As a typical steroid hormone drug, estradiol (E2) is also one of the most frequently detected endocrine disrupting chemicals (EDCs) in the aquatic environment. Herein, in response to the potential risk of E2 in steroid hormone pharmaceutical industry wastewater to human and wildlife, a novel carbon nanotubes / amine-functionalized Fe3O4 (CNTs/MNPs@NH2) nanocomposites with magnetic responsive have been developed for the enrichment and extraction of E2 in pharmaceutical industry wastewater, where amino-functionalized Fe3O4 magnetic nanoparticles (MNPs@NH2) were used as a magnetic source. The resultant CNTs/MNPs@NH2 possessed both the features of CNTs and desired magnetic property, enabling to rapidly recognize and separate E2 from pharmaceutical industry wastewater. Meanwhile, the CNTs/MNPs@NH2 had good binding behavior toward E2 with fast binding kinetics and high adsorption capacity, as well as exhibited satisfactory selectivity to steroidal estrogen compounds. Furthermore, the change of pH value of aqueous phase in adsorption solvent hardly affected the adsorption of E2 by CNTs/MNPs@NH2, and the adsorption capacity of E2 ranged from 19.9 to 17.2 mg g-1 in the pH range of 3.0 to 11.0, which is a latent advantage of the follow-up development method to detect E2 in pharmaceutical industry wastewater. As a result, the CNTs/MNPs@NH2 serving as a solid phase extraction medium were successfully applied to efficiently extract E2 from pharmaceutical industry wastewater. Therefore, the CNTs/MNPs@NH2 nanocomposites could be used as a potential adsorbent for removing steroidal estrogens from water. More importantly, the developed method would provide a promising solution for the monitoring and analysis of EDCs in pharmaceutical industry wastewater.Considering that neurotransmitters (NTs) and amino acids (AAs) exert pivotal roles in various neurological diseases, global detection of these endogenous metabolites is of great significance for the treatment of nervous system diseases. Herein, a workflow that could cope with various challenges was proposed to establish an extendable all-in-one injection liquid chromatography-tandem mass spectrometry (LC-MS/MS) assay for analyzing these small molecular metabolites with high coverage. To obtain a qualified blank biological matrix for the preparation of standard curves and quality control samples, different absorption solvents, including activated carbon (AC), calcite (Cal) and montmorillonite (Mnt) were systematically evaluated for efficient absorption of endogenous substances with minimum residue. We also firstly proposed a “Collision Energy Defect (CED)” strategy to solve the huge difference of mass signal strength caused by different properties and concentrations of 11 NTs and 17 AAs. The quantitative results were validated by LC-MS/MS. Sensitivity, accuracy, and recovery meeting generally accepted bioanalytic guidelines were observed in a concentration span of at least 100 to 500 times for each analyte. Then the temporal changes of intracerebral and peripheral NTs and AAs in ischemic stroke model and sham operated rats were successfully produced and compared using the described method. All these results suggested that the currently developed assay was powerful enough to simultaneously monitor a large panel of endogenous small molecule metabolites, which was expected to be widely used in the research of various diseases mediated by NTs and AAs.An analytical challenge that arises in environmental and food analysis is to quantify heterogeneous nanoparticles especially in polydisperse and complex samples. The method stated herein based on the reinjection asymmetrical flow field-flow fractionation (AF4 × AF4) coupled with inductively coupled plasma-mass spectrometer (ICP-MS) and statistical deconvolution allowed for identifying the molecular weight (Mw) and selenium abundance of the low Mw protein fractions (ca. less then 132 kDa) in an unknown and complex sample (e.g., selenium-rich soybean protein isolates (Se-SPI)). A non-linear decay crossflow program was also developed to get better resolution and shorter elution time for both low and high Mw components. The concept of the reinjection method was based on the excellent ability for reducing sample complexity regarding the size fractionation, and peak reproducibility under the identical conditions of AF4 system. The standard protein mixture was used as a proof-of-principle sample. The results showed the underlying peaks predicted by the reinjection method were agreed with the separation result using the standard mixture (the relative standard deviation of peak locations less then 1%), which indicated the reinjection method could provide an accurate assessment of the underlying peak number and location, and was promising to minimize the overfitting problem for statistic deconvolution. Interestingly, significant differences of Se abundance in protein fractions were observed in the low Mw range for Se-SPI, ranging from 0.28 to 1.66 cps/V with the Mw ranging from 13.75 kDa to 104.17 kDa, which indicated significant differences in the ability of binding Se for these selenium-rich proteins in Se-SPI.An important challenge in chromatography is the development of adequate separation methods. Accurate retention models can significantly simplify and expedite the development of adequate separation methods for complex mixtures. The purpose of this study was to introduce reinforcement learning to chromatographic method development, by training a double deep Q-learning algorithm to select optimal isocratic scouting runs to generate accurate retention models. These scouting runs were fit to the Neue-Kuss retention model, which was then used to predict retention factors both under isocratic and gradient conditions. Piperaquine molecular weight The quality of these predictions was compared to experimental data points, by computing a mean relative percentage error (MRPE) between the predicted and actual retention factors. By providing the reinforcement learning algorithm with a reward whenever the scouting runs led to accurate retention models and a penalty when the analysis time of a selected scouting run was too high (> 1h); it was hypothesized that the reinforcement learning algorithm should by time learn to select good scouting runs for compounds displaying a variety of characteristics.