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Tailoring composition of polyoctylthiophene optodes, including hydrogen binding compound in the polymer phase, effectively diminishes the effect of pH change on sensitivity of proposed optodes, as shown on model examples of potassium and calcium sensors. Thus polyoctylthiophene based nanosensors show equally high sensitivity for analyte cations concentration change both in acidic (pH&nbsp;=&nbsp;4) and alkaline (pH&nbsp;=&nbsp;9.2) media. Protein function is modulated via different levels including their structure and chemical modifications, but method for monitoring of protein structures and their modifications on a large scale is far from mature. In this study, we present a shot-gun proteomic method, which combine ultrafiltration with limited tryptic proteolysis (FLiP) to enrich the surface accessible peptides and modification sites on protein structures. FLiP enable high throughput confirmation of the structural information of 1939 proteins. Based on this, we identified 120 types of modifications located on the surface accessible regions of proteins, including some rare modification types like phosphorylation of asparagine and acetylation of threonine. Our data provides a comprehensive picture of the spatial distribution of multiple modification types on protein structures. Collectively, our work provides a promising tool for large-scale confirmation of protein structures and unbiased discovery of multiple proteins surface assessable post-translational modifications, which may also benefits the study of rare modification types. This work designs the synthesis of a novel amino morpholine schiff base functionalized calix[4]arene cage (SCC), its deposition onto Quartz Crystal Microbalance (QCM) crystal surface, and usage for the selective detecting of formaldehyde (HCHO). The SCC modified QCM sensor has been characterized by contact angle measurements and microscopy images. Initial experiments revealed that the frequency response decreased significantly which means that there was a good interaction between the SCC molecules and HCHO. The proposed sensor exhibited a linear response towards HCHO in different concentrations ranging from 1.85 to 9.25&nbsp;ppm, having the high sensitivity (S) and low limit of detection (LOD) being 18.324&nbsp;Hz/ppm and 0.67&nbsp;ppm, respectively. Furthermore, the adsorption behavior and mechanism of HCHO onto the QCM sensor were investigated for this sensing system and the adsorption data exhibited a good correlation with the Freundlich and Langmuir-Freundlich adsorption models in terms of the regression coefficient. The QCM sensor showed outstanding selective performance to HCHO among %97 RH and some a number of interfering volatile organic compounds (VOCs) such as chloroform, dichloromethane, acetone, n-hexane, methanol, xylene, and ammonia. Thus, real-time, sensitive, selective and effective recognition of HCHO by the sensor can be explained some adsorption mechanisms such as size-fit concept, three-dimensional structures of molecules and interaction between moieties of the sensible film layer and analyte molecules such as hydrogen bonding interactions. A simple, fast and sensitive amperometric immunosensing method for the determination of the clinically relevant cytokine interferon gamma (IFN-γ) in saliva complying the requirements demanded for this kind of sample is reported. The target analyte was sandwiched between a specific capture antibody covalently immobilized on a screen-printed electrode functionalized by the diazonium salt grafting of p-aminobenzoic acid, and a biotinylated detector antibody labeled with a streptavidin-horseradish peroxidase conjugate. The amperometric responses measured at - 0.20&nbsp;V vs Ag pseudo-reference electrode upon addition of hydrogen peroxide in the presence of hydroquinone as the redox mediator allowed a calibration plot with a linear range between 2.5 and 2000&nbsp;pg&nbsp;mL-1 and a low limit of detection (1.6&nbsp;pg&nbsp;mL-1) to be obtained. In addition, a good selectivity against other non-target proteins was achieved. The developed method was validated by analyzing a WHO 1st International Standard for IFN-γ. In addition, the immunosensor was used for the determination of the endogenous IFN-γ in saliva with results in excellent agreement with those obtained by a commercial ELISA kit. Terrorism by means of explosives has become a crucial threat. [https://www.selleckchem.com/products/BKM-120.html check details] Nanoparticles with distinctive properties can offer novel aspects for instant detection of explosive materials. Common explosives are organic compounds that contain nitro group (NO2) along with carbon and hydrogen elements. This study demonstrates complete identification of nitramine explosives (RDX &amp; HMX) using colloidal silica nanoparticles. Sustainable fabrication of colloidal silica was conducted via hydrothermal processing technique. Explosive identification involves a digestion of the tested material using strong acid. The digestion process results in the development of nitro group and corresponding formaldehyde segment. The identification of the nitro group was performed using colloidal silica nanoparticles functionalized with secondary amine to develop a characteristic dark blue colour. Simultaneous identification of formaldehyde segment was performed using colloidal silica functionalized with aromatic phenol to develop a red colour. This robust explosive detection technology can find wide applications on site where instant identification to assess potential threat is a crucial demand. Thanks to hydrothermal processing, sustainable fabrication and surface modification of colloidal silica particles can be obtained. This work contributes to the improvement of glucose quantification using near-infrared (NIR), mid-infrared (MIR), and combination of NIR and MIR absorbance spectroscopy by classifying the spectral data prior to the application of regression models. Both manual and automated classification are presented based on three homogeneous classes defined following the clinical definition of the glycaemic ranges (hypoglycaemia, euglycaemia, and hyperglycaemia). For the manual classification, partial least squares and principal component regressions are applied to each class separately and shown to lead to improved quantification results compared to when applying the same regression models for the whole dataset. For the automatic classification, linear discriminant analysis coupled with principal component analysis is deployed, and regressions are applied to each class separately. The results obtained are shown to outperform those of regressions for the entire dataset.