A review of recent progress in the application of Raman spectroscopy and SERS detection of microplastics and derivatives.

The global environmental concern surrounding microplastic (MP) pollution has raised alarms due to its potential health risks to animals, plants, and humans. Because of the complex structure and composition of microplastics (MPs), the detection methods are limited, resulting in restricted detection accuracy. Surface enhancement of Raman spectroscopy (SERS), a spectral technique, offers several advantages, such as high resolution and low detection limit. It has the potential to be extensively employed for sensitive detection and high-resolution imaging of microplastics. We have summarized the research conducted in recent years on the detection of microplastics using Raman and SERS. Here, we have reviewed qualitative and quantitative analyses of microplastics and their derivatives, as well as the latest progress, challenges, and potential applications.

Synthesis of nanoparticles via microfluidic devices and integrated applications.

In recent years, nanomaterials have attracted the research intervention of experts in the fields of catalysis, energy, biomedical testing, and biomedicine with their unrivaled optical, chemical, and biological properties. From basic metal and oxide nanoparticles to complex quantum dots and MOFs, the stable preparation of various nanomaterials has always been a struggle for researchers. Microfluidics, as a paradigm of microscale control, is a remarkable platform for online stable synthesis of nanomaterials with efficient mass and heat transfer in microreactors, flexible blending of reactants, and precise control of reaction conditions. We describe the process of microfluidic preparation of nanoparticles in the last 5 years in terms of microfluidic techniques and the methods of microfluidic manipulation of fluids. Then, the ability of microfluidics to prepare different nanomaterials, such as metals, oxides, quantum dots, and biopolymer nanoparticles, is presented. The effective synthesis of some nanomaterials with complex structures and the cases of nanomaterials prepared by microfluidics under extreme conditions (high temperature and pressure), the compatibility of microfluidics as a superior platform for the preparation of nanoparticles is demonstrated. Microfluidics has a potent integration capability to combine nanoparticle synthesis with real-time monitoring and online detection, which significantly improves the quality and production efficiency of nanoparticles, and also provides a high-quality ultra-clean platform for some bioassays.

[Application of stationary wavelet transformation to winter wheat SPAD hyperspectral monitoring].

By field trials, the canopy hyperspectral reflectance and chlorophyll content (SPAD) for winter wheat during 2010 and 2011 growth periods were measured by the ASD portable spectrometer and portable chlorophyll meter SPAD-502, respectively. The canopy spectral characteristics of different SPAD values were analyzed in different growth periods. The winter wheat SPAD estimation models based on normalized difference vegetation index (NDVI), ratio vegetation index (RVI) and wavelet energy coefficients were established in different growth periods. The results showed that green peak and red valley characteristics became more and more obvious with the increase of the SPAD. The SPAD estimation models based on NDVI performed better at the regreening stage, elongation stage, heading stage and filling stage with determination coefficients (R2) being 0.7957, 0.8096, 0.7557 and 0.5033, respectively. The winter wheat SPAD estimation models based on wavelet energy coefficients could greatly improve the SPAD estimation accuracy, with regression determination coefficients (R2) of the PVC estimation models based on high frequency energy coefficient and low frequency energy coefficient being 0.9168, 0.9154, 0.8802 and 0.9087 at the regreening stage, elongation stage, heading stage and filling stage, respectively.

Estimating daytime ecosystem respiration to improve estimates of gross primary production of a temperate forest.

Leaf respiration is an important component of carbon exchange in terrestrial ecosystems, and estimates of leaf respiration directly affect the accuracy of ecosystem carbon budgets. Leaf respiration is inhibited by light; therefore, gross primary production (GPP) will be overestimated if the reduction in leaf respiration by light is ignored. However, few studies have quantified GPP overestimation with respect to the degree of light inhibition in forest ecosystems. To determine the effect of light inhibition of leaf respiration on GPP estimation, we assessed the variation in leaf respiration of seedlings of the dominant tree species in an old mixed temperate forest with different photosynthetically active radiation levels using the Laisk method. Canopy respiration was estimated by combining the effect of light inhibition on leaf respiration of these species with within-canopy radiation. Leaf respiration decreased exponentially with an increase in light intensity. Canopy respiration and GPP were overestimated by approximately 20.4% and 4.6%, respectively, when leaf respiration reduction in light was ignored compared with the values obtained when light inhibition of leaf respiration was considered. This study indicates that accurate estimates of daytime ecosystem respiration are needed for the accurate evaluation of carbon budgets in temperate forests. In addition, this study provides a valuable approach to accurately estimate GPP by considering leaf respiration reduction in light in other ecosystems.

[Application of ANFIS in in-situ measured hyperspectral data for vegetation chlorophyll content estimation].

Hyperspectral reflectance and green degree of Platanus orientalis L. and Populus tomentosa Carr. leaves were measured by the ASD portable spectrometer and the portable chlorophyll meter SPAD-502, respectively. The chlorophyll concentration retrieval models based on 10 common vegetation indexes were established, and the ANN-BP model which used wave bands with larger correlation coefficient as input variables was established for chlorophyll content estimation. Finally, the ANFIS model was established to inverse vegetation chlorophyll content using hyperspectral data. The results showed that normalized difference vegetation index can inverse chlorophyll content better than other vegetation index, and the determination coefficients R2 of models of Platanus orientalis L. and Populus tomentosa Carr. were 0.795 7 and 0.754 6, respectively. The determination coefficients R2 between the predicted and the measured chlorophyll content based on ANN-BP models of Platanus orientalis L. and Populus tomentosa Carr. were 0.935 2 and 0.917 1, respectively. ANFIS model which is a good method to be applied to hyperspectral data for estimation of vegetation chlorophyll concentration can greatly improve vegetation chlorophyll concentration estimation accuracy, and the determination coefficients R2 between the predicted and the measured chlorophyll content of Platanus orientalis L. and Populus tomentosa Carr. were 0.935 2 and 0.917 1, respectively.