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Projeto de investigação
SCENT: Hybrid Gels for Rapid Microbial Detection
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Sustainable plant polyesters as substrates for optical gas sensors
Publication . Rodrigues, Rúben Miguel Lopes; Palma, Susana I. C. J.; Correia, Vanessa G.; Padrão, Inês; Pais, Joana; Banza, M.; Alves, Cláudia; Deuermeier, Jonas; Martins, Celso; Costa, Henrique M. A.; Ramou, Efthymia; Pereira, Cristina Silva; Roque, Ana Cecília Afonso; Instituto de Tecnologia Química e Biológica António Xavier (ITQB); UCIBIO - Applied Molecular Biosciences Unit; DQ - Departamento de Química; CENIMAT-i3N - Centro de Investigação de Materiais (Lab. Associado I3N); UNINOVA-Instituto de Desenvolvimento de Novas Tecnologias; DCM - Departamento de Ciência dos Materiais; Bioresources 4 Sustainability (GREEN-IT); Elsevier BV
The fast and non-invasive detection of odors and volatile organic compounds (VOCs) by gas sensors and electronic noses is a growing field of interest, mostly due to a large scope of potential applications. Additional drivers for the expansion of the field include the development of alternative and sustainable sensing materials. The discovery that isolated cross-linked polymeric structures of suberin spontaneously self-assemble as a film inspired us to develop new sensing composite materials consisting of suberin and a liquid crystal (LC). Due to their stimuli-responsive and optically active nature, liquid crystals are interesting probes in gas sensing. Herein, we report the isolation and the chemical characterization of two suberin types (from cork and from potato peels) resorting to analyses of gas chromatography–mass spectrometry (GC-MS), solution nuclear magnetic resonance (NMR), and X-ray photoelectron spectroscopy (XPS). The collected data highlighted their compositional and structural differences. Cork suberin showed a higher proportion of longer aliphatic constituents and is more esterified than potato suberin. Accordingly, when casted it formed films with larger surface irregularities and a higher C/O ratio. When either type of suberin was combined with the liquid crystal 5CB, the ensuing hybrid materials showed distinctive morphological and sensing properties towards a set of 12 VOCs (comprising heptane, hexane, chloroform, toluene, dichlormethane, diethylether, ethyl acetate, acetonitrile, acetone, ethanol, methanol, and acetic acid). The optical responses generated by the materials are reversible and reproducible, showing stability for 3 weeks. The individual VOC-sensing responses of the two hybrid materials are discussed taking as basis the chemistry of each suberin type. A support vector machines (SVM) algorithm based on the features of the optical responses was implemented to assess the VOC identification ability of the materials, revealing that the two distinct suberin-based sensors complement each other, since they selectively identify distinct VOCs or VOC groups. It is expected that such new environmentally-friendly gas sensing materials derived from natural diversity can be combined in arrays to enlarge selectivity and sensing capacity.
Cephalopod proteins for bioinspired and sustainable biomaterials design
Publication . Lychko, Iana; Padrão, Inês; Eva, Afonso Vicente; Domingos, Catarina Alexandra Oliveira; Costa, Henrique Miguel Aljustrel da; Dias, Ana Margarida Gonçalves Carvalho; Roque, Ana Cecília Afonso; UCIBIO - Applied Molecular Biosciences Unit; Elsevier BV
Nature offers a boundless source of inspiration for designing bio-inspired technologies and advanced materials. Cephalopods, including octopuses, squids, and cuttlefish, exhibit remarkable biological adaptations, such as dynamic camouflage for predator evasion and communication, as well as robust prey-capturing tools, including beaks and sucker-ring teeth that operate under extreme mechanical stresses in aqueous environments. Central to these remarkable traits are structural proteins that serve as versatile polymeric materials. From a materials science perspective, proteins present unique opportunities due to their genetically encoded sequences, enabling access to a diversity of sequences and precise control over polymer composition and properties. This intrinsic programmability allows scalable, environmentally sustainable production through recombinant biotechnology, in contrast to petroleum-derived polymers. This review highlights recent advances in understanding cephalopod-specific proteins, emphasizing their potential for creating next-generation bioengineered materials and driving sustainable innovation in biomaterials science.
