Impact of the Cationic Moiety of Ionic Liquids on Chemoselective Artificial Olfaction.

Ionogels and derived materials are assemblies of polymers and ionic liquids characterized by high stability and ionic conductivity, making them interesting choices as gas sensors. In this work, we assessed the effect of the ionic liquid moiety to generate ionogels and hybrid gels as electrical and optical gas sensors. Six ionic liquids consisting of a constant anion (chloride) and distinct cationic head groups were used to generate ionogels and hybrid gels and further tested as gas sensors in customized electronic nose devices. In general, ionogel-based sensors yielded higher classification accuracies of standard volatile organic compounds when compared to hybrid material-based sensors. In addition, the high chemical diversity of ionic liquids is further translated to a high functional diversity in analyte molecular recognition and sensing.

Effect of Polymer Hydrophobicity in the Performance of Hybrid Gel Gas Sensors for E-Noses.

Relative humidity (RH) is a common interferent in chemical gas sensors, influencing their baselines and sensitivity, which can limit the performance of e-nose systems. Tuning the composition of the sensing materials is a possible strategy to control the impact of RH in gas sensors. Hybrid gel materials used as gas sensors contain self-assembled droplets of ionic liquid and liquid crystal molecules encapsulated in a polymeric matrix. In this work, we assessed the effect of the matrix hydrophobic properties in the performance of hybrid gel materials for VOC sensing in humid conditions (50% RH). We used two different polymers, the hydrophobic PDMS and the hydrophilic bovine gelatin, as polymeric matrices in hybrid gel materials containing imidazolium-based ionic liquids, [BMIM][Cl] and [BMIM][DCA], and the thermotropic liquid crystal 5CB. Better accuracy of VOC prediction is obtained for the hybrid gels composed of a PDMS matrix combined with the [BMIM][Cl] ionic liquid, and the use of this hydrophobic matrix reduces the effect of humidity on the sensing performance when compared to the gelatin counterpart. VOCs interact with all the moieties of the hybrid gel multicomponent system; thus, VOC correct classification depends not only on the polymeric matrix used, but also on the IL selected, which seems to be key to achieve VOCs discrimination at 50% RH. Thus, hybrid gels' tunable formulation offers the potential for designing complementary sensors for e-nose systems operable under different RH conditions.

Synergy between silk fibroin and ionic liquids for active gas-sensing materials.

Silk fibroin is a biobased material with excellent biocompatibility and mechanical properties, but its use in bioelectronics is hampered by the difficult dissolution and low intrinsic conductivity. Some ionic liquids are known to dissolve fibroin but removed after fibroin processing. However, ionic liquids and fibroin can cooperatively give rise to functional materials, and there are untapped opportunities in this combination. The dissolution of fibroin, followed by gelation, in designer ionic liquids from the imidazolium chloride family with varied alkyl chain lengths (2-10 carbons) is shown here. The alkyl chain length of the anion has a large impact on fibroin secondary structure which adopts unconventional arrangements, yielding robust gels with distinct hierarchical organization. Furthermore, and due to their remarkable air-stability and ionic conductivity, fibroin ionogels are exploited as active electrical gas sensors in an electronic nose revealing the unravelled possibilities of fibroin in soft and flexible electronics.

Nanoscale Events on Cyanobiphenyl-Based Self-Assembled Droplets Triggered by Gas Analytes.

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.

Tackling Humidity with Designer Ionic Liquid-Based Gas Sensing Soft Materials.

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.

Stable and Oriented Liquid Crystal Droplets Stabilized by Imidazolium Ionic Liquids.

