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Title: Forensic applications of atomic force microscopy
Author: Konopinski, D. I.
Awarding Body: University College London (University of London)
Current Institution: University College London (University of London)
Date of Award: 2013
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The first project undertaken was to develop a currently non-existent forensic technique -- data recovery from damaged SIM cards. SIM cards hold data valuable to a forensic investigator within non-volatile EEPROM/flash memory arrays. This data has been proven to be able to withstand temperatures up to 500°C, surviving such scenarios as house fires or criminal evidence disposal. A successful forensically-sound sample extraction, mounting and backside processing methodology was developed to expose the underside of a microcontroller circuit's floating gate transistor tunnel oxide, allowing probing via AFM-based electrical scanning probe techniques. Scanning Kelvin probe microscopy has thus far proved capable of detecting the presence of stored charge within the floating gates beneath the thin tunnel oxide layer, to the point of generating statistical distributions reflecting the threshold voltage states of the transistors. The second project covered the novel forensic application of AFM as a complimentary technique to SEM examination of quartz grain surface textures. The analysis and interpretation of soil/sediment samples can provide indications of their provenance, and enable exclusionary comparisons to be made between samples pertinent to a forensic investigation. Multiple grains from four distinct sample sets were examined with the AFM, and various statistical figures of merit were derived. Canonical discriminant analysis was used to assess the discriminatory abilities of these statistical variables to better characterise the use of AFM results for grain classification. The final functions correctly classified 65.3% of original grouped cases, with the first 3 discriminant functions used in the analysis (Wilks' Lambda=0.336, p=0.000<0.01). This degree of discrimination shows a great deal of promise for the AFM as a quantitative corroborative technique to traditional SEM grain surface examination.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available