Coatings based on titanium nitrides, titanium carbides and silicon carbides can optimize the surface properties of titanium or silicon for various applications ranging from biocompatibility to chemical stability and durability. Here, we investigated a high power (100 W) high pulse repetition rate femtosecond laser process (λ=1030 nm, τ=750 fs, f=1 MHz) for the treatment of titanium and silicon in atmospheres of argon, nitrogen, methane, ethene and acetylene. In a nitrogen atmosphere, a homogeneous coating of TiON is formed on titanium. In an ethene/argon atmosphere coatings of TiOC and SiC are formed on Ti and Si, respectively. The process allows a fast surface transformation with a process rate of 0.33 cm2 s−1 and a high spatial resolution below 0.5 mm with a minimal heat affected zone at the same time. In contrast to low repetition rate femtosecond laser processed samples, the surfaces are more robust against mechanical impact. At the same time, the surfaces reveal a distinct microstructure in comparison to coatings obtained by vapor deposition techniques.

Invited for this month's cover is the group of Prof. Eike G. Hübner at Fraunhofer Heinrich Hertz Institute HHI, Goslar and Clausthal University of Technology, Clausthal‐Zellerfeld, Germany. The cover picture shows a titanium plate, on which the crystal structure (golden circle=Ti, blue circle=O/N/C) of isomorphous TiO, TiN or TiC, respectively, has been engraved by a high‐power high pulse repetition rate femtosecond laser process. The process allows for a fast and spatially resolved surface transformation of titanium to golden TiN, blue TiO/TiO2 or black TiC in an atmosphere of nitrogen, air or ethene/argon. The background represents a typical surface microstructure of these interstitial compounds obtained during this transformation. Read the full text of the article at 10.1002/cplu.202100118.

Redox-active organic molecules, i.e., molecules that can relatively easily accept and/or donate electrons, are ubiquitous in biology, chemical synthesis, and electronic and spintronic devices, such as solar cells and rechargeable batteries, etc. Choosing the best candidates from an essentially infinite chemical space for experimental testing in a target application requires efficient screening approaches. In this Review, we discuss modern in silico techniques for predicting reduction and oxidation potentials of organic molecules that go beyond conventional first-principles computations and thermodynamic cycles. Approaches ranging from simple linear fits based on molecular orbital energy approximation and energy difference approximation to advanced regression and neural network machine learning algorithms employing complex descriptors of molecular compositions, geometries, and electronic structures are examined in conjunction with relevant literature examples. We discuss the interplay between ab initio data and machine learning (ML), i.e., whether it is better to base predictions on low-level quantum-chemical results corrected with ML or to bypass first-principles computations entirely and instead rely on elaborate deep learning architectures. Finally, we list currently available data sets of redox-active organic molecules and their experimental and/or computed properties to facilitate the development of screening platforms and rational design of redoxactive organic molecules.

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