Advances in two-dimensional engineered nanomaterials applications for the agro- and food-industries.

Two-dimensional nanomaterials, such as graphene, transition metal dichalcogenides, MXenes, and other layered compounds, are the subject of intense theoretical and experimental research for applications in a wide range of advanced technological solutions, given their outstanding physical, chemical, and mechanical properties. In the context of food science and technology, their contributions are starting to appear, based on the advantages that two-dimensional nanostructures offer to agricultural- and food-related key topics, such as sustainable water use, nano-agrochemicals, novel nanosensing devices, and smart packaging technologies. These application categories facilitate the grasping of the current and potential uses of such advanced nanomaterials in the field, backed by their advantageous physical, chemical, and structural properties. Developments for water cleaning and reuse, efficient nanofertilizers and pesticides, ultrasensitive sensors for food contamination, and intelligent nanoelectronic disposable food packages are among the most promising application examples reviewed here and demonstrate the tremendous impact that further developments would have in the area as the fundamental and applied research of two-dimensional nanostructures continues. We expect this work will contribute to a better understanding of the promising characteristics of two-dimensional nanomaterials that could be used for the design of novel and feasible solutions in the agriculture and food areas. © 2023 Society of Chemical Industry.

Programmable logic construction kits for hyper-real-time neuronal modeling.

Programmable logic designs are presented that achieve exact integration of leaky integrate-and-fire soma and dynamical synapse neuronal models and incorporate spike-time dependent plasticity and axonal delays. Highly accurate numerical performance has been achieved by modifying simpler forward-Euler-based circuitry requiring minimal circuit allocation, which, as we show, behaves equivalently to exact integration. These designs have been implemented and simulated at the behavioral and physical device levels, demonstrating close agreement with both numerical and analytical results. By exploiting finely grained parallelism and single clock cycle numerical iteration, these designs achieve simulation speeds at least five orders of magnitude faster than the nervous system, termed here hyper-real-time operation, when deployed on commercially available field-programmable gate array (FPGA) devices. Taken together, our designs form a programmable logic construction kit of commonly used neuronal model elements that supports the building of large and complex architectures of spiking neuron networks for real-time neuromorphic implementation, neurophysiological interfacing, or efficient parameter space investigations.