Relative humidity is simultaneously a sensing target and a contaminant in gas and volatile organic compound (VOC) sensing systems, where strategies to control humidity interference are required. An unmet challenge is the creation of gas-sensitive materials where the response to humidity is controlled by the material itself. Here, humidity effects are controlled through the design of gelatin formulations in ionic liquids without and with liquid crystals as electrical and optical sensors, respectively. In this design, the anions [DCA]− and [Cl]− of room temperature ionic liquids from the 1-butyl-3-methylimidazolium family tailor the response to humidity and, subsequently, sensing of VOCs in dry and humid conditions. Due to the combined effect of the materials formulations and sensing mechanisms, changing the anion from [DCA]− to the much more hygroscopic [Cl]−, leads to stronger electrical responses and much weaker optical responses to humidity. Thus, either humidity sensors or humidity-tolerant VOC sensors that do not require sample preconditioning or signal processing to correct humidity impact are obtained. With the wide spread of 3D- and 4D-printing and intelligent devices, the monitoring and tuning of humidity in sustainable biobased materials offers excellent opportunities in e-nose sensing arrays and wearable devices compatible with operation at room conditions.

In hybrid gels with immobilized liquid crystal
(LC) droplets, fast and unique optical texture variations are
generated when distinct volatile organic compounds (VOCs)
interact with the LC and disturb its molecular order. The
optical texture variations can be observed under a polarized
optical microscope or transduced into a signal representing the
variations of light transmitted through the LC. We show how
hybrid gels can accurately identify 11 distinct VOCs by using
deep learning to analyze optical texture variations of individual
droplets (0.93 average F1-score) and by using machine learning
to analyze 1D optical signals from multiple droplets in hybrid
gels (0.88 average F1-score)

Enhancing the selectivity of gas sensing materials towards specific volatile organic compounds (VOCs) is
challenging due to the chemical simplicity of VOCs as well as the difficulty in interfacing VOC selective
biological elements with electronic components used in the transduction process. We aimed to tune the
selectivity of gas sensing materials through the incorporation of VOC-selective peptides into gel-like gas
sensing materials. Specifically, a peptide (P1) known to discriminate single carbon deviations among benzene
and derivatives, along with two modified versions (P2 and P3), were integrated with gel compositions
containing gelatin, ionic liquid and without or with a liquid crystal component (ionogels and hybrid gels
respectively). These formulations change their electrical or optical properties upon VOC exposure, and were
tested as sensors in an in-house developed e-nose. Their ability to distinct and identify VOCs was evaluated
via a supervised machine learning classifier. Enhanced discrimination of benzene and hexane was detected
for the P1-based hybrid gel. Additionally, complementarity of the electrical and optical sensors was observed
considering that a combination of both their accuracy predictions yielded the best classification results for the
tested VOCs. This indicates that a combinatorial array in a dual-mode e-nose could provide optimal
performance and enhanced selectivity.