable to atmospheric visibility prediction was constructed. On the basis of deep belief network theory, network structure parameters, including data preprocessing, the number of hidden layers, the number of nodes, and activation and weight functions, are simulated and analyzed. A deep belief network model suitable for atmospheric visibility prediction is established, where a double hidden layer is adopted with the node numbers 70 and 50, and the Z-score method is used for normalization processing with the tanh activation function and Adam optimizer. The average accuracy of atmospheric visibility prediction by the deep belief network reached 0.84, and the coefficient of determination reached 0.96; these results are significantly superior to those of the back propagation (BP) neural network and convolutional neural network (CNN), thus verifying the feasibility and effectiveness of the established deep belief network for predicting atmospheric visibility. Finally, a deep belief network model based on time series is used to predict the short- and long-term trends of atmospheric visibility. The results show that the model has good visibility prediction results within 3 days and has an accuracy rate of 0.79. Covering the visibility change evaluations of different weather conditions, the model demonstrates good practicability. The established deep learning network model provides an effective and feasible technical solution for the prediction of atmospheric meteorology and environmental parameters, which enjoys a wide range of application prospects in highway transportation, navigation, sea and air, meteorology, and environmental research.

Measurement method for slant visibility with slant path scattered radiance correction by lidar and the SBDART model.

Different from the existing methods for estimating averaged slant visibility by lidar and the traditional Koschmieder visibility formula, a measurement method for slant visibility is fundamentally proposed in this paper that considers the correction of slant path scattered radiance. Lidar is adopted to provide aerosol parameters, including optical depth and scattering parameters, and the SBDART (Santa Barbara DISORT Atmospheric Radiative Transfer) model is used to solve the radiative transfer equation to obtain the corresponding radiance distribution; thus, the corrected apparent brightness contrast between the object and background along the slant path is used to achieve accurate slant visibility. Based on the measurement principle of slant visibility, a theoretical simulation and an analysis of the slant path scattered radiance are performed, and the resulting slant visibility is studied in detail in this paper.

Development of an all-day portable polarization lidar system based on the division-of-focal-plane scheme for atmospheric polarization measurements.

A portable polarization lidar system based on the division-of-focal-plane scheme has been proposed for all-day accurate retrieval of the atmospheric depolarization ratio. The polarization lidar system has been designed as a T-shaped architecture consisting of a closed transmitter and a detachable large focal receiver, which is capable of outdoor unmanned measurements. The lidar system features low cost, low maintenance and short blind range (∼100 m) by utilizing a 450 nm multimode laser diode as the light source and a polarization image sensor with four polarized channels as the detector. Validation measurements have been carried out on a near horizontal path in ten consecutive days. The linear volume depolarization ratio (LVDR) as well as its measurement uncertainty has been theoretically and experimentally evaluated without employing additional optical components and sophisticated online calibrations. The offset angle can also be accurately retrieved (i.e., -0.06°) from the four-directional polarized lidar profiles with a standard deviation of ±0.02° during the whole measurement period, which contributes negligible influence on the retrieval of the LVDR. It has been found out that the uncertainty of the LVDR was mainly originated from the random noise, which was below 0.004 at nighttime and may reach up to 0.008 during daytime owing to the increasing sunlight background. The performance of the polarization lidar system has been further examined through atmospheric vertical measurements. The low-cost low-maintenance portable polarization lidar system, capable of detecting four-directional polarized lidar signals simultaneously, opens up many possibilities for all-day field measurements of dust, cloud, urban aerosol, oriented particles, etc.

Detection of atmospheric temperature by using polarization high-spectral-resolution lidar.

In order to achieve high signal-to-noise ratio by using small laser energy and telescope aperture, we present a polarization filter in high-spectral-resolution lidar (HSRL) for the measurement of atmospheric temperature. Compared with the filter method in a traditional HSRL in which the intensity of the return signal is split into the different transmission channel of a discriminator, the advantage of this filter system is that the intensity of the return signal is fully utilized for each discriminator channel, and the return signal changes the polarization state of the light without loss of intensity when it is incident on the two Rayleigh channels. In addition, the daytime detection capability of HSRL is improved by using a polarization optical scheme to suppress the solar background light. The advantages of the polarization filter are proven by the theoretical calculations using the Stokes vector and a Mueller matrix. In detection experiments of atmospheric temperature, the detection height is 4 km at night and 2.5 km during the day by using the pulsed energy of 50 mJ and telescope diameter of 250 mm. The results are in good agreement with the data detected by radiosonde.

Observation of vertical wind profiling with lidar based on correction of sensitivity.

A high spectral resolution lidar (HSRL) for simultaneously detecting vertical wind, temperature, and the backscattering ratio in the troposphere is developed. The atmospheric temperature and vertical wind are determined by the Rayleigh scattering spectrum width and Mie scattering spectrum Doppler shift, respectively. The influence of temperature and the backscattering ratio on vertical wind measurement accuracy is also analyzed. The temperature and backscattering ratio affect the wind measurement, which produces the vertical wind offset. A correction considering the effects of the method is conducted considering real-time and on-site temperature profiles and the backscattering ratio to correct wind measurement sensitivity. Measurements of HSRL taken under different weather conditions (fine and hazy days) are demonstrated. Good agreement between the HSRL and the radiosonde measurements was obtained considering lapse rates and temperature inversions. The maximum temperature offsets were 1.3 and 4 K at a height of 1.5 km on fine and hazy days, respectively. Then, real-time and on-site temperature profiles and backscattering ratios were applied to correct the real-time and on-site wind. The corrected wind profiles showed satisfactory agreement with the wind profiles acquired from the calibrated wind lidar. The maximum detection offsets of the retrieved wind speed were reduced from 1 m/s to 0.55 m/s and from 1 m/s to 0.21 m/s, respectively, which were decreases of 0.45 and 0.79 m/s in fine and hazy days after correction of sensitivity. It is evident that the corrected wind method can reduce the influence of temperature and the backscattering ratio on the wind measurement and the offset of vertical wind. The reliability of the method is also proven.