Architecting and Developing Sensors for Environmental Monitoring
| dc.contributor.guide | Ramamurthy, Praveen C and Subramanian, S | |
| dc.creator.researcher | Anil, Amith G | |
| dc.date.accessioned | 2022-12-21T10:05:30Z | |
| dc.date.available | 2022-12-21T10:05:30Z | |
| dc.date.awarded | 2022 | |
| dc.date.completed | 2021 | |
| dc.description.abstract | Pollution arising from inorganic and organic contaminants is a major environmental hazard that needs imperative attention. Anthropogenic and natural deposition of heavy metal ions constitute a significant component of this pollution. Selective, sensitive, and unambiguous detection of such pollutants is of prime importance in assessing environmental damage and controlling toxicity in biosystems. To this date, multifarious sensing systems and methodologies have been developed. Limited selectivity, sensitivity and environmental stability of these sensors continue to hinder their large-scale deployment. This works attempts to look at the problems of selectivity and sensitivity taking inspiration from natural processes like bioremediation and utilising data driven methods. Bioremediation is a potential, environmentally benign method for treating heavy metal polluted water. Bioremediation of hexavalent chromium, a carcinogenic pollutant, by Citrobacter freundii species of bacteria has been evaluated. Synchrotron based scanning transmission X-ray microscopy was utilised to analyse the process in an attempt to decipher the biosorption and bioreduction mechanism. Understanding this would assist in designing better selective sensors for Cr(VI). Bacteria derived carbon dots were synthesised from Citrobacter freundii bacterial cells following a green synthesis approach. The obtained carbon dots displayed fluorescence sensing with exceptional selectivity towards Cr(VI). The sensor performance and mechanism of Cr(VI) detection were thoroughly evaluated. In another work, a colorimetric sensor with high selectivity toward Cr(VI) was demonstrated and the sensor response was predicted using data driven methods. The computational method could simulate and predict sensor response at unknown concentrations in between known experimental concentrations. These predictions can generate sensor calibration scales with high accuracy, eliminating experimental and instrumental errors involved... | |
| dc.format.accompanyingmaterial | DVD | |
| dc.identifier.uri | http://hdl.handle.net/10603/429354 | |
| dc.language | English | |
| dc.publisher.institution | Materials Engineering | |
| dc.publisher.place | Bangalore | |
| dc.publisher.university | Indian Institute of Science Bangalore | |
| dc.rights | university | |
| dc.source.university | University | |
| dc.subject.keyword | Engineering and Technology | |
| dc.subject.keyword | Material Science | |
| dc.subject.keyword | Materials Science Multidisciplinary | |
| dc.title | Architecting and Developing Sensors for Environmental Monitoring | |
| dc.type.degree | Ph.D. |
Files
Original bundle
1 - 5 of 13
Loading...
- Name:
- 01_title.pdf
- Size:
- 47.82 KB
- Format:
- Adobe Portable Document Format
- Description:
- Attached File
Loading...
- Name:
- 02_prelim pages.pdf
- Size:
- 250.52 KB
- Format:
- Adobe Portable Document Format
Loading...
- Name:
- 03_table of content.pdf
- Size:
- 16.47 KB
- Format:
- Adobe Portable Document Format
License bundle
1 - 1 of 1