FindAPhD Weekly PhD Newsletter | JOIN NOW FindAPhD Weekly PhD Newsletter | JOIN NOW

Development of next-generation analytical and data treatment approaches for enhancing possibilities in forensic profiling of chemical residues (Ref: SF20/APP/GALLIDABINO)

   Faculty of Health and Life Sciences

This project is no longer listed on and may not be available.

Click here to search for PhD studentship opportunities
  Dr M Gallidabino  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

Chemical traces, such as gunshot or arson accelerant residues, are common evidence types that can assist in the investigation of many serious crimes. Despite, however, the inherent abundance of information that could be extracted from their analysis, often just a small proportion is exploited in current routine analysis and, usually, also only for the purpose of identifying the type of material involved. Questions with a potentially higher impact in crime investigation, such as establishing the source of the analysed residues and/or inferring the specific activity that generated them, are still rarely addressed, mainly due to the complexity associated with the extraction and interpretation of the related chemical features.
This project aims to develop new inter-disciplinary approaches to extract enhanced forensic evidence and/or intelligence from chemical traces and, therefore, improve their role in the criminal justice system. In this regard, recent literature has shown that the application of non-targeted, omics-inspired methods coupled to next-generation chemometrics, as used in other analytical fields, could also assist forensic scientists in efficiently approaching complex problems in chemical criminalistics. The potential of these approaches will thus be explored throughout the project, in order to provide easy-to-use solutions to be implemented in an operational context. In particular, new comprehensive, multi-residue techniques will be developed for the analysis of common chemical residues and coupled with modern chemometric methods, based on machine learning and artificial intelligence, for the interpretation of extracted data. This will allow new tools to be proposed to effectively model relationships between physical traces, as well as their formation and transfer phenomena. The future benefits will be broad, with potential opportunities for extension to other scientific fields, such as environmental sciences.

Eligibility and How to Apply:
Please note eligibility requirement:
• Academic excellence of the proposed student i.e. 2:1 (or equivalent GPA from non-UK universities [preference for 1st class honours]); or a Masters (preference for Merit or above); or APEL evidence of substantial practitioner achievement.
• Appropriate IELTS score, if required.
• Applicants cannot apply for this funding if currently engaged in Doctoral study at Northumbria or elsewhere.

For further details of how to apply, entry requirements and the application form, see

Please note: Applications should include a covering letter that includes a short summary (500 words max.) of a relevant piece of research that you have previously completed and the reasons you consider yourself suited to the project. Applications that do not include the advert reference (e.g. SF20/…) will not be considered.

Deadline for applications: 1st July for October start, or 1st December for March start
Start Date: October or March
Northumbria University takes pride in, and values, the quality and diversity of our staff. We welcome applications from all members of the community. The University holds an Athena SWAN Bronze award in recognition of our commitment to improving employment practices for the advancement of gender equality.

For enquiries, contact Dr Matteo Gallidabino ([Email Address Removed])

Funding Notes

Please note, this is a self-funded project and does not include tuition fees or stipend; the studentship is available to Students Worldwide. Fee bands are available at . A relevant fee band will be discussed at interview based on project running costs


Gallidabino, M.D., Barron, L.P., Weyermann, C., & Romolo, F.S. (2019). Quantitative profile-profile relationship (QPPR) modelling: a novel machine learning approach to predict and associate chemical characteristics of unspent ammunition from gunshot residue (GSR). Analyst, 144(4), 1128-1139.

Gallidabino, M.D., Irlam, R.C., Salt, M.C., O’Donnell, M., Beardah, M.S., & Barron, L.P. (2019). Targeted and non-targeted forensic profiling of black powder substitutes and gunshot residue using gradient ion chromatography – high resolution mass spectrometry (IC-HRMS). Analytica Chimica Acta, 1072, 1-14.

Gallidabino, M., Hamdan, L., Murphy, B., & Barron, L.P. (2018). Suspect screening of halogenated carboxylic acids in drinking water using ion chromatography – high resolution (Orbitrap) mass spectrometry (IC-HRMS). Talanta, 178, 57-68. DOI: 10.1016/j.talanta.2017.08.092

Gallidabino, M., Romolo, F.S., & Weyermann, C (2017). Time since discharge of 9 mm cartridges by headspace analysis, part 1: comprehensive optimisation and validation of a headspace sorptive extraction (HSSE) method. Forensic Science International, 272, 159-170. DOI: 10.1016/j.forsciint.2016.12.029

Gallidabino, M., Romolo, F.S., & Weyermann, C (2017). Time since discharge of 9 mm cartridges by headspace analysis, part 2: ageing study and estimation of the time since discharge using multivariate regression. Forensic Science International, 272, 171-183. DOI: 10.1016/j.forsciint.2016.12.027

PhD saved successfully
View saved PhDs