FCT: DQ - Documentos de conferências internacionais
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- Structure of a Sixteen Heme Cytochrome by X-Ray Crystallography.Publication . Silva, T. S.; Dias, J. M.; Bourenkov, G.; Bartunik, H.; Moura, I.; Romão, M. J.; DQ - Departamento de Química; CQFB-REQUIMTE - Centro de Química Fina e Biotecnologia (Lab. Associado REQUIMTE); John Wiley and Sons Inc.
- Incorporation of VOC-Selective Peptides in Gas Sensing MaterialsPublication . Oliveira, Ana Rita; Ramou, Efthymia; Teixeira, Gonçalo Duarte Gomes; Palma, Susana I. C. J.; Roque, Ana C. A.; UCIBIO - Applied Molecular Biosciences Unit; DQ - Departamento de QuímicaEnhancing 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, complementaritv 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.
- Fish gelatin-based films for gas sensingPublication . Moreira, Inês Pimentel; Sato, Laura; Alves, Cláudia; Palma, Susana; Roque, Ana Cecília; UCIBIO - Applied Molecular Biosciences Unit; DQ - Departamento de QuímicaElectronic 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.
- Impact of sensing film's production method on classification accuracy by electronic nosePublication . Pádua, Ana; Gruber, Jonas; Gamboa, Hugo; Roque, Ana Cecília; UCIBIO - Applied Molecular Biosciences Unit; DQ - Departamento de Química; DF – Departamento de Física; LIBPhys-UNLThe 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.
- An optimized e-nose for efficient volatile sensing and discriminationPublication . Santos, Gonçalo; Alves, Cláudia; Pádua, Ana Carolina; Palma, Susana; Gamboa, Hugo; Roque, Ana Cecília; UCIBIO - Applied Molecular Biosciences Unit; DQ - Departamento de Química; DF – Departamento de Física; LIBPhys-UNLElectronic noses (E-noses), are usually composed by an array of sensors with different selectivities towards classes of VOCs (Volatile Organic Compounds). These devices have been applied to a variety of fields, including environmental protection, public safety, food and beverage industries, cosmetics, and clinical diagnostics. This work demonstrates that it is possible to classify eleven VOCs from different chemical classes using a single gas sensing biomaterial that changes its optical properties in the presence of VOCs. To accomplish this, an in-house built E-nose, tailor-made for the novel class of gas sensing biomaterials, was improved and combined with powerful machine learning techniques. The device comprises a delivery system, a detection system and a data acquisition and control system. It was designed to be stable, miniaturized and easy-to-handle. The data collected was pre-processed and features and curve fitting parameters were extracted from the original response. A recursive feature selection method was applied to select the best features, and then a Support Vector Machine classifier was implemented to distinguish the eleven distinct VOCs. The results show that the followed methodology allowed the classification of all the VOCs tested with 94.6% (± 0.9%) accuracy.
- IoT applied to Environmental Monitoring in Oysters' FarmsPublication . Viegas, Vítor; Pereira, J. M. Dias; Girão, Pedro; Postolache, Octavian; Salgado, Ricardo; DQ - Departamento de Química; LAQV@REQUIMTENowadays, the aquaculture of oysters represents an important economical activity in coastal and estuarine areas. It is known that the growth rate of oysters is affected by several water parameters, including temperature, salinity, turbidity, pH and dissolved oxygen. In this paper, a cloud-based platform is proposed to acquire water parameters that affect oysters' growth. The paper includes the hardware and software description of the measurement system, details about the storage and processing of the acquired data, and some experimental results about the above-mentioned parameters.
- Sustainable energy generation by reverse electrodialysisPublication . Pawlowski, S.; Crespo, J. P. G.; Velizarov, S.; DQ - Departamento de Química; CQFB-REQUIMTE - Centro de Química Fina e Biotecnologia (Lab. Associado REQUIMTE)
- Study of the behavior of magnetic ionic liquids supported membranes for selective TransportPublication . Daniel, C. I.; Afonso, C. A.; Chavez, F. V.; Sebastião, P. J.; Portugal, C. A.; Crespo, J. G.; DQ - Departamento de Química; CQFB-REQUIMTE - Centro de Química Fina e Biotecnologia (Lab. Associado REQUIMTE)
- Influence of inorganic salts on the rejection and transport of organic compounds through a reverse osmosis membranePublication . Oliveira, P. I. C.; Velizarov, S.; Crespo, J. G.; DQ - Departamento de Química; CQFB-REQUIMTE - Centro de Química Fina e Biotecnologia (Lab. Associado REQUIMTE)
- Electromembrane processing for the recovery of low-molecular weight bioactive compounds from model solutionsPublication . Bober, M.; Crespo, J. G.; Velizarov, S.; DQ - Departamento de Química; CQFB-REQUIMTE - Centro de Química Fina e Biotecnologia (Lab. Associado REQUIMTE)