Machine Learning Controlled Synthesis of Atomically Thin Gold Nanomaterials for the Development of Quantified Lateral Flow Devices

In this project, we will develop machine-learning approaches for the controlled synthesis of novel 2D nanomaterials. Optical properties and morphologies will be used to create a feedback mechanism to control the production of materials with the desired optical properties. <br /> <br /> Once produced, the gold nanomaterials will be functionalised for testing in quantitative lateral flow tests (LFTs). We will use them to detect the bacteria involved in urinary tract infections. The LFTS will be tested on model and patient samples and validated through collaboration with Medicine. The LFTS will be optimised for the simultaneous detection of multiple species of bacteria to enhance the accuracy of diagnosis. We will also develop a quantitative method of characterising the LFT to allow reading on mobile devices. You will work with a multidisciplinary team across physical, biological and medical sciences and with our industrial partner Surescreen Diagnostics.

<p style="text-align:justify"><strong>Background. </strong></p> <p style="text-align:justify">We have developed a low-temperature, aqueous-based method for the synthesis of 2D Au nanosheets (AuNS). The formation is controlled via the reduction of a Au salt in the presence of methyl orange (MO). Our preliminary studies have shown that the MO concentration plays a key role in controlling AuNX morphology, with low MO concentrations leading to quasi-1D tapes (AuNT) and 2D sheets (AuNS) whilst higher concentrations lead to the formation of nano-pinecones (AuNPC). In all cases, the AuNX have a very high surface-to-volume ratio and strong plasmonic absorbance in the near infra-red (NIR). The material, the world&rsquo;s thinnest free-standing gold, it is flexible and has demonstrated superior catalytic and enzymatic performance over gold nanoparticles per unit mass.[1] However, such materials could have a much broader range of applications. In this project, we aim to establish: i) a deeper understanding of the growth mechanism, including how to control the lateral dimensions of the AuNS using novel AI approaches, ii) the ability to functionalise them with organic ligands and iii) quantify their use in lateral flow devices for testing bacterial infections.</p> <p><strong>Project description</strong></p> <p>We will extend our recent work to determine the key factors affecting (AuNX synthesis to better understand the formation mechanism and to develop better control over size, morphology, and yield.[2] In particular, we will look at the role of time, temperature and reagent concentrations in determining the nanoparticle (size, morphology and yield). The primary characterisation will be via UV-vis Spectroscopy, TEM (CTEM) and scanning TEM (STEM) (with the monochromated Titan Themis TEM/STEM at 300 kV), dynamic light scattering (DLS).</p> <p>We are interested in developing a supervised machine learning (ML) approach to assist the production/morphological control of 2D AuNX. This will be undertaken using a droplet microfluidics approach. Different supervised ML approaches will be compared, including linear regression, deep neural networks, and random forest. ML approaches have been applied to controlling 3D nanoparticle synthesis [3-6]. This project would be the first implementation for controlling 2D Au nanomaterial production. Optimised conditions will be used to scale up production for subsequent bioconjugation and testing. Note we will prioritise our effort on AuNPC&gt;AuNT&gt;AuNS based on our preliminary data and high yield in synthesis.</p> <p>The Covid-19 pandemic brought lateral flow tests (LFTs) to the forefront of public awareness as a rapid diagnostic tool for testing one&rsquo;s state of infection. LFTs typically consist of one, or two, &lsquo;test&rsquo; lines plus a &lsquo;control&rsquo; line. The &lsquo;test&rsquo; lines are usually functionalised with a capture antibody specific for a biomarker indicative of the infectious agent, whilst the &lsquo;control&rsquo; line typically detects any protein/antibody and is used to demonstrate the proper functioning of the LFT. In this project, we will develop LFTs for detecting bacteria involved in urinary tract infections. Optimisation of antibody loading and AuNX concentration control sensitivity levels and we want to detect earlier stages of infection. The LFTs need to be optimised for any given application, and their sensitivity and specificity evaluated in an iterative process involving; i) antibody /Affimer AuNX conjugation; ii) test and control line conjugation; iii) flow control in nitrocellulose membrane; iv) sample, conjugate and wick pad treatments and loading capacities; v) running buffer; and vii) analysis/readout (including sensitivity and specificity).</p> <p>Two LFT formats will initially be considered for testing a single bacterial species before moving to a multiplexed format; these will be guided by collaboration with clinical partners. We will work closely with our industrial partner (SureScreen Diagnostics). The first will be a conventional LFTS, whilst the second test device design will be a &lsquo;dip&rsquo; test-based LFT, where the components (AuNX, running buffer and wetting agents) are in the bottom of a vial, and upon the addition of a specified volume of sample, the addition of the top will &lsquo;dip&rsquo; the strip to a predetermined depth to perform the test in a sealed environment. Devices will be tested/optimised initially on multi-purpose artificial urine with added bacterial components for testing over the clinically relevant concentrations before moving onto patient samples. We will develop algorithms for the quantitative read-out of LFTs suitable for use with mobile phone cameras.</p> <p style="text-align:justify"><strong>References</strong></p> <ol> <li>Ye et al. Adv.Science 2019, DOI:10.1002/advs.201900911.&nbsp;</li> <li>Fox J, et al. <em>J Phys Chem C Nanomater Interfaces</em> 2023;127(6):3067-76. doi: 10.1021/acs.jpcc.2c07582</li> <li>Tao H, et al., <em>Nature Reviews Materials</em> 2021;6(8):701-16.</li> <li>&nbsp;Salley D, et al., <em>Nat Commun</em> 2020;11(1):2771.</li> <li>Mekki-Berrada F, et al., <em>npj Computational Materials</em> 2021;7(1).</li> <li>Tominaga T. <em>J Microbiol Methods</em> 2019;160:29-35.</li> </ol> <p style="text-align:justify">&nbsp;</p>

