Artificial Intelligence (AI) is currently experiencing a bloom driven by deep learning (DL) techniques, which rely on networks of connected simple computing units operating in parallel. The low communication bandwidth between memory and processing units in conventional von Neumann machines does not support the requirements of emerging applications that rely extensively on large sets of data. More recent computing paradigms, such as high parallelization and near-memory computing, help alleviate the data communication bottleneck to some extent, but paradigm- shifting concepts are required. Memristors, a novel beyond-complementary metal-oxide-semiconductor (CMOS) technology, are a promising choice for memory devices due to their unique intrinsic device-level properties, enabling both storing and computing with a small, massively-parallel footprint at low power. Theoretically, this directly translates to a major boost in energy efficiency and computational throughput, but various practical challenges remain. In this work we review the latest efforts for achieving hardware-based memristive artificial neural networks (ANNs), describing with detail the working principia of each block and the different design alternatives with their own advantages and disadvantages, as well as the tools required for accurate estimation of performance metrics. Ultimately, we aim to provide a comprehensive protocol of the materials and methods involved in memristive neural networks to those aiming to start working in this field and the experts looking for a holistic approach.

Memristor, based on the principle of biological synapse, is recognized as one of the key devices in confronting the bottleneck of classical von Neumann computers. However, conventional memristors are difficult to continuously adjust the conduction and dutifully mimic the biosynapse function. Here, TiO₂ films with self‐assembled Ag nanoclusters implemented by gradient Ag dopant are employed to achieve enhanced memristor performance. The memristors exhibit gradual both potentiating and depressing conduction under positive and negative pulse trains, which can fully emulate excitation and inhibition of biosynapse. Moreover, comprehensive biosynaptic functions and plasticity, including the transition from short‐term memory to long‐term memory, long‐term potentiation and depression, spike‐timing‐dependent plasticity, and paired‐pulse facilitation, are implemented with the fabricated memristors in this work. The applied pulses with a width of hundreds of nanoseconds timescale are beneficial to realize fast learning and computing. High‐resolution transmission electron microscopy observations clearly demonstrate that Ag clusters redistribute to form Ag conductive filaments between Ag and Pt electrode under electrical field at ON‐state device. The experimental data confirm that the oxides doped with Ag clusters have the potential for mimicking biosynaptic behavior, which is essential for the further creation of artificial neural systems.

Water shortage is one of the most concerning global challenges in the 21st century. Solar‐inspired vaporization employing photothermal nanomaterials is considered to be a feasible and green technology for addressing the water challenge by virtue of abundant and clean solar energy. 2D nanomaterials aroused considerable attention in photothermal evaporation‐induced water production owing to their large absorption surface, strong absorption in broadband solar spectrum, and efficient photothermal conversion. Herein, the recent progress of 2D nanomaterials‐based photothermal evaporation, mainly including emerging Xenes (phosphorene, antimonene, tellurene, and borophene) and binary‐enes (MXenes and transition metal dichalcogenides), is reviewed. Then, the optimization strategies for higher evaporation performance are summarized in terms of modulation of the intrinsic photothermal performance of 2D nanomaterials and design of the complete evaporation system. Finally, the challenges and prospective of various kinds of 2D photothermal nanomaterials are discussed in terms of the photothermal performance, stability, environmental influence, and cost. One important principle is that solutions for water challenges should not introduce new environmental and social problems. This Review aims to highlight the role of 2D photothermal nanomaterials in solving water challenges and provides a viable scheme toward the practical use in photothermal materials selection, design, and evaporation systems building.