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Grinding and hydrogen-annealing activate ANi₂P₄(A = Ba or Sr) clathrates toward the reduction of nitrate or nitroarenes. Activity and selectivity can be tuned based on the catalyst activation method, particle size, or acid used.

 

The dissemination of sensors is key to realizing a sustainable, ‘intelligent’ world, where everyday objects and environments are equipped with sensing capabilities to advance the sustainability and quality of our lives—e.g. via smart homes, smart cities, smart healthcare, smart logistics, Industry 4.0, and precision agriculture. The realization of the full potential of these applications critically depends on the availability of easy-to-make, low-cost sensor technologies. Sensors based on printable electronic materials offer the ideal platform: they can be fabricated through simple methods (e.g. printing and coating) and are compatible with high-throughput roll-to-roll processing. Moreover, printable electronic materials often allow the fabrication of sensors on flexible/stretchable/biodegradable substrates, thereby enabling the deployment of sensors in unconventional settings. Fulfilling the promise of printable electronic materials for sensing will require materials and device innovations to enhance their ability to transduce external stimuli—light, ionizing radiation, pressure, strain, force, temperature, gas, vapours, humidity, and other chemical and biological analytes. This Roadmap brings together the viewpoints of experts in various printable sensing materials—and devices thereof—to provide insights into the status and outlook of the field. Alongside recent materials and device innovations, the roadmap discusses the key outstanding challenges pertaining to each printable sensing technology. Finally, the Roadmap points to promising directions to overcome these challenges and thus enable ubiquitous sensing for a sustainable, ‘intelligent’ world.

 

Query optimizers are a performance-critical component in every database system. Due to their complexity, optimizers take experts months to write and years to refine. In this work, we demonstrate for the first time that learning to optimize queries without learning from an expert optimizer is both possible and efficient. We present Balsa, a query optimizer built by deep reinforcement learning. Balsa first learns basic knowledge from a simple, environment-agnostic simulator, followed by safe learning in real execution. On the Join Order Benchmark, Balsa matches the performance of two expert query optimizers, both open-source and commercial, with two hours of learning, and outperforms them by up to 2.8× in workload runtime after a few more hours. Balsa thus opens the possibility of automatically learning to optimize in future compute environments where expert-designed optimizers do not exist. 

The application of an external magnetic field of sufficient strength to a spin system composed of a localized singlet can overcome the energy gap and trigger bosonic condensation and so provide an alternative method to realize exotic phases of matter in real materials. Previous research has indicated that a spin Hamiltonian with on-site Kondo coupling may be the effective many-body Hamiltonian for Ba₂NiO₂(AgSe)₂(BNOAS) and here we study such a Hamiltonian using a tensor network ansatz in two dimensions. Our results unveil a phase diagram which indicates the underlying phases of BNOAS. We propose, in response to the possible doping-induced superconductivity of BNOAS, a fermionic model for further investigation. We hope that our discovery can bring up further interest in both theoretical and experimental researches for related nickelate compounds.