Understanding the water splitting mechanism in photocatalysis is a rewarding goal as it will allow producing clean fuel for a sustainable life in the future. However, identifying the photocatalytic mechanisms by modeling photoactive nanoparticles requires sophisticated computational techniques based on multiscale modeling. In this review, we will survey the strengths and drawbacks of currently available theoretical methods at different length and accuracy scales. Understanding the surface-active site through Density Functional Theory (DFT) using new, more accurate exchange-correlation functionals plays a key role for surface engineering. Larger scale dynamics of the catalyst/electrolyte interface can be treated with Molecular Dynamics albeit there is a need for more generalizations of force fields. Monte Carlo and Continuum Modeling techniques are so far not the prominent path for modeling water splitting but interest is growing due to the lower computational cost and the feasibility to compare the modeling outcome directly to experimental data. The future challenges in modeling complex nano-photocatalysts involve combining different methods in a hierarchical way so that resources are spent wisely at each length scale, as well as accounting for excited states chemistry that is important for photocatalysis, a path that will bring devices closer to the theoretical limit of photocatalytic efficiency.

Challenges of modeling nanostructured materials for photocatalytic water splitting.

https://doi.org/10.1080/14686996.2022.2162323

A review of memristor: material and structure design, device performance, applications and prospects

Splitting of water with the help of photocatalysts has gained a strong interest in the scientific community for producing clean energy, thus requiring novel semiconductor materials to achieve high-yield hydrogen production. The emergence of 2D nanoscale materials with remarkable electronic and optical properties has received much attention in this field. Owing to the recent developments in high-end computation and advanced electronic structure theories, first principles studies offer powerful tools to screen photocatalytic systems reliably and efficiently. This review is organized to highlight the essential properties of 2D photocatalysts and the recent advances in the theoretical engineering of 2D materials for the improvement in photocatalytic overall water-splitting. The advancement in the strategies including (i) single-atom catalysts, (ii) defect engineering, (iii) strain engineering, (iv) Janus structures, (v) type-II heterostructures (vi) Z-scheme heterostructures (vii) multilayer configurations (viii) edge-modification in nanoribbons and (ix) the effect of pH in overall water-splitting are summarized to improve the existing problems for a photocatalytic catalytic reaction such as overcoming large overpotential to trigger the water-splitting reactions without using cocatalysts. This review could serve as a bridge between theoretical and experimental research on next-generation 2D photocatalysts.

/pdf/engineering-2d-materials-for-photocatalytic-water-splitting-34razs5p.pdf

Engineering 2D Materials for Photocatalytic Water-Splitting from a Theoretical Perspective

Memristive devices are among the most emerging electronic elements to realize artificial synapses for neuromorphic computing (NC) applications and have potential to replace the traditional von-Neumann computing architecture in recent times. In this work, pulsed laser deposition-manufactured Ag/TiO2/Pt memristor devices exhibiting digital and analog switching behavior are considered for NC. The TiO2 memristor shows excellent performance of digital resistive switching with a memory window of order ∼103. Furthermore, the analog resistive switching offers multiple conductance levels supporting the development of the bioinspired synapse. A possible mechanism for digital and analog switching behavior in our device is proposed. Remarkably, essential synaptic functions such as pair-pulse facilitation, long-term potentiation (LTP), and long-term depression (LTD) are successfully realized based on the change in conductance through analog memory characteristics. Based on the LTP-LTD, a neural network simulation for the pattern recognition task using the MNIST data set is investigated, which shows a high recognition accuracy of 95.98%. Furthermore, more complex synaptic behavior such as spike-time-dependent plasticity and Pavlovian classical conditioning is successfully emulated for associative learning of the biological brain. This work enriches the TiO2-based resistive random-access memory, which provides information about the simultaneous existence of digital and analog behavior, thereby facilitating the further implementation of memristors in low-power NC.

Bipolar Resistive Switching in TiO2 Artificial Synapse Mimicking Pavlov's Associative Learning.

