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In methanol-to-hydrocarbon chemistry, methanol and dimethyl ether (DME) can act as methylating agents. Therefore, we focus on the different reactivity of methanol and DME towards benzene methylation in H-ZSM-5 at operating conditions by combining first principles microkinetic modeling and experiments. Methylation reactions are known to follow either a concerted reaction path or a stepwise mechanism going through a framework-bound methoxide. By constructing a DFT based microkinetic model including the concerted and stepwise reactions, product formation rates can be calculated at conditions that closely mimic the experimentally applied conditions. Trends in measured rates are relatively well reproduced by our DFT based microkinetic model. We find that benzene methylation with DME is faster than with methanol but the difference decreases with increasing temperature. At low temperatures, the concerted mechanism dominates, however at higher temperatures and low pressures the mechanism shifts to the stepwise pathway. This transition occurs at lower temperatures for methanol than for DME, resulting in smaller reactivity differences between methanol and DME at high temperature. Our theory-experiment approach shows that the widely assumed rate law with zeroth and first order in oxygenate and hydrocarbon partial pressure is not generally applicable and depends on the applied temperature, pressure and feed composition. [ABSTRACT FROM AUTHOR]

Descriptor-based analysis is a powerful tool for understanding the trends across various catalysts. In general, the rate of a reaction over a given catalyst is a function of many parameters-reaction energies, activation barriers, thermodynamic conditions, etc. The high dimensionality of this problem makes it very difficult and expensive to solve completely, and even a full solution would not give much insight into the rational design of new catalysts. The descriptor-based approach seeks to determine a few 'descriptors' upon which the other parameters are dependent. By doing this it is possible to reduce the dimensionality of the problem-preferably to 1 or 2 descriptors-thus greatly reducing computational efforts and simultaneously increasing the understanding of trends in catalysis. The 'CatMAP' Python module seeks to standardize and automate many of the mathematical routines necessary to move from 'descriptor space' to reaction rates for heterogeneous (electro) catalysts. The module is designed to be both flexible and powerful, and is available for free online. A 'reaction model' can be fully defined by a configuration file, thus no new programming is necessary to change the complexity or assumptions of a model. Furthermore, various steps in the process of moving from descriptors to reaction rates have been abstracted into separate Python classes, making it easy to change the methods used or add new functionality. This work discusses the structure of the code and presents the underlying algorithms and mathematical expressions both generally and via an example for the CO oxidation reaction. Graphical Abstract: [Figure not available: see fulltext.] [ABSTRACT FROM AUTHOR]

With continuously shrinking feature sizes of integrated circuits, the vast majority of semiconductor companies have become fabless , outsourcing to foundries across the globe. This exposes the design industry to a number of threats, including piracy via IP-theft or unauthorized overproduction and subsequent reselling on the black market. One alleged solution for this problem is logic locking , where the genuine functionality of a chip is "locked" using a key only known to the designer. Solely with a correct key, the design works as intended. Since unlocking is handled by the designer only after production, an adversary in the supply chain should not be able to unlock overproduced chips. In this work, we focus on logic locking against the threat of overproduction. First, we survey existing locking schemes and characterize them by their handling of keys, before extracting similarities and differences in the employed attacker models. We then compare said models to the real-world capabilities of the primary adversary in overproduction-a malicious foundry. This comparison allows us to identify pitfalls in existing models and derive a more realistic attacker model. Then, we discuss how existing schemes hold up against the new attacker model. Our discussion highlights that several attacks beyond the usually employed SAT-based approaches are viable. Crucially, these attacks stem from the underlying structure of current logic locking approaches, which has never changed since its introduction in 2008. We conclude that logic locking, while being a promising approach, needs a fundamental rethinking to achieve real-world protection against overproduction., Competing Interests: Conflict of interestThe authors declare that they have no conflict of interest., (© The Author(s) 2022.)

We present a new open repository for chemical reactions on catalytic surfaces, available at https://www.catalysis-hub.org . The featured database for surface reactions contains more than 100,000 chemisorption and reaction energies obtained from electronic structure calculations, and is continuously being updated with new datasets. In addition to providing quantum-mechanical results for a broad range of reactions and surfaces from different publications, the database features a systematic, large-scale study of chemical adsorption and hydrogenation on bimetallic alloy surfaces. The database contains reaction specific information, such as the surface composition and reaction energy for each reaction, as well as the surface geometries and calculational parameters, essential for data reproducibility. By providing direct access via the web-interface as well as a Python API, we seek to accelerate the discovery of catalytic materials for sustainable energy applications by enabling researchers to efficiently use the data as a basis for new calculations and model generation.

Neural stem cells (NSCs) and imprinted genes play an important role in brain development. On historical grounds, these two determinants have been largely studied independently of each other. Recent evidence suggests, however, that NSCs can reset select genomic imprints to prevent precocious depletion of the stem cell reservoir. Moreover, imprinted genes like the transcriptional regulator Zac1 can fine tune neuronal vs astroglial differentiation of NSCs. Zac1 binds in a sequence-specific manner to pro-neuronal and imprinted genes to confer transcriptional regulation and furthermore coregulates members of the p53-family in NSCs. At the genome scale, Zac1 is a central hub of an imprinted gene network comprising genes with an important role for NSC quiescence, proliferation and differentiation. Overall, transcriptional, epigenomic, and genomic mechanisms seem to coordinate the functional relationships of NSCs and imprinted genes from development to maturation, and possibly aging.