Liquid crystals represent a fascinating intermediate state of matter, with dynamic yet organized molecular features and untapped opportunities in sensing. Several works report the use of liquid crystal droplets formed by microfluidics and stabilized by surfactants such as sodium dodecyl sulfate (SDS). In this work, we explore, for the first time, the potential of surface-active ionic liquids of the imidazolium family as surfactants to generate in high yield, stable and oriented liquid crystal droplets. Our results show that [C12MIM][Cl], in particular, yields stable, uniform and monodisperse droplets (diameter 74 ± 6 µm; PDI = 8%) with the liquid crystal in a radial configuration, even when compared with the standard SDS surfactant. These findings reveal an additional application for ionic liquids in the field of soft matter.

Optical Gas Sensing with Liquid Crystal Droplets and Convolutional Neural Networks.

Liquid crystal (LC)-based materials are promising platforms to develop rapid, miniaturised and low-cost gas sensor devices. In hybrid gel films containing LC droplets, characteristic optical texture variations are observed due to orientational transitions of LC molecules in the presence of distinct volatile organic compounds (VOC). Here, we investigate the use of deep convolutional neural networks (CNN) as pattern recognition systems to analyse optical textures dynamics in LC droplets exposed to a set of different VOCs. LC droplets responses to VOCs were video recorded under polarised optical microscopy (POM). CNNs were then used to extract features from the responses and, in separate tasks, to recognise and quantify the vapours exposed to the films. The impact of droplet diameter on the results was also analysed. With our classification models, we show that a single individual droplet can recognise 11 VOCs with small structural and functional differences (F1-score above 93%). The optical texture variation pattern of a droplet also reflects VOC concentration changes, as suggested by applying a regression model to acetone at 0.9-4.0% (v/v) (mean absolute errors below 0.25% (v/v)). The CNN-based methodology is thus a promising approach for VOC sensing using responses from individual LC-droplets.

Ionogels Based on a Single Ionic Liquid for Electronic Nose Application.

Ionogel are versatile materials, as they present the electrical properties of ionic liquids and also dimensional stability, since they are trapped in a solid matrix, allowing application in electronic devices such as gas sensors and electronic noses. In this work, ionogels were designed to act as a sensitive layer for the detection of volatiles in a custom-made electronic nose. Ionogels composed of gelatin and a single imidazolium ionic liquid were doped with bare and functionalized iron oxide nanoparticles, producing ionogels with adjustable target selectivity. After exposing an array of four ionogels to 12 distinct volatile organic compounds, the collected signals were analyzed by principal component analysis (PCA) and by several supervised classification methods, in order to assess the ability of the electronic nose to distinguish different volatiles, which showed accuracy above 98%.

Microfluidics in Gas Sensing and Artificial Olfaction.

Rapid, real-time, and non-invasive identification of volatile organic compounds (VOCs) and gases is an increasingly relevant field, with applications in areas such as healthcare, agriculture, or industry. Ideal characteristics of VOC and gas sensing devices used for artificial olfaction include portability and affordability, low power consumption, fast response, high selectivity, and sensitivity. Microfluidics meets all these requirements and allows for in situ operation and small sample amounts, providing many advantages compared to conventional methods using sophisticated apparatus such as gas chromatography and mass spectrometry. This review covers the work accomplished so far regarding microfluidic devices for gas sensing and artificial olfaction. Systems utilizing electrical and optical transduction, as well as several system designs engineered throughout the years are summarized, and future perspectives in the field are discussed.

Enhanced Gas Sensing with Soft Functional Materials.

The materials described in this work result from the self-assembly of liquid crystals and ionic liquids into droplets, stabilized within a biopolymeric matrix. These systems are extremely versatile gels, in terms of composition, and offer potential for fine tuning of both structure and function, as each individual component can be varied. Here, the characterization and application of these gels as sensing thin films in gas sensor devices is presented. The unique supramolecular structure of the gels is explored for molecular recognition of volatile organic compounds (VOCs) by employing gels with distinct formulations to yield combinatorial optical and electrical responses used in the distinction and identification of VOCs.

Effect of film thickness in gelatin hybrid gels for artificial olfaction.

