A charge-free and membrane-free hybrid capacitive mixing (H-CapMix) system for salinity gradient energy harvesting with EDA-YP80F as a capacitive anode for Cl⁻ absorption and FeHCF as a battery-type cathode for Na⁺ incorporation.

Natural evaporation has recently come under consideration as a viable source of renewable energy. Demonstrations of the validity of the concept have been reported for devices incorporating carbon-based nanocomposite materials. In this study, we investigated the possibility of using polymer thin films to generate electricity from natural evaporation. We considered a polymeric system based on polyvinylidene fluoride (PVDF). Porous PVDF films were created by incorporating a variety of nanocomposite materials into the polymer structure through a simple mixing procedure. Three nanocomposite materials were considered: carbon nanotubes, graphene oxide, and silica. The evaporation-induced electricity generation was confirmed experimentally under various ambient conditions. Among the nanocomposite materials considered, mesoporous silica (SBA-15) was found to outperform the other two materials in terms of open-circuit voltage, and graphene oxide generated the highest short-circuit current. It was found that the nanocomposite material content in the PVDF film plays an important role: on the one hand, if particles are too few in number, the number of channels will be insufficient to support a strong capillary flow; on the other hand, an excessive number of particles will suppress the flow due to excessive water absorption underneath the surface. We show that the device can be modeled as a simple circuit powered by a current source with excellent agreement between the theoretical predictions and experimental data.

Machine learning is increasingly applied to midstream oil and gas pipelines, yet reported performance rarely establishes readiness for field use. This narrative review organizes peer-reviewed and industry sources from 2015–2025 using a five-level operational maturity framework (sensing and detection, diagnosis, prediction, decision and optimization, and human-supervised closed-loop automation), defined by a rubric of decision authority, operator role, and the validation evidence each level requires. A second axis records where each study’s performance was demonstrated, from simulation to closed-loop use. Applications include leak and anomaly detection, defect and corrosion assessment, hard spots, pump cavitation, predictive maintenance, failure-risk modeling, scheduling, and digital twins. Cross-classifying the coded sources on both axes shows that validation realism does not keep pace with claimed operational responsibility. Peer-reviewed evaluations are concentrated at Levels 1–3 and rely largely on laboratory, simulated, or retrospective data; the only operational Level 4 example is a practitioner case, and no coded source demonstrates Level 5 closed-loop control. The corpus thus shows a validation gap at the lower levels and an evidence-coverage gap at the upper ones; beyond the performance shown in controlled evaluations, validation, transferability, and governance remain major unresolved barriers to deployment. A ranked agenda led by graduated field validation follows.