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Incorporation of VOC-Selective Peptides in Gas Sensing Materials

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dc.contributor.authorOliveira, Ana Rita
dc.contributor.authorRamou, Efthymia
dc.contributor.authorTeixeira, Gonçalo Duarte Gomes
dc.contributor.authorPalma, Susana I. C. J.
dc.contributor.authorRoque, Ana C. A.
dc.contributor.institutionUCIBIO - Applied Molecular Biosciences Unit
dc.contributor.institutionDQ - Departamento de Química
dc.date.accessioned2022-07-06T22:15:42Z
dc.date.available2022-07-06T22:15:42Z
dc.date.issued2021
dc.descriptionSCENT-ERC-2014-STG-639123, (2015-2022) LA/P/0140/2020
dc.description.abstractEnhancing 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.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent1466500
dc.identifier.doi10.5220/0010797200003123
dc.identifier.isbn978-989-758-552-4
dc.identifier.otherPURE: 45178997
dc.identifier.otherPURE UUID: a0e54937-130d-4fa6-9bb3-fcb5fde4cb16
dc.identifier.otherWOS: 000778905500002
dc.identifier.otherORCID: /0000-0002-1851-8110/work/115499376
dc.identifier.urihttp://hdl.handle.net/10362/141469
dc.language.isoeng
dc.peerreviewedyes
dc.publisherSciTePress - Science and Technology Publications
dc.relationinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F04378%2F2020/PT
dc.relationApplied Molecular Biosciences Unit
dc.relationApplied Molecular Biosciences Unit
dc.relationApplied Molecular Biosciences Unit
dc.relationAn affinity-based approach towards highly specific odor sensing
dc.relationEngineered proteins for gas sensing
dc.relationinfo:eu-repo/grantAgreement/FCT/OE/PD%2FBD%2F139800%2F2018/PT
dc.subjectIonogels
dc.subjectHybrid Gels
dc.subjectPeptides
dc.subjectGas Sensing
dc.subjectElectronic Nose
dc.titleIncorporation of VOC-Selective Peptides in Gas Sensing Materialsen
dc.typeconference object
degois.publication.firstPage25
degois.publication.lastPage34
degois.publication.titleProceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIODEVICES)
degois.publication.title15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC) / 15th International Conference on Biomedical Electronics and Devices (BIODEVICES)
degois.publication.volume1
dspace.entity.typePublication
oaire.awardNumberUIDP/04378/2020
oaire.awardNumberUIDB/04378/2020
oaire.awardNumberUID/Multi/04378/2019
oaire.awardNumberSFRH/BD/128687/2017
oaire.awardNumberPD/BD/139800/2018
oaire.awardTitleApplied Molecular Biosciences Unit
oaire.awardTitleApplied Molecular Biosciences Unit
oaire.awardTitleApplied Molecular Biosciences Unit
oaire.awardTitleAn affinity-based approach towards highly specific odor sensing
oaire.awardTitleEngineered proteins for gas sensing
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F04378%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04378%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UID%2FMulti%2F04378%2F2019/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT//SFRH%2FBD%2F128687%2F2017/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT//PD%2FBD%2F139800%2F2018/PT
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccess
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