News & Comment

Donata Iandolo and Gonçalo Silva on the need for an open discussion on mental health in academia.

We are launching a new column to talk about science with our readers from a broader perspective. Our first topic is mental health in academia.

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.

Synergy between materials and methods is helping to address open questions in magnetism and superconductivity.

2D materials face challenges along the road to commercialization, with increasing efforts being made in order to satisfy industrial needs.

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.

Rapid progress in machine learning is enabling opportunities for improved clinical decision support. Importantly, however, developing, validating and implementing machine learning models for healthcare entail some particular considerations to increase the chances of eventually improving patient care.

The redefinition of SI units removes materiality from science’s weights and measures. There’s logic to the decision, but it reminds us what we still don’t know about nature’s scales.

Nian Sun, a professor at Northeastern University (Electrical and Computer Engineering Department), talks to Nature Materials about the potential applications of multiferroic materials, and issues associated with commercializing these technologies.

Much academic and industrial effort has been devoted to the study of multiferroics, but if related technologies are to have real-world impact, market awareness and reproducibility are also key.

Topological structures have considerable potential in nanoelectronics and new device concepts. They are key to the design and understanding of novel functionalities in ferroic materials — that is, materials that have one or more types of built-in order such as magnetic, ferroelectric, ferroelastic and multiferroic materials.

As the periodic table reaches the age of 150, we reflect on the historical search for new elements, and consider element usage trends in some key research fields.

The 2018 Nobel prizes in chemistry and medicine celebrate tools for engineering biological materials.

This Comment describes some of the common pitfalls encountered in deriving and validating predictive statistical models from high-dimensional data. It offers a fresh perspective on some key statistical issues, providing some guidelines to avoid pitfalls, and to help unfamiliar readers better assess the reliability and significance of their results.