Dataset Information

Machine learning-assisted ammonium detection using zinc oxide/multi-walled carbon nanotube composite based impedance sensors.

ABSTRACT: We report a machine learning approach to accurately correlate the impedance variations in zinc oxide/multi walled carbon nanotube nanocomposite (F-MWCNT/ZnO-NFs) to NH₄⁺ ions concentrations. Impedance response of F-MWCNT/ZnO-NFs nanocomposites with varying ZnO:MWCNT compositions were evaluated for its sensitivity and selectivity to NH₄⁺ ions in the presence of structurally similar analytes. A decision-making model was built, trained and tested using important features of the impedance response of F-MWCNT/ZnO-NF to varying NH₄⁺ concentrations. Different algorithms such as kNN, random forest, neural network, Naïve Bayes and logistic regression are compared and discussed. ML analysis have led to identify the most prominent features of an impedance spectrum that can be used as the ML predictors to estimate the real concentration of NH₄⁺ ion levels. The proposed NH₄⁺ sensor along with the decision-making model can identify and operate at specific operating frequencies to continuously collect the most relevant information from a system.

SUBMITTER: Aliyana AK

PROVIDER: S-EPMC8692315 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Machine learning-assisted ammonium detection using zinc oxide/multi-walled carbon nanotube composite based impedance sensors.

Aliyana Akshaya Kumar AK Naveen Kumar S K SK Marimuthu Pradeep P Baburaj Aiswarya A Adetunji Michael M Frederick Terrance T Sekhar Praveen P Fernandez Renny Edwin RE

Scientific reports 20211221 1

We report a machine learning approach to accurately correlate the impedance variations in zinc oxide/multi walled carbon nanotube nanocomposite (F-MWCNT/ZnO-NFs) to NH4+ ions concentrations. Impedance response of F-MWCNT/ZnO-NFs nanocomposites with varying ZnO:MWCNT compositions were evaluated for its sensitivity and selectivity to NH4+ ions in the presence of structurally similar analytes. A decision-making model was built, trained and tested using im ...[more]

PMID: 34934086

Similar Datasets

Project description:UnlabelledThe use of exogenous electrical stimulation to promote nerve regeneration has achieved only limited success. Conditions impeding optimized outgrowth may arise from inadequate stimulus presentation due to differences in injury geometry or signal attenuation. Implantation of an electrically-conductive biomaterial may mitigate this attenuation and provide a more reproducible signal. In this study, a conductive nanofiller (single-walled carbon nanotubes [SWCNT]) was selected as one possible material to manipulate the bulk electrical properties of a collagen type I-10% Matrigel™ composite hydrogel. Neurite outgrowth within hydrogels (SWCNT or nanofiller-free controls) was characterized to determine if: (1) nanofillers influence neurite extension and (2) electrical stimulation of the nanofiller composite hydrogel enhances neurite outgrowth. Increased SWCNT loading (10-100-μg/mL) resulted in greater bulk conductivity (up to 1.7-fold) with no significant changes to elastic modulus. Neurite outgrowth increased 3.3-fold in 20-μg/mL SWCNT loaded biomaterials relative to the nanofiller-free control. Electrical stimulation promoted greater outgrowth (2.9-fold) within SWCNT-free control. The concurrent presentation of electrical stimulation and SWCNT-loaded biomaterials resulted in a 7.0-fold increase in outgrowth relative to the unstimulated, nanofiller-free controls. Local glia residing within the DRG likely contribute, in part, to the observed increases in outgrowth; but it is unknown which specific nanofiller properties influence neurite extension. Characterization of neuronal behavior in model systems, such as those described here, will aid the rational development of biomaterials as well as the appropriate delivery of electrical stimuli to support nerve repair.Statement of significanceNovel biomedical devices delivering electrical stimulation are being developed to mitigate symptoms of Parkinson's, treat drug-resistant depression, control movement or enhance verve regeneration. Carbon nanotubes and other novel materials are being explored for novel nano-neuro devices based on their unique properties. Neuronal growth on carbon nanotubes has been studied in 2D since the early 2000s demonstrating increased outgrowth, synapse formation and network activity. In this work, single-walled carbon nanotubes were selected as one possible electrically-conductive material, dispersed within a 3D hydrogel containing primary neurons; extending previous 2D work to 3D to evaluate outgrowth within nanomaterial composites with electrical stimulation. This is the first study to our knowledge that stimulates neurons in 3D composite nanomaterial-laden hydrogels. Examination of electrically conductive biomaterials may serve to promote regrowth following injury or in long term stimulation.

Dataset Information

Machine learning-assisted ammonium detection using zinc oxide/multi-walled carbon nanotube composite based impedance sensors.

Publications

Machine learning-assisted ammonium detection using zinc oxide/multi-walled carbon nanotube composite based impedance sensors.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets