NERC: Jennifer Watts: NE/S007504/1

This research proposal aims to develop advanced power sources that can convert indoor light into electricity to operate electronic sensors for the internet of things (IoT) - an emerging trillion-dollar industry that impacts all human life. The proposed new technology is termed 'indoor photovoltaics'. The technology is based on current organic photovoltaics that can be made flexible, lightweight, rollable, semi-transparent and of different colours at an ultra-low dollar per-watt cost. Using new chemistry principles, photoactive materials design, device engineering, advanced printing and electrical connections, the project aims to deliver fully functional indoor power devices ready for market evaluation. The proposed concept is new and expected to have a broad impact on Canada's and the UK's energy, communication and manufacturing sectors. The proposed chemistries are unique and should lead to paradigm shifts in the view of molecular self-assembly of organic photoactive materials. The ability to fabricate fully printed devices and integrate them into circuits all at once is the key strength of this proposal and serves to immediately validate or invalidate specific materials and/or device designs to ensure objectives are met in a timely fashion. The development of prototypes at the University level enables faster innovations and will allow this technology to bridge the infamous "valley-of-death" laboratory to market transition. The iOPV technology embodies a new paradigm in photovoltaics fabrication using solution-processable materials that can be delivered under ambient conditions (much like ink printed on paper). The simple additive manufacturing process mitigates CO2 production by requiring significantly less energy than traditional lithography-based methods. In addition, the potential for large scale roll-to-roll processing requires only a small capital investment, allowing for localised manufacturing. Printing equipment can tremendously reduce human interaction and the labour required for mass production. Thus, this can promote cost-effective local manufacturing for electronic devices.

Corrosion of metals affects multiple industries and poses major risks to the environment and human safety, and is estimated to cause economic losses in excess of £2.5 trillion worldwide (around 6% of global GDP). Microbiologically-influenced corrosion (MIC) is believed to play a major role in this, but precise estimates are prevented by our limited understanding of MIC-related processes. In the oil and gas sector biocorrosion is usually linked to the problem of "souring" caused by sulfate-reducing bacteria (SRB) that produce corrosive hydrogen sulfide in subsurface reservoirs and topsides facilities. To combat souring, reservoir engineers have begun turning to nitrate injection as a green biotechnology whereby sulfide removal can be catalysed by diverse sulfide-oxidising nitrate-reducing bacteria (soNRB). However, this promising technology is threatened by reports that soNRB could enhance localized corrosion through incomplete oxidation of sulfide to corrosive sulfur intermediates. It is likely that soNRB are corrosive under certain circumstances; end products of soNRB metabolism vary depending prevailing levels of sulfide (i.e., from the SRB-catalyzed reservoir souring) and nitrate (i.e., the engineering "nitrate dose" introduced to combat souring). Furthermore soNRB corrosion will depend on the specific physiological features of the particular strains present, which vary from field to field, but usually include members of the Epsilonproteobacteria - the most frequently detected bacterial phylum in 16S rRNA genomic surveys of medium temperature oil fields. A new era of biological knowledge is dawning with the advent of inexpensive, high throughput nucleic acid sequencing technologies that can now be applied to microbial genomics. New high throughput sequencing platforms are allowing unprecedented levels of interrogation of microbial communities at the DNA (genomic) and RNA (transcriptomic) levels. Engineering biology aims to harness the power of this biological "-omics" revolution by bringing these powerful tools to bear on industrial problems like biocorrosion. This project will combine genomics and transcriptomics with process measurements of soNRB metabolism and real time corrosion monitoring via linear polarization resistance. By measuring all of these variables in experimental oil field microcosms, and scaling-up to pan-industry oil field screening, a predictive understanding of corrosion linked to nitrogen and sulfur biotransformations will emerge, putting new diagnostic genomics assays in the hands of petroleum engineers. The oil industry needs green technologies like nitrate injection. This research will develop new approaches that will safeguard this promising technology by allowing nitrate-associated biocorrosion potential to be assessed in advance. This will enhance nitrate injection's ongoing successful application to be based on informed risk assessments.

EPSRC : Paul Smith : EP/N509498/1 Lipids are biological molecules that have hydrophobic tails and hydrophilic headgroups. Along with proteins, lipids constitute the complex fluid mixture of biological cell membranes. There are hundreds of types of lipids in cell membranes, each with a different combination of tail and headgroup, and many serving important biological functions. The lateral organization of lipids - the way in which different lipid types mix with one another - also serves important but poorly understood biological roles, including providing platforms for transmitting signals across the membrane. Physics-based computer simulations offer a unique opportunity to study biological structures at sub-nanometer resolution. We will use computer simulations to systematically study the effect of curvature on lipid mixing and the local physical properties of the membrane. This will both add to our general understanding of membrane biophysics as well as allow us to better model the behavior of complex biological structures like red blood cells.

ESRC DTP : Veronica Diveica : ES/P00069X/1 "You are mistaken, Mr Darcy, if you suppose that the mode of your declaration affected me in any other way, than as it spared me the concern which I might have felt in refusing you, had you behaved in a more gentleman-like manner." - Jane Austen, Pride and Prejudice The above prose of Pride and Prejudice, a classic tale in the importance of personal character, integrity, and morality, almost exclusively uses words that refer to social behaviours. In storytelling, social words like manner and gentleman help bring forth a sense of humanness, and transport the reader or listener into the perspective of another. Indeed, we encounter social words frequently in our daily lives and they represent almost half of our vocabulary. Their ubiquity in our language and our experience reflects their importance for successfully navigating the social world. Despite this acknowledged importance, psychological research has only recently begun to explore whether social words have a special status in language. Despite the great importance of language to our mental and social lives, we do not yet have a complete cognitive or neural explanation for key aspects of it. In the proposed research, we focus on one such aspect: how we are able to bring to mind the meaning of words. Word meaning retrieval is a key component process in reading, listening, and other skills that involve language understanding. However, much of the previous research has focused upon the processing of concrete nouns, like chair and dog, and much less is known about abstract social words like trust and honour. Therefore, there are major outstanding questions about 'social language', including fundamental question like what it is exactly that defines a word as being 'social', what properties make social words different to others, and how social words are represented by the brain. To begin to answer these questions, we will ask participants to read a set of words and rate them on various dimensions, including the degree to which each word describes a social behaviour, a social institution, or other relevant aspect of the social environment. This will allow us to understand the concept of 'socialness' and how a wide range of words sit on a potential 'socialness' continuum. We will examine this alongside other important dimensions related to word meaning such as such as the degree to which a word conjures an image in the mind's eye, and the degree to which it can have multiple meanings or senses depending on the context. Then we will investigate how the 'socialness' of a word, relative to other dimensions, can influence how fast and how well people process and make subsequent decisions about its meaning. This will help us to build a more complete theory about how word meaning is represented, and about the social brain more generally, which will have implications for the understanding of both language development and the challenges faced by those with language impairments and other social interaction difficulties (e.g., Autism Spectrum Disorder).