PEO/LAGP hybrid solid polymer electrolytes for ambient temperature lithium batteries by solvent-free, “one pot” preparation

Here, we report hybrid solid polymer electrolytes (HSPE) obtained by rapid, truly solvent-free, thus scalable preparation process. HSPE composition is very simple: a LiTFSI added poly(ethylene oxide) (PEO) polymer matrix encompassing NASICON-type Li1.5Al0.5Ge1.5(PO4)3 (LAGP) super Li+ ion conducting ceramic. Homogeneous, self-standing, mechanically robust solid electrolyte films are obtained by simply mixing in “one pot” and hot pressing the solid mixture of dry powders at moderate temperature. Noteworthy, unlike several other super ionic conductors used for composite electrolytes, LAGP is relatively stable in air atmosphere and can be processed in a dry-room, which is more favorable, cheap and scalable than Ar-filled dry glove box for industrial fabrication of safe lithium batteries. The proper, homogeneous mixing of LAGP powder, PEO and LiTFSI leads to HSPE with interesting electrochemical behavior in lab-scale lithium cells, especially under high current regimes, and even at ambient temperature. HSPE-based cells outperform the PEO-LiTFSI-based counterpart, in terms of specific capacity output (about 70% of the theoretical value retained at very high 2C rate), limited fading and excellent Coulombic efficiency (>99.5%) even at low rate. Interfacial stability issues remain to be solved, chiefly linked to the reactivity of LAGP in contact with lithium metal, but results here proposed represent a step further toward truly all-solid-state batteries conceived for high energy/power technologies, assuring safety and performance in a wide range of operating conditions

Determination of the geographical origin of green coffee beans using NIR spectroscopy and multivariate data analysis

In this work, near infrared (NIR) spectroscopy and multivariate data analysis were investigated as a fast and non disruptive method to classify green coffee beans on continents and countries bases. FT-NIR spectra of 191 coffee samples, origin from 2 continents and 9 countries, were acquired by two different laboratories. Laboratory-independent Partial Least Square-Discriminant Analysis and interval PIS-DA models were developed by following a hierarchical approach, i.e. considering at first the continent and then the country of origin as discrimination rule. The best continent-based classification model was able to identify correctly more than 98% in prediction, whereas 100% of them were correctly predicted by the best country-based classification model. The inter-laboratory reliability of the proposed method was confirmed by McNemar test, since no significant differences (P > 0.05) were found. Furthermore, a validation was performed predicting the spectral test set of a laboratory using the model developed by the other one

SERS-active metal-dielectric nanostructures integrated in microfluidic devices for ultra-sensitive label-free miRNA detection

In this work, silver decorated porous silicon membranes integrated in a polydimethylsiloxane multi-chamber microfluidic chip were functionalized with DNA-probes and used for the detection of miRNA by Surface-enhanced Raman Scattering analysis. An innovative biological protocol has been designed: the probe was divided in two short pieces that interact before and after the miRNA incubation. The optofluidic biosensor was applied for the label-free detection of miRNA sequences at in vivo concentrations

Monitoring Chemical Changes of Coffee Beans During Roasting Using Real-time NIR Spectroscopy and Chemometrics

Variations occurring in coffee beans during roasting are ascribable to several chemical-physical phenomena: to quickly track the whole process and to ensure its reproducibility, a process analytical technology (PAT) approach is needed. In this study, a method combining in-line Fourier transform near-infrared (FT-NIR) spectroscopy and chemometric modelling was investigated to get real-time and practical knowledge about the roasting effects on coffee’s chemical-physical composition. In-line spectra were acquired by inserting a NIR probe into a laboratory coffee roaster, running twenty-four roasting experiments, planned spanning different coffee species (Arabica and Robusta), four roasting temperature settings (TS1–TS4) and times (650–1580 s). Multivariate curve resolution-alternate least squares (MCR-ALS) was used to model the chemical-physical changes occurring during the roasting process, and information about maximum rate, acceleration and deceleration of the process was obtained, also highlighting potential effects due to the different roasting temperatures and coffee varieties. The proposed approach provides the groundwork for direct real-time implementation of rapid, non-invasive automated monitoring of the roasting process at industrial scale

Role of probe design and bioassay configuration in surface enhanced Raman scattering based biosensors for miRNA detection

The accurate design of labelled oligo probes for the detection of miRNA biomarkers by Surface Enhanced Raman Scattering (SERS) may improve the exploitation of the plasmonic enhancement. This work, thus, critically investigates the role of probe labelling configuration on the performance of SERS-based bioassays for miRNA quantitation. To this aim, highly efficient SERS substrates based on Ag-decorated porous silicon/PDMS membranes are functionalized according to bioassays relying on a one-step or two-step hybridization of the target miRNA with DNA probes. Then, the detection configuration is varied to evaluate the impact of different Raman reporters and their labelling position along the oligo sequence on bioassay sensitivity. At high miRNA concentration (100-10 nM), a significantly increased SERS intensity is detected when the reporters are located closer to the plasmonic surface compared to farther probe labelling positions. Counterintuitively, a levelling-off of the SERS intensity from the different configurations is recorded at low miRNA concentration. Such effect is attributed to the increased relative contribution of Raman hot-spots to the whole SERS signal, in line with the electric near field distribution simulated for a simplified model of the Ag nanostructures. However, the beneficial effect of reducing the reporter-to-surface distance is partially retained for a two-step hybridization assay thanks to the less sterically hindered environment in which the second hybridization occurs. The study thus demonstrates an improvement of the detection limit of the two-step assay by tuning the probe labelling position, but sheds at the same time light on the multiple factors affecting the sensitivity of SERS-based bioassays

Chemometric Differentiation of Sole and Plaice Fish Fillets Using Three Near-Infrared Instruments

Fish species substitution is one of the most common forms of fraud all over the world, as fish identification can be very challenging for both consumers and experienced inspectors in the case of fish sold as fillets. The difficulties in distinguishing among different species may generate a “grey area” in which mislabelling can occur. Thus, the development of fast and reliable tools able to detect such frauds in the field is of crucial importance. In this study, we focused on the distinction between two flatfish species largely available on the market, namely the Guinean sole (Synaptura cadenati) and European plaice (Pleuronectes platessa), which are very similar looking. Fifty fillets of each species were analysed using three near-infrared (NIR) instruments: the handheld SCiO (Consumer Physics), the portable MicroNIR (VIAVI), and the benchtop MPA (Bruker). PLS-DA classification models were built using the spectral datasets, and all three instruments provided very good results, showing high accuracy: 94.1% for the SCiO and MicroNIR portable instruments, and 90.1% for the MPA benchtop spectrometer. The good classification results of the approach combining NIR spectroscopy, and simple chemometric classification methods suggest great applicability directly in the context of real-world marketplaces, as well as in official control plans