| Title: | Nanoscale Events on Cyanobiphenyl-Based Self-Assembled Droplets Triggered by Gas Analytes |
| Author(s): | Ramou E; Palma S; Roque ACA; |
| Address: | "Associate Laboratory i4HB horizontal line Institute for Health and Bioeconomy, School of Science and Technology, NOVA University Lisbon, 2829-516 Caparica, Portugal. UCIBIO horizontal line Applied Molecular Biosciences Unit, Department of Chemistry, School of Science and Technology, NOVA University Lisbon, 2829-516 Caparica, Portugal" |
| Journal Title: | ACS Appl Mater Interfaces |
| ISSN/ISBN: | 1944-8252 (Electronic) 1944-8244 (Print) 1944-8244 (Linking) |
| Abstract: | "Liquid crystals (LCs) are prime examples of dynamic supramolecular soft materials. Their autonomous self-assembly at the nanoscale level and the further nanoscale events that give rise to unique stimuli-responsive properties have been exploited for sensing purposes. One of the key features to employ LCs as sensing materials derives from the fine-tuning between stability and dynamics. This challenging task was addressed in this work by studying the effect of the alkyl chain length of cyanobiphenyl LCs on the molecular self-assembled compartments organized in the presence of ionic liquid molecules and gelatin. The resulting multicompartment nematic and smectic gels were further used as volatile organic compound chemical sensors. The LC structures undergo a dynamic sequence of phase transitions, depending on the nature of the LC component, yielding a variety of optical signals, which serve as optical fingerprints. In particular, the materials incorporating smectic compartments resulted in unexpected and rich optical textures that have not been reported previously. Their sensing capability was tested in an in-house-assembled electronic nose and further assessed via signal collection and machine-learning algorithms based on support vector machines, which classified 12 different gas analytes with high accuracy scores. Our work expands the knowledge on controlling LC self-assembly to yield fast and autonomous accurate chemical-sensing systems based on the combination of complex nanoscale sensing events with artificial intelligence tools" |
| Keywords: | electronic nose ionic liquid liquid crystal machine learning nanoscale self-assembly; |
| Notes: | "PubMed-not-MEDLINERamou, Efthymia Palma, Susana I C J Roque, Ana Cecilia A eng 639123/ERC_/European Research Council/International 2022/01/20 ACS Appl Mater Interfaces. 2022 Feb 2; 14(4):6261-6273. doi: 10.1021/acsami.1c24721. Epub 2022 Jan 19" |