<p>Formal applications for research degree study should be made online through the&nbsp;<a href="https://www.leeds.ac.uk/research-applying/doc/applying-research-degrees">University&#39;s website</a>. Please state clearly in the Planned Course of Study section that you are applying for <strong><em>PhD Physics &amp; Astronomy</em></strong> and in the research information section&nbsp;that the research degree you wish to be considered for is <em><strong>Machine Learning Controlled Synthesis of Atomically Thin Gold Nanomaterials for the Development of Quantified Lateral Flow Devices</strong></em> as well as&nbsp;<a href="https://eps.leeds.ac.uk/physics/staff/4107/professor-stephen-evans">Professor Stephen Evans</a> as your proposed supervisor.</p> <p>If English is not your first language, you must provide evidence that you meet the University&#39;s minimum English language requirements (below).</p> <p><em>As an international research-intensive university, we welcome students from all walks of life and from across the world. We foster an inclusive environment where all can flourish and prosper, and we are proud of our strong commitment to student education. Across all Faculties we are dedicated to diversifying our community and we welcome the unique contributions that individuals can bring, and particularly encourage applications from, but not limited to Black, Asian, people who belong to a minority ethnic community, people who identify as LGBT+ and people with disabilities. Applicants will always be selected based on merit and ability.</em></p> <p class="MsoNoSpacing">Applications will be considered on an ongoing basis. &nbsp;Potential applicants are strongly encouraged to contact the supervisors for an informal discussion before making a formal application. &nbsp;We also advise that you apply at the earliest opportunity as the application and selection process may close early, should we receive a sufficient number of applications or that a suitable candidate is appointed.</p> <p>Please note that you must provide the following documents in support of your application by the closing date of 1 June 2023:</p> <ul> <li>Full Transcripts of all degree study or if in final year of study, full transcripts to date</li> <li>Personal Statement outlining your interest in the project</li> <li>CV</li> <li>Funding information:&nbsp; School of Physics &amp; Astronomy (EPSRC REFUTE Grant)</li> </ul>

Applicants to research degree programmes should normally have at least a first class or an upper second class British Bachelors Honours degree (or equivalent) in an appropriate discipline. The criteria for entry for some research degrees may be higher, for example, several faculties, also require a Masters degree. Applicants are advised to check with the relevant School prior to making an application. Applicants who are uncertain about the requirements for a particular research degree are advised to contact the School or Graduate School prior to making an application.

The minimum English language entry requirement for research postgraduate research study is an IELTS of 6.0 overall with at least 5.5 in each component (reading, writing, listening and speaking) or equivalent. The test must be dated within two years of the start date of the course in order to be valid. Some schools and faculties have a higher requirement.

<p class="MsoNoSpacing">A highly competitive School of Physics &amp; Astronomy Studentship in support of the EPSRC REFUTE Grant, consisting of the award of fees at the UK fee rate together with a maintenance grant (currently &pound;17,668 for session 2022/23) for 3.5 years.</p> <p>This opportunity is open to UK applicants only. All candidates will be placed into the School of Phsyics &amp; Astronomy Studentship Competition and selection is based on academic merit.<br /> <br /> Please refer to the&nbsp;<a href="https://www.ukcisa.org.uk/">UKCISA</a>&nbsp;website for&nbsp;information regarding Fee Status for Non-UK Nationals.</p>

<p>For further information about this project, please contact Professor Stephen Evans<br /> e:&nbsp;<a href="mailto:s.d.evans@leeds.ac.uk">s.d.evans@leeds.ac.uk</a></p> <p>For further information about your application, please contact Doctoral College Admissions<br /> e:&nbsp;<a href="mailto:maps.pgr.admissions@leeds.ac.uk">maps.pgr.admissions@leeds.ac.uk</a></p>