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Verified CAS / Academic Author2 Decoded Studies

Prof. Tao JING

National Center for Nanoscience and Technology, Chinese Academy of Sciences

Co-Affiliations:State Key Laboratory of Multiphase Flow in Power Engineering, Xi'an Jiaotong University

Research Publications & English Decoded Briefs

Showing 2 publications
SCIENCE CHINA Materials2026DOI: 10.1007/s40843-026-4483-x

Narrow-Bandgap Acceptors with Low Energetic Disorder Achieve over 21% Efficiency in Organic Solar Cells

The referenced literature comprises five peer-reviewed studies published between 2025 and 2026 in Nature Materials, Journal of the American Chemical Society, Nature Communications, and Science China Materials. These works collectively address the persistent trade-off between open-circuit voltage (Voc) and short-circuit current density (Jsc) in organic photovoltaics (OPVs). Tao et al. (Nat Mater, 2026) demonstrate that narrow-bandgap nonfullerene acceptors engineered to exhibit low energetic disorder achieve power conversion efficiencies (PCEs) exceeding 21%, primarily by suppressing non-radiative recombination losses. Westbrook et al. (JACS, 2025) establish that solid-state packing motifs govern exciton delocalization and photophysics in nonfullerene acceptors, providing a structural handle for reducing energetic disorder. Jiang et al. (Nat Commun, 2025) show that photoluminescent delocalized excitons in donor polymers facilitate efficient charge generation, linking exciton coherence to device performance. Zhang et al. (Nat Commun, 2026) employ synergistic steric hindrance and chlorination to realize binary OSCs with low energy loss, achieving high Voc without sacrificing photocurrent. The cumulative findings indicate that molecular design strategies targeting low energetic disorder and controlled solid-state packing can overcome the longstanding efficiency ceiling of ~20% in OPVs. These results have direct implications for the commercial viability of solution-processed, lightweight, and flexible solar cells, though scalability and long-term stability remain to be validated under industrial manufacturing conditions.

The Chinese Journal of Process Engineering2026DOI: 10.12034/j.issn.1009-606X.225184

A Review on Energy-Saving and Consumption-Reducing Technologies for Thermal Power Units Based on Economic Benefit Evaluation

Thermal power units have long dominated China's energy structure due to the low cost of coal and their role in ensuring grid stability. However, under the dual pressures of climate change and national carbon peaking/neutrality goals, the environmental impact of their 'three wastes' has become critical, necessitating energy-saving retrofits. This review systematically examines mainstream energy-saving technologies for thermal power units, including boiler combustion optimization, heating surface cleaning, turbine flow path upgrades, waste heat recovery and cascade utilization, and cold-end system optimization. Using coal consumption rate as the core economic index, the study integrates case studies and operational data from typical domestic and international units to evaluate the latest progress, practical effects, advantages, and limitations of each technology. Results indicate that these technologies significantly improve energy efficiency and reduce pollution. For instance, boiler combustion optimization based on support vector machines and neural networks enhances thermal efficiency and reduces NOx emissions. Turbine flow path modifications, from full three-dimensional CFD optimization to advanced blades and combined steam seals, yield notable gains in cylinder efficiency and heat rate reduction. Low-temperature economizers reduce coal consumption and auxiliary power/water use in dust removal and desulfurization systems. Heat pump applications include absorption, compression, and hybrid types. In cold-end optimization, data-driven predictive maintenance and real-time performance tuning of condensers achieve nearly 50% energy savings in circulating water pumps and an average coal consumption reduction of 2-3 g/(kW·h). Despite these advances, gaps remain in multi-objective optimization robustness, intelligent diagnosis, and advanced materials. Future research should focus on deep reinforcement learning for adaptive control, sensor networks for real-time diagnostics and predictive maintenance, and high-temperature corrosion-resistant materials for heat exchangers, while balancing initial investment and maintenance costs.