Spin-Controlled Helical Quantum Sieve Chiral Spectrometer.

This article reports on a molecular-spin-sensitive-antenna (MSSA) that is based on stacked layers of organically functionalized graphene on a fibrous helical cellulose network for carrying out spatiotemporal identification of chiral enantiomers. The MSSA structures combine three complementary features: (i) chiral separation via a helical quantum sieve for chiral trapping, (ii) chiral recognition by a synthetically implanted spin-sensitive center in a graphitic lattice; and (iii) chiral selectivity by a chirality-induced-spin mechanism that polarizes the local electronic band-structure in graphene through chiral-activated Rashba spin-orbit interaction field. Combining the MSSA structures with decision-making principles based on neuromorphic artificial intelligence shows fast, portable, and wearable spectrometry for the detection and classification of pure and a mixture of chiral molecules, such as butanol (S and R), limonene (S and R), and xylene isomers, with 95-98% accuracy. These results can have a broad impact where the MSSA approach is central as a precautionary risk assessment against potential hazards impacting human health and the environment due to chiral molecules; furthermore, it acts as a dynamic monitoring tool of all parts of the chiral molecule life cycles.

Non-Invasive Staging of In Vitro Mice Embryos by Means of Volatolomics.

Current methods for embryo selection are limited. This study assessed a novel method for the prediction of embryo developmental potential based on the analysis of volatile organic compounds (VOCs) emitted by embryo samples. The study included mice embryos monitored during the pre-implantation period. Four developmental stages of the embryos were tested, covering the period from 1 to 4 days after fecundation. In each stage, the VOCs released by the embryos were collected and examined by employing two different volatolomic techniques, gas-chromatography coupled to mass-spectrometry (GC-MS) and a nanoarray of chemical gas sensors. The GC-MS study revealed that the VOC patterns emanating from embryo samples had statistically different values at different stages of embryo development. The sensor nanoarray was capable of classifying the developmental stages of the embryos. The proposed volatolomics analysis approach for embryos presents a promising potential for predicting their developmental stage. In combination with conventional morphokinetic parameters, it could be effective as a predictive model for detecting metabolic shifts that affect embryo quality before preimplantation.