Neuromorphic systems can parallelize the perception and computation of information, making it possible to break through the von Neumann bottleneck. Neuromorphic engineering has been developed over a long period of time based on Hebbian learning rules. The optoelectronic neuromorphic analog device combines the advantages of electricity and optics, and can simulate the biological visual system, which has a very strong development potential. Low‐dimensional materials play a very important role in the field of optoelectronic neuromorphic devices due to their flexible bandgap tuning mechanism and strong light‐matter coupling efficiency. This review introduces the basic synaptic plasticity of neuromorphic devices. According to the different number of terminals, two‐terminal neuromorphic memristors, three‐terminal neuromorphic transistors and artificial visual system are introduced from the aspects of the action mechanism and device structure. Finally, the development prospect of optoelectronic neuromorphic analog devices based on low‐dimensional materials is prospected.

Advanced Optoelectronic Devices for Neuromorphic Analog Based on Low‐Dimensional Semiconductors

The memristor is a foundational device for an artificial synapse, which is essential to realize next-generation neuromorphic computing. Herein, an optoelectronic memristor based on a two-dimensional (2D) transitional-metal trichalcogenide (TMTC) is designed and demonstrated. Owing to the excellent optical and electrical characteristics of titanium trisulfide (TiS3), the memristor exhibits stable bipolar resistance switching (RS) as a result of the controllable formation and rupturing of the conductive aluminum filaments. Multilevel storage is realized with light of multiple wavelengths between 400 and 808 nm, and the synaptic properties such as conduction modulation and spiking timing-dependent plasticity (STDP) are achieved. On the basis of the photonic potentiation and electrical habitual ability, Pavlovian-associative learning is successfully established on this TiS3-based artificial synapse. All these results reveal the large potential of 2D TMTCs in artificial neuromorphic chips.

Optoelectronic Artificial Synapses Based on Two-Dimensional Transitional-Metal Trichalcogenide.

Constructing van der Waals (vdW) hetero-structure by stacking different two-dimensional (2D) materials has become an effective method for designing new-type and high-quality electronic and optoelectronic nano-devices. In this work, we designed a 2D As/BlueP vdW hetero-structure by stacking monolayer arsenene (As) and monolayer blue phosphorous (BlueP) vertically, which were recently implemented in experiments, and investigated its structural, electronic, and photocatalytic water splitting properties by using the standard first principles calculation method with HSE06 hybrid exchange-correlation functional. Numerical results show that the As/BlueP vdW hetero-structure is structural robust, even at room temperature. It presents semi-conducting behavior, and the conduction band minimum (CBM) and the valence band maximum (VBM) are dominated by BlueP and As, respectively. The typical type-II band alignment predicts the potential application of the hetero-structure in highly efficient optoelectronics and solar energy conversion. Moreover, the CBM and the VBM straddle the redox potentials of water in acid environment, predicting the possibility of the As/BlueP hetero-structure as a 2D photocatalyst for water splitting. When an in-plane strain is applied, the band edges and, further, the optoelectronic properties of the hetero-structure can be effectively tuned. Especially, when tensile strain is equal to 4.5%, the optical absorption spectrum is effectively broadened in a visible light region, which will largely improve its photocatalytic efficiency, although the pH value of the solution range reduction. This work provides theoretical evidence that the As/BlueP hetero-structure has potential application as a 2D photocatalyst in water splitting.

/pdf/two-dimensional-as-bluep-van-der-waals-hetero-structure-as-a-2aqrzesm2h.pdf

Two-Dimensional As/BlueP van der Waals Hetero-Structure as a Promising Photocatalyst for Water Splitting: A DFT Study

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Optoelectronic Artificial Synapses Based on Two-Dimensional Transitional-Metal Trichalcogenide.

Two-Dimensional As/BlueP van der Waals Hetero-Structure as a Promising Photocatalyst for Water Splitting: A DFT Study