Hillhead Review

University of Glasgow projects secures two Converge Awards

The organisation rewards “the pinnacle of Scottish academic innovation” permitting LotusDX to win the KickStart Impact Award worth £5,000, LotusDX was developed by two PhD students Parisa Dehghani and Parisa Dehghani out of a basement at the University. Their invention permits a simple, at-home sexually transmitted infection (STI) test. One which is discreet and reliable. 

The University of Glasgow also produced the joint winner of the Hydrogen Challenge Prize worth £10,000, Payam Bozorgi, for their project Novo Hydrogen. A project on the development of green hydrogen which is economical, efficient and strives to overcome costly methods of proton exchange electrolysis. The Hydrogen Challenge is a new category challenge created in partnership with the Royce Hydrogen Accelerator, which is supported by the Henry Royce Institute. The focus here is primarily on commercialisation, an encouraged idea within the green sector. 

LeverHulme Trust Winner advances sustainable materials

Professor Draper’s work focuses on the development of more sustainable and cheaper organic alternative materials to replace the metal used in traditional devices.

Professor Draper said: “It is precisely this kind of flexible, forward-looking, and trust-based support that will allow me to take the intellectual and technical risks required to deliver genuine transformation, both in my own work and in the broader landscape of organic electronics. 

“Our goal is not only to make progress in our own devices, but to develop a standardised, accessible approach for the entire field.”

Offering exciting developments for textiles at the University of Glasgow, similarly Glasgow “SUSTAIN” engineers are now a part of a £1OM 5 year commitment transforming textiles into electronics which users may interact with by wearing them. 

The project’s Lead said: “This is the first time researchers in electronics, sustainability, and textiles have joined forces from the beginning of the design process, so everything we develop will take all needs into account from the early stages.” 

Laser breakthroughs, 3D microscopes and AI biomolecular language 

Smaller, cheaper, more easily made technologies are what is driving innovation forward. University of Glasgow researchers have designed a “narrow-linewidth laser on a single, fully integrated microchip that achieves the best performance ever recorded in semiconductor lasers of its type.”

Compact design and top balance performance are often at crossroads, meaning that hybrid integration permitted the creation of MOIL-TISE which is now capable of producing a narrower and purer than before laser light which is regarded as superior to any and all previously distributed feedback laser systems.MOIL-TISE system is capable of producing a linewidth of just 983Hz, a significant advance on monolithic DFB lasers currently on the market.

But what does it all mean? Well the inventors say that this will permit “unbreakable encryption and communication devices of the future” a comforting thought in the time of data leaks and breaches in encrypted security. 

More great news ahead – as we speak a new powerful 3D microscope, which is capable of achieving more detailed insights into cancer cells, is being installed at the University of Glasgow. It is the Stellaromics Pyxa™ 3D Spatial Transcriptomics System.

The 3D microscope will offer a detailed look not only at tumours at a standard  cellular level but also enable visualisation of molecules within cells. Permitting new insights into a range of cancers including glioblastoma, pancreatic cancer, and colorectal cancer. Promising scientists better and more accurate information, beyond what can be achieved in thin tissue sections.

All while this is going on, a supercomputer normally used by astronomers to study the universe has been retasked to develop a new machine learning model to help translate the language of protein interactions – to predict how mutation occurs and even how these molecules “talk” to one another. 

The research team led by Dr Ke Yuan from the University’s School of Cancer Sciences shares intel on how important this research is, with its potential stretching as far as being able to identify drug targets or even predict a virus’s pandemic potential at an unprecedented scale and level of accuracy. The Protein Language Model outperforms competing models. Other protein AI tools, including the Google DeepMind-powered AlphaFold3, were only able to predict one of the five protein-to-protein interactions.