Covalent organic frameworks with tunable porous crystallinity and outstanding stability have recently exhibited fascinating pretreatment performance as solid-phase microextraction coatings. In this report, a β-ketoenamine-linked covalent organic framework (TpPa-1) was successfully constructed through a Schiff-base-type reaction between 1,3,5-triformylphloroglucinol (Tp) and para-phenylenediamine (Pa-1). A TpPa-1 coating was then fabricated on a stainless-steel fiber for capturing trace synthetic musks. This TpPa-1 coating exhibited strong interaction with synthetic musks because of its hydrophobicity and π-π affinity. This TpPa-1-based solid-phase microextraction methodology, coupled with gas chromatography-tandem mass spectrometry, provided high enrichment factors (1214-12 487), wide linearity (0.5-1000 ng L-1), low limits of detection (0.04-0.31 ng L-1), and acceptable reproducibility (relative standard deviation, less then 10%) for nine synthetic musks. Recoveries at three spiked levels in three types of water samples were between 76.2% and 118.7%. selleckchem These results indicated the promising applicability of the TpPa-1 as a solid-phase microextraction fiber coating for reliably detecting trace concentrations of synthetic musks in the environment.The past 30 years have seen increasing availability of methods and equipment using thermal desorption for the measurement of airborne pollutants. These methods offer greater sensitivity than methods using solvent desorption; are more amenable to automation; and, are better suited to mass spectrometry (MS)-based detection. The greater sensitivity offered by thermal desorption means it is well suited to the analysis of samples collected through diffusive sampling with the additional benefits that this offers. This Technical Brief informs both analysts and less technically aware users of the capabilities and limitations of thermal desorption equipment and measurement methods.We present a method for the detection of luteinizing hormone (LH) in buffalo urine by using gold nanoparticles (AuNPs) conjugated with novel anti-peptide antibodies against LH (anti LHP) in lateral flow assay format. Buffalo LH is an important reproductive hormone and is a chemically complex glycoprotein. Its surge release precedes ovulation and therefore detecting LH has implications in identifying the ovulation event. Any sensor thus developed for sensing LH may have the potential for predicting ovulation and hence can assist herd managers in making decisions on the timing of artificial insemination. Recombinant LH production is time consuming, difficult and costly. Hence, we identified an epitope peptide sequence in buffalo LH and raised antibodies against it. The chemically synthesized peptide and antibodies were used for developing the sensor. The gold nanoparticles and conjugates were characterized through physicochemical methods which confirmed the binding of peptides and antibodies to the gold nanoparticles. A qualitative ELISA for sensing LH was developed based on competitive binding of gold nanoparticles conjugated with the epitope peptide and LH towards the anti-peptide antibodies against LH. We also further explored the detection of LH in buffalo urine using the gold nanoparticle-LHP conjugate (AuNP-LHP) in dipstick format. These experiments provided a proof-of-concept towards applicability of the LH based sensor for ovulation prediction in buffaloes.The level of d-amino acids (DAAs) is significantly related to bacterial contamination in health and disease, food science and nutrition, and industrial applications. Most sensing methods for the detection of DAAs need expensive equipment, and skilled operation experience, making the test kit for DAA analysis much more complex and challenging. Toward this end, we exploited a label-free DAA test kit based on 1,4-benzenediboronic acid (BDBA)-induced gold nanoparticles (AuNPs) aggregations. DAAs were first catalyzed by their specific catalase (DAAO, d-amino acid oxidase) and oxidized to produce H2O2. Then, the produced H2O2 inhibited the citrate-capped AuNPs aggregation under BDBA, while the unreacted BDBA could lead to AuNPs aggregation. As a result, the UV/vis absorption spectra and optical photographs of the AuNPs solution are changed in the presence of different DAA target amounts. This method not only can be used for qualitative and semi-quantitative analysis with a naked-eye readout, but also it can quantify DAAs in aqueous solutions with high sensitivity and specificity. Furthermore, the DAAs test kit provided an effective and selective quantification of DAAs with excellent precision and accuracy in bacterial samples. We believe this DAA test kit provides the potential to be further developed for DAA detection for satisfying both lab and practical needs in different fields.A de-waxing protocol that successfully removes paraffin from tissue microarray (TMA) cores of fixed tissue obtained from oral cancer is described. The success of the protocol is demonstrated by the comparison of Fourier transform infrared (FTIR) results obtained on paraffin-embedded and de-waxed tissue and the absence of any significant correlations between infrared scanning near-field optical microscopy (SNOM) images of de-waxed tissue obtained at the three main paraffin IR peaks. The success of the protocol in removing paraffin from tissue is also demonstrated by images obtained with scanning electron microscopy (SEM) and by energy dispersive spectra (EDS) of a de-waxed CaF2 disc which shows no significant contribution from carbon. The FTIR spectra of the de-waxed TMA core overlaps that obtained from OE19 oesophageal cancer cells which had never been exposed to paraffin.Wheat is susceptible to contamination by deoxynivalenol (DON) which is regarded as a class III carcinogen. In this paper, a rapid and nondestructive method for DON content determination and contamination degree discrimination in wheat was developed by using a multispectral imaging (405-970 nm) system. Genetic algorithm (GA) and principal component analysis (PCA), as preprocessing methods, were used to obtain the best spectral characteristics. The determination model was established by combining preprocessing methods and chemometric methods including partial least squares (PLS), support vector machines (SVM) and back propagation neural network (BPNN). The best quantitative determination result was obtained based on GA-SVM with a correlation coefficient of prediction (Rp), root mean square error of prediction (RMSEP) and residual predictive deviation (RPD) of 0.9988, 365.3 μg kg-1 and 8.6, respectively. Furthermore, the accuracy of contamination degree classification was up to 94.29% in the prediction set by using the PCA-PLS model.