Artificial olfaction is a fast-growing field aiming to mimic natural olfactory systems. Olfactory systems rely on a first step of molecular recognition in which volatile organic compounds (VOCs) bind to an array of specialized olfactory proteins. This results in electrical signals transduced to the brain where pattern recognition is performed. An efficient approach in artificial olfaction combines gas-sensitive materials with dedicated signal processing and classification tools. In this work, films of gelatin hybrid gels with a single composition that change their optical properties upon binding to VOCs were studied as gas-sensing materials in a custom-built electronic nose. The effect of films thickness was studied by acquiring signals from gelatin hybrid gel films with thicknesses between 15 and 90 µm when exposed to 11 distinct VOCs. Several features were extracted from the signals obtained and then used to implement a dedicated automatic classifier based on support vector machines for data processing. As an optical signature could be associated to each VOC, the developed algorithms classified 11 distinct VOCs with high accuracy and precision (higher than 98%), in particular when using optical signals from a single film composition with 30 µm thickness. This shows an unprecedented example of soft matter in artificial olfaction, in which a single gelatin hybrid gel, and not an array of sensing materials, can provide enough information to accurately classify VOCs with small structural and functional differences.

Author Correction: Machine learning for the meta-analyses of microbial pathogens' volatile signatures.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has not been fixed in the paper.

Machine learning for the meta-analyses of microbial pathogens' volatile signatures.

Non-invasive and fast diagnostic tools based on volatolomics hold great promise in the control of infectious diseases. However, the tools to identify microbial volatile organic compounds (VOCs) discriminating between human pathogens are still missing. Artificial intelligence is increasingly recognised as an essential tool in health sciences. Machine learning algorithms based in support vector machines and features selection tools were here applied to find sets of microbial VOCs with pathogen-discrimination power. Studies reporting VOCs emitted by human microbial pathogens published between 1977 and 2016 were used as source data. A set of 18 VOCs is sufficient to predict the identity of 11 microbial pathogens with high accuracy (77%), and precision (62-100%). There is one set of VOCs associated with each of the 11 pathogens which can predict the presence of that pathogen in a sample with high accuracy and precision (86-90%). The implemented pathogen classification methodology supports future database updates to include new pathogen-VOC data, which will enrich the classifiers. The sets of VOCs identified potentiate the improvement of the selectivity of non-invasive infection diagnostics using artificial olfaction devices.

Tunable Gas Sensing Gels by Cooperative Assembly.

The cooperative assembly of biopolymers and small molecules can yield functional materials with precisely tunable properties. Here, the fabrication, characterization, and use of multicomponent hybrid gels as selective gas sensors are reported. The gels are composed of liquid crystal droplets self-assembled in the presence of ionic liquids, which further coassemble with biopolymers to form stable matrices. Each individual component can be varied and acts cooperatively to tune gels' structure and function. The unique molecular environment in hybrid gels is explored for supramolecular recognition of volatile compounds. Gels with distinct compositions are used as optical and electrical gas sensors, yielding a combinatorial response conceptually mimicking olfactory biological systems, and tested to distinguish volatile organic compounds and to quantify ethanol in automotive fuel. The gel response is rapid, reversible, and reproducible. These robust, versatile, modular, pliant electro-optical soft materials possess new possibilities in sensing triggered by chemical and physical stimuli.

A value-added exopolysaccharide as a coating agent for MRI nanoprobes.

