This year for the first time we are awarding Outstanding Paper prizes, in recognition of the high-impact work published in the Horizons journals, and the authors behind those articles.
The Horizons journals – Nanoscale Horizonsand Materials Horizons – publish exceptionally high quality, innovative science from top researchers. Each journal has awarded an Outstanding Paper, a runner-up, and an Outstanding Review.
The winning papers were chosen by each journal’s Editorial and Advisory Board members, from a shortlist put together by the editorial team. The shortlist was based on the science presented in each paper, as well as article metrics including downloads, citations, and Altmetric scores.
None of us is the same as all of us: resolving the heterogeneity of extracellular vesicles using single-vesicle, nanoscale characterization with resonance enhanced atomic force microscope infrared spectroscopy (AFM-IR)
Sally Yunsun Kim, Dipesh Khanal, Priyanka Tharkar, Bill Kalionis and Wojciech Chrzanowski, Nanoscale Horiz., 2018, 3, 430–438, DOI: 10.1039/C8NH00048D
All the cells in our body are constantly producing tiny bubbles filled with a cocktail of DNA and other molecules – these function as a 'programme' telling other cells what to do. The bubbles are released into the bloodstream and act as nanosized 'messenger' particles, enabling communications between cells in different parts of the body. Known as extracellular vesicles, they can also be thought of as a fingerprint of cell health.
Professor Wojciech Chrzanoski and his team at the University of Sydney and the University of Melbourne have carried out research that enables them to 'listen in' on the communications between cells, detecting 'complaints' in the messages."By capturing extracellular vesicles and understanding how they are produced and what is inside them – deciphering the messages they contain – we might be able to join cellular conversations and potentially develop new therapeutics or diagnostic tools. Imagine, if we could work out how to write those messages ourselves!"
The team have discovered that they can study extracellular vesicles as a means of early detection of serious medical conditions such as preeclampsia, multiple sclerosis, and Parkinson’s disease. The method is not only useful for diagnosis, but could even be used to develop therapeutic approaches to tackle currently incurable conditions such as chronic obstructive pulmonary disease.
The team is currently moving towards clinical trials and commercialisation of the therapeutic platform for lung degeneration. If it is successful this work will provide the first treatment that enables lung tissue to regenerate and recover its function – something that could be life changing for millions of patients.
“We strongly believe that through the fundamental understanding of ‘cellular language’ we will be able to devise many different therapies as well as detect and treat diseases more effectively and faster.”
Sally Yunsun Kim and Wojciech Chrzanowski are the corresponding authors of the Nanoscale Horizons winning paperPicture:
Bioinspired hierarchical composite design using machine learning: simulation, additive manufacturing, and experiment
Grace X. Gu, Chun-Teh Chen, Deon J. Richmond and Markus J. Buehler, Mater. Horiz., 2018, 5, 939–945, DOI: 10.1039/CiMH00653A
Silk and bone are just two examples of materials made in nature that often far surpass any material that could be engineered in a lab. Naturally-occurring materials are often composites made from a range of different building blocks, and combine different properties such as light weight, high strength and toughness to make a final material that is uniquely suited to its purpose.
Because the structure and composition of such materials can be so complex, it is very difficult to reproduce them. There is a huge range of possible designs, which would be impossible to deduce by trial and error.
Professor Markus Buehler and his team from the Massachusetts Institute of Technology in the US have overcome this design challenge using a combination of artificial intelligence and optimisation techniques.
"In our work, a machine learning model is trained from a large number of computer simulations to learn patterns to generate new high-performing materials", he says. "In the same way as we recognise different objects such as dogs and cats, from our experience of seeing many dogs and cats in our lifetime, models are learning from data how to efficiently evaluate materials' performance.
Grace Gu and Markus Buehler are the corresponding authors of the Materials Horizons winning paper.Picture:
"Similarly to how nature grows and optimises layer-by-layer, this work uses advanced additive manufacturing techniques to fabricate and print the complex materials from algorithms layer-by-layer. The machine learning models are then validated using experimental tension tests of materials created by additive manufacturing."
This approach will make it possible for scientists to design stronger, tougher and lighter material composites from scratch, for use in everything from vehicles to medical implants.