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The awarding of this year’s Nobel Prize in Chemistry for the development of lithium-ion batteries was long overdue for a technology that is already providing a vital component of the energy economy.
3D printing is now widely used in aerospace, healthcare, energy, automotive and other industries. Metal printing, in particular, is the fastest growing sector, yet its development presents scientific, technological and economic challenges that must be understood and addressed.
Electronic and photonic devices based on graphene have unique properties, leading to outstanding performance figures of merit. Mastering the integration of this unconventional material into an established semiconductor fabrication line represents a critical step towards commercialization.
The past few years have witnessed significant development in graphene research, yet a number of challenges remain for its commercialization and industrialization. This Comment discusses relevant issues for industrial-scale graphene synthesis, one of the critical aspects for the future graphene industry.
Oxides of non-magnetic cations exhibit elusive signs of weak temperature-independent ferromagnetism. The effect is associated with surface defects, but it defies conventional explanation. Possible hypotheses are a spin-split defect impurity band, or giant orbital paramagnetism related to zero-point vacuum fluctuations.
Highly quantitative, robust, single-cell analyses can help to unravel disease heterogeneity and lead to clinical insights, particularly for complex and chronic diseases. Advances in computer vision and machine learning can empower label-free cell-based diagnostics to capture subtle disease states.
Machine learning is swiftly infiltrating many areas within the healthcare industry, from diagnosis and prognosis to drug development and epidemiology, with significant potential to transform the medical landscape.
At the recent Artificial Intelligence Applications in Biopharma Summit in Boston, USA, a panel of scientists from industry who work at the interface of machine learning and pharma discussed the diverging opinions on the past, present and future role of AI for ADME/Tox in drug discovery and development.