Fucopol, a fucose-containing exopolysaccharide (EPS) produced by the bacterium Enterobacter A47 DSM 23139 using glycerol as a carbon source, was employed as a new coating material for iron oxide magnetic nanoparticles (MNPs). The coated particles were assessed as nanoprobes for cell labeling by Magnetic Resonance Imaging (MRI). The MNPs were synthesized by a thermal decomposition method and transferred to an aqueous medium by a ligand-exchange reaction with meso-2,3-dimercaptosuccinic acid (DMSA). Covalent binding of EPS to DMSA-stabilized nanoparticles (MNP-DMSA) resulted in a hybrid magnetic-biopolymeric nanosystem (MNP-DMSA-EPS) with a hydrodynamic size of 170 nm, a negative surface charge under physiological conditions and transverse to longitudinal relaxivity ratio, r2/r1, of 148. In vitro studies with two human cell lines (colorectal carcinoma - HCT116 - and neural stem/progenitor cells - ReNcell VM) showed that EPS promotes internalization of nanoparticles in both cell lines. In vitro MRI cell phantoms showed a superior performance of MNP-DMSA-EPS in ReNcell VM, for which the iron dose-dependent MRI signal drop was obtained at relatively low iron concentrations (12-20 µg Fe per ml) and short incubation times. Furthermore, ReNcell VM multipotency was not affected by culture in the presence of MNP-DMSA or MNP-DMSA-EPS for 14 days. Our study suggests that Fucopol-coated MNPs represent useful cell labeling nanoprobes for MRI.

Covalent coupling of gum arabic onto superparamagnetic iron oxide nanoparticles for MRI cell labeling: physicochemical and in vitro characterization.

Gum arabic (GA) is a hydrophilic composite polysaccharide derived from exudates of Acacia senegal and Acacia seyal trees. It is biocompatible, possesses emulsifying and stabilizing properties and has been explored as coating agent of nanomaterials for biomedical applications, namely magnetic nanoparticles (MNPs). Previous studies focused on the adsorption of GA onto MNPs produced by co-precipitation methods. In this work, MNPs produced by a thermal decomposition method, known to produce uniform particles with better crystalline properties, were used for the covalent coupling of GA through its free amine groups, which increases the stability of the coating layer. The MNPs were produced by thermal decomposition of Fe(acac)3 in organic solvent and, after ligand-exchange with meso-2,3-dimercaptosuccinic acid (DMSA), GA coating was achieved by the establishment of a covalent bond between DMSA and GA moieties. Clusters of several magnetic cores entrapped in a shell of GA were obtained, with good colloidal stability and promising magnetic relaxation properties (r2 /r1 ratio of 350). HCT116 colorectal carcinoma cell line was used for in vitro cytotoxicity evaluation and cell-labeling efficiency studies. We show that, upon administration at the respective IC50 , GA coating enhances MNP cellular uptake by 19 times compared to particles bearing only DMSA moieties. Accordingly, in vitro MR images of cells incubated with increasing concentrations of GA-coated MNP present dose-dependent contrast enhancement. The obtained results suggest that the GA magnetic nanosystem could be used as a MRI contrast agent for cell-labeling applications.

Effects of phase transfer ligands on monodisperse iron oxide magnetic nanoparticles.

Oleic acid coated iron oxide nanoparticles synthesized by thermal decomposition in organic medium are highly monodisperse but at the same time are unsuitable for biological applications. Ligand-exchange reactions are useful to make their surface hydrophilic. However, these could alter some structural and magnetic properties of the modified particles. Here we present a comprehensive study and comparison of the effects of employing either citric acid (CA) or meso-2,3-dimercaptosuccinic acid (DMSA) ligand-exchange protocols for phase transfer of monodisperse hydrophobic iron oxide nanoparticles produced by thermal decomposition of Fe(acac)3 in benzyl ether. We show the excellent hydrodynamic size distribution and colloidal stability of the hydrophilic particles obtained by the two protocols and confirm that there is a certain degree of oxidation caused by the ligand-exchange. CA revealed to be more aggressive towards the iron oxide surface than DMSA and greatly reduced the saturation magnetization values and initial susceptibility of the resulting particles compared to the native ones. Besides being milder and more straightforward to perform, the DMSA ligand exchange protocol produces MNP chemically more versatile for further functionalization possibilities. This versatility is shown through the covalent linkage of gum Arabic onto MNP-DMSA using carboxyl and thiol based chemical routes and yielding particles with comparable properties.