Herein, we have successfully constructed a solid-state Z-scheme photosystem with enhanced light absorption capacity by combining the optoelectrical properties of AgNPs with those of the MoS₂/RGO/NiWO₄ (Ag-MRGON) heterostructure. The Ag-MRGON Z-scheme system demonstrates improved photo-electrochemical (PEC) water-splitting performance in terms of applied bias photon-to-current conversion efficiency (ABPE), which is 0.52%, and 17.3- and 4.3-times better than those of pristine MoS₂ and MoS₂/NiWO₄ photoanodes, respectively. The application of AgNPs as an optical property enhancer and RGO as an electron mediator improved the photocurrent density of Ag-MRGON to 3.5 mA/cm² and suppressed the charge recombination to attain the photostability of ∼2 h. Moreover, the photocurrent onset potential of the Ag-MRGON heterojunction (i.e., 0.61 V_RHE) is cathodically shifted compared to those of NiWO₄ (0.83 V_RHE), MoS₂ (0.80 V_RHE), and MoS₂/NiWO₄ heterojunction (0.73 V_RHE). The improved PEC water-splitting performance in terms of ABPE, photocurrent density, and onset potential is attributed to the facilitated charge transfer through the RGO mediator, the plasmonic effect of AgNPs, and the proper energy band alignments with the thermodynamic potentials of hydrogen and oxygen evolution.

The advancement of mircogrids and the adoption of blockchain technology in the energy-trading sector can build a robust and sustainable energy infrastructure. The decentralization and transparency of blockchain technology have several advantages for data management, security, and trust. In particular, the uses of smart contracts can provide automated transaction in energy trading. Individual entities (household, industries, institutes, etc.) have shown increasing interest in producing power from potential renewable energy sources for their own usage and also in distributing this power to the energy market if possible. The key success in energy trading significantly depends on understanding one’s own energy demand and production capability. For example, the production from a solar panel is highly correlated with the weather condition, and an efficient machine learning model can characterize the relationship to estimate the production at any time. In this article, we propose an architecture for energy trading that uses smart contracts in conjunction with an efficient machine learning algorithm to determine participants’ appropriate energy productions and streamline the auction process. We conducted an analysis on various machine learning models to identify the best suited model to be used with the smart contract in energy trading.

Hotspots are high-intensity electromagnetic zones that form, for example, at the interstitials of plasmonic nanoaggregates, resulting in a considerable rise in the enhancement factor. However, it is inevitable to achieve specific nanometric geometry as well as a suitable technique to capture the details of hotspots. We report near-field surface-enhanced Raman scattering (SERS) spectroscopy of a well-defined gold nanoaggregate of a few nanoparticles adsorbed with a small number of target analytes. A spectrally and spatially resolved SERS measurement setup using an aperture near-field scanning optical microscope (a-NSOM) facilitated the direct observation of localized electromagnetic (EM) fields at the interstitials through SERS. Correlated optical image and corresponding nanometric geometry were captured through the home-built a-NSOM setup. Near-field SERS spectra were recorded at different sites of interest. It was evident that the interstitial positioned at the center of the tetramer provided the most intense Raman scattering, implying the possibility of a SERS-active hotspot therein. SERS bands of the spectrum of the Raman-active dye Rhodamine 6G recorded at the same hotspot coincided well with those reported so far. It was noteworthy that most of the SERS bands in such scenery got enhanced. Such direct observation with high spatial resolution is indispensable to understanding the origin of localized EM fields at “hotspots” and the EM enhancement factor in the SERS process. A finite-difference time-domain (FDTD) analysis was carried out to validate the results.