Fish gelatin-based films for gas sensing
Publication . Moreira, Inês Pimentel; Sato, Laura; Alves, Cláudia; Palma, Susana; Roque, Ana Cecília; UCIBIO - Applied Molecular Biosciences Unit; DQ - Departamento de Química
Electronic noses (e-noses) mimic the complex biological olfactory system, usually including an array of gas sensors to act as the olfactory receptors and a trained computer with signal-processing and pattern recognition tools as the brain. In this work, a new stimuli-responsive material is shown, consisting of self-assembled droplets of liquid crystal and ionic liquid stabilised within a fish gelatin matrix. These materials change their opto/electrical properties upon contact with volatile organic compounds (VOCs). By using an in-house developed e-nose, these new gas-sensing films yield characteristic optical signals for VOCs from different chemical classes. A support vector machine classifier was implemented based on 12 features of the signals. The results show that the films are excellent identifying hydrocarbon VOCs (toluene, heptane and hexane) (95% accuracy) but lower performance was found to other VOCs, resulting in an overall 60.4% accuracy. Even though they are not reusable, these sustainable gas-sensing films are stable throughout time and reproducible, opening several opportunities for future optoelectronic devices and artificial olfaction systems.
Classification of Volatile Compounds with Morphological Analysis of e-nose Response
Publication . Alves, Rita; Rodrigues, João; Ramou, Efthymia; Palma, Susana; Roque, Ana; Gamboa, Hugo; LIBPhys-UNL; DQ - Departamento de Química; UCIBIO - Applied Molecular Biosciences Unit
Electronic noses (e-noses) mimic human olfaction, by identifying Volatile Organic Compounds (VOCs). This work presents a novel approach that successfully classifies 11 known VOCs using the signals generated by sensing gels in an in-house developed e-nose. The proposed signals' analysis methodology is based on the generated signals' morphology for each VOC since different sensing gels produce signals with different shapes when exposed to the same VOC. For this study, two different gel formulations were considered, and an average f1-score of 84% and 71% was obtained, respectively. Moreover, a standard method in time series classification was used to compare the performances. Even though this comparison reveals that the morphological approach is not as good as the 1-nearest neighbour with euclidean distance, it shows the possibility of using descriptive sentences with text mining techniques to perform VOC classification.
Impact of sensing film's production method on classification accuracy by electronic nose
Publication . Pádua, Ana; Gruber, Jonas; Gamboa, Hugo; Roque, Ana Cecília; UCIBIO - Applied Molecular Biosciences Unit; DQ - Departamento de Química; DF – Departamento de Física; LIBPhys-UNL
The development of gas sensing materials is relevant in the field of non-invasive biodevices. In this work, we used an electronic nose (E-nose) developed by our research group, which possess versatile and unique sensing materials. These are gels that can be spread over the substrate by Film Coating or Spin Coating. This study aims to evaluate the influence of the sensing film spreading method selected on the classification capabilities of the E-nose. The methodology followed consisted of performing an experiment where the E-nose was exposed to 13 different pure volatile organic compounds. The sensor array had two sensing films produced by Film Coating, and other two produced by Spin Coating. After data collection, a set of features was extracted from the original signal curves, and the best were selected by Recursive Feature Elimination. Then, the classification performance of Multinomial Logistic regression, Decision Tree, and Naïve Bayes was evaluated. The results showed that both spreading methods for sensing films production are adequate since the estimated error of classification was inferior to 4 % for all the classification tools applied.
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Entidade financiadora
European Commission
Programa de financiamento
H2020
Número da atribuição
639123