John Selberg
University of California, Santa Cruz
26 Papers
28 Citations
John Selberg is an academic researcher from University of California, Santa Cruz. The author has contributed to research in topics: Computer science & Bioelectronics. The author has an hindex of 9, co-authored 19 publications.
Chat about Author
Papers
A non-enzymatic glucose sensor enabled by bioelectronic pH control.
Xenofon Strakosas,John Selberg,Pattawong Pansodtee,Nebyu Yonas,Pattawut Manapongpun,Mircea Teodorescu,Marco Rolandi +6 more
TL;DR: This work introduces a non-enzymatic metal oxide glucose sensor that functions in neutral fluids by electronically inducing a reversible and localized pH change and demonstrates glucose monitoring at physiologically relevant levels inneutral fluids mimicking sweat, and wireless communication with a personal computer via an integrated circuit board.
Proton conductivity of glycosaminoglycans.
TL;DR: The proton conductivity of hydrated keratan sulfate purified from Bovine Cornea is measured to support the relationship between proton Conductivity and the chemical structure of biopolymers.
Taking Electrons out of Bioelectronics: From Bioprotonic Transistors to Ion Channels.
TL;DR: The latest efforts on bioprotonic devices that monitor and control a current of H+ in physiological conditions are summarized, and future potential applications are discussed.
33
Modular automated microfluidic cell culture platform reduces glycolytic stress in cerebral cortex organoids
Spencer T. Seiler,Gary L. Mantalas,John Selberg,Sergio P. Cordero,Sebastian Torres-Montoya,Pierre Baudin,Victoria T. Ly,Finn Amend,Liam Tran,Ryan Hoffman,Marco Rolandi,Richard E. Green,David Haussler,Sofie R. Salama,Mircea Teodorescu +14 more
TL;DR: In this article , a multiplex platform is developed to automate the culture of individual organoids in isolated microenvironments at user-defined media flow rates, allowing the use of multiple reagent reservoirs that may be applied to direct differentiation, study temporal variables, and grow cultures long term.
Feedback Control of Bioelectronic Devices Using Machine Learning
Mohammad Jafari,Giovanny Marquez,John Selberg,Manping Jia,Harika Dechiraju,Pattawong Pansodtee,Mircea Teodorescu,Marco Rolandi,Marcella M. Gomez +8 more
- 01 Oct 2021
TL;DR: The promise of leveraging tools in synthetic biology with an online machine learning (ML)-based feedback controller to achieve a precise spatiotemporal response of biological systems using bioelectronics driven by an adaptive external “sense and respond” learning algorithm is demonstrated.
25