Spray‐Layer‐by‐Layer Carbon Nanotube/Electrospun Fiber Electrodes for Flexible Chemiresistive Sensor Applications

Development of a versatile method for incorporating conductive materials into textiles could enable advances in wearable electronics and smart textiles. One area of critical importance is the detection of chemicals in the environment for security and industrial process monitoring. Here, the fabrication of a flexible, sensor material based on functionalized multi‐walled carbon nanotube (MWNT) films on a porous electrospun fiber mat for real‐time detection of a nerve agent simulant is reported. The material is constructed by layer‐by‐layer (LbL) assembly of MWNTs with opposite charges, creating multilayer films of MWNTs without binder. The vacuum‐assisted spray‐LbL process enables conformal coatings of nanostructured MWNT films on individual electrospun fibers throughout the bulk of the mat with controlled loading and electrical conductivity. A thiourea‐based receptor is covalently attached to the primary amine groups on the MWNT films to enhance the sensing response to dimethyl methylphosphonate (DMMP), a simulant for sarin nerve agent. Chemiresistive sensors based on the engineered textiles display reversible responses and detection limits for DMMP as low as 10 ppb in the aqueous phase and 5 ppm in the vapor phase. This fabrication technique provides a versatile and easily scalable strategy for incorporating conformal MWNT films into three‐dimensional substrates for numerous applications.     

Adsorption of3He on cesium surfaces

Vapor pressure isotherms of³He on Cs are reported which show that, in contrast to⁴He,³He weis Cs for 1.26K<T<2K. Although the isotherms are distinctly different from those taken on strong binding substrates, we have observed no evidence for a first order prewetting transition. The data suggest that the prewetting critical point is substantially below T=1.26K.     

Independent components analysis applied to mid-infrared spectra of edible oils to study the thermal stability of heated oils

In this study, independent components analysis (ICA) is tested as a mathematical method to extract characteristic signals from a data set of mid-infrared spectra acquired during accelerated oxidation of vegetable oils (corn oil, sunflower oil and soya oil) at different temperatures. These characteristic signals highlight the modifications that occur in the oils and facilitate their interpretation. ICA clearly showed that although the nature of the chemical reactions (such as oxidation and cis–trans isomerisation) induced by the heating are the same for all the types of analysed samples (corn oil, sunflower oil and soya oil), their kinetics are different depending on the oil composition. ICA also showed that higher temperatures reduce the induction times of the oxidation reaction and accelerate the degradation of the oils.     

Headspace solid-phase microextraction for wine volatile analysis

The most commonly used technique to prepare samples for the analysis of wine volatile is the headspace solid-phase microextraction (HS-SPME). This method has gained popularity in last few years, as it is a unique solventless preparation technique. In this paper, a summary of recently published studies using HS-SPME for the analysis of wine aromas, with special emphasis on the method developed, has been compiled. Several papers are discussed in detail, mainly with respect to the SPME conditions used. A brief summary of the reviews related to HS-SPME analysis is given and discussed. Several parameters affecting the HS-SPME, such as the salt concentration and the agitation conditions, are used in the same way as used in several papers. The HS-SPME extraction proved to be sufficiently sensitive to satisfy legislative requirements related to low detection and quantification limits as well as method accuracy and precision requirements. However, in order to achieve the best performance and precision, the protocol needs to be optimized for each case. The effect of different parameters must be well characterized to ensure correct extraction and desorption to ensure the transfer of extracted compounds into the analytical system. The operating parameters, such as time, temperature, and agitation, must then be kept constant for all the samples.     

Detection of Blackcurrant Adulteration by Aronia Berry Using High Resolution Mass Spectrometry,Variable Selection and Combined PLS Regression Models

Aronia berry has a deep red colour, and as it is cheaper than blackcurrant, it may be used to adulterate blackcurrant juice and drinks made therefrom. The aim of the study was to produce a model using multivariate tools to quantify the level of adulteration of blackcurrant concentrates with aronia berry concentrates. Samples of various blends between the two fruits were analysed after dilution on an ultra high pressure liquid chromatography-time of flight mass spectrometry (UPLC-TOF-MS) to create partial least squares (PLS) models. Moreover, variable selection was investigated with the recursive weighted PLS (rPLS) method to increase PLS model quality, and different levels of fusion between data from the two ionisation modes were also compared. The best model was obtained from fusion of ionisation modes with variable selection using the rPLS method. The limit of detection of 5 % aronia berry concentrate in blackcurrant concentrate was achievable with ±1 % error on the adulteration level.