Author

G. Barandun

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioengineering· 3

Signature Assays, Organisms & Methods

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Approach:experimental assay· 2 Research Focus:differential expression· 1 Research Focus:drug response / drug discovery· 1 Assay:electrophysiology· 1 Approach:machine learning (classical)· 1 Assay:metagenomics· 1 Organism:plant (other)· 1

Frequent Co-Authors

T. Asfour· 3 shared E. Nunez-Bajo· 2 shared F. Guder· 2 shared L. Gonzalez-Macia· 2 shared P. Coatsworth· 2 shared Y. Cotur· 2 shared A. Collins· 1 shared E. Tan· 1 shared

Activity by Year

2020-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 35 mutual co-author links

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3 shared papersBioengineering

T. Asfour

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Facilitating Electrochemical Lateral Flow Assay using NFC-Enabled Potentiostat and Nitrocellulose-based Metal Electrodes

L. Gonzalez-Macia, Y. Li, K. Zhang, E. Nunez-Bajo, G. Barandun, Y. Cotur, T. Asfour, S. Olenik, P. Coatsworth, J. Herrington, F. Güder

The paper develops a digital diagnostics platform using NFC-enabled potentiostat and nitrocellulose-based electrodes for electrochemical detection of pathogens at the point-of-need.

bioRxiv · Mar 9, 2023 · PreprintBioengineering

PEDOT:PSS-Modified Cotton Conductive Thread for Mass Manufacturing of Textile-Based Electrical Wearable Sensors by Computerized Embroidery

F. Alshabouna, H. S. Lee, G. Barandun, E. Tan, Y. Cotur, T. Asfour, L. Gonzalez-Macia, P. Coatsworth, E. Nunez-Bajo, J. S. Kim, F. Guder

This paper develops a conductive thread compatible with computerized embroidery for mass-producing wearable sensors.

bioRxiv · Dec 6, 2021Bioengineering

Determining and Predicting Soil Chemistry with a Point-of-Use Sensor Toolkit and Machine Learning Model

M. Grell, G. Barandun, T. Asfour, M. Kasimatis, A. Collins, J. Wang, F. Guder

This paper develops a point-of-use sensor and machine learning model to predict soil nitrogen levels, improving fertilization planning and reducing overfertilization.

bioRxiv · Oct 8, 2020 · PreprintBioengineering