Activated carbon (AC) is widely used for organic micro-pollutants (OMPs) removal, yet adsorbability evaluation remains challenging due to molecular diversity and adsorbent heterogeneity, especially given the limitations of traditional assessment metrics such as hydrophobicity (logD). This study proposed a machine learning (ML)-driven assessment strategy by aligning the adsorbability of various AC adsorbents with a hypothetical “Standard AC” to evaluate the adsorbabilities across 56 OMPs. XGBoost, RF, and ET models achieved high prediction accuracy on the test set (R² = 0.88–0.98, RMSE = 0.17–0.38, MAE = 0.13–0.27), and were further validated against a published experimental dataset. Interpretable ML analysis identified a logD threshold of ≈ 2, at which the dominant adsorption mechanisms transitioned from hydrophobic interactions for OMPs with higher hydrophobicity to π-π interactions, hydrogen bonding, and pore-filling for those with lower hydrophobicity. Adsorbability increased with molecular weight, as flexible molecules (rotatable bond ratio > 0.012) overcame steric hindrance in micropores, enhancing pore-filling efficiency through improved accessibility. By introducing a standardized, data-driven adsorbability reference and elucidating the intrinsic interplay between molecular properties and adsorption mechanisms, this study offers a robust framework for knowledge-informed treatability evaluation and a practical benchmark to guide adsorption process design.

Magnonics is a research field that has gained an increasing interest in both the fundamental and applied sciences in recent years. This field aims to explore and functionalize collective spin excitations in magnetically ordered materials for modern information technologies, sensing applications and advanced computational schemes. Spin waves, also known as magnons, carry spin angular momenta that allow for the transmission, storage and processing of information without moving charges. In integrated circuits, magnons enable on-chip data processing at ultrahigh frequencies without the Joule heating, which currently limits clock frequencies in conventional data processors to a few GHz. Recent developments in the field indicate that functional magnonic building blocks for in-memory computation, neural networks and Ising machines are within reach. At the same time, the miniaturization of magnonic circuits advances continuously as the synergy of materials science, electrical engineering and nanotechnology allows for novel on-chip excitation and detection schemes. Such circuits can already enable magnon wavelengths of 50 nm at microwave frequencies in a 5G frequency band. Research into non-charge-based technologies is urgently needed in view of the rapid growth of machine learning and artificial intelligence applications, which consume substantial energy when implemented on conventional data processing units. In its first part, the 2024 Magnonics Roadmap provides an update on the recent developments and achievements in the field of nano-magnonics while defining its future avenues and challenges. In its second part, the Roadmap addresses the rapidly growing research endeavors on hybrid structures and magnonics-enabled quantum engineering. We anticipate that these directions will continue to attract researchers to the field and, in addition to showcasing intriguing science, will enable unprecedented functionalities that enhance the efficiency of alternative information technologies and computational schemes.

A long photoluminescence decay lifetime has been regarded as a generic indication of long charge carrier recombination lifetime in semiconductors such as metal halide perovskites (MHPs), which have shown tremendous success in solar cells. Here, we report that MHP polycrystalline films with extrinsic metal ions have a very long charge recombination lifetime, but a much shorter photoluminescence decay lifetime, and this huge difference can be explained by a model of lateral homojunction within each individual grain. The lateral homojunction is formed due to the doping along grain boundaries by metal ions, and then verified by nanoscale potential mapping and transient photo-response mapping. The built-in electric field within each grain reduces the recombination of free charge carriers within the perovskite grain and along grain boundaries, while the free electrons and holes are collected to cathode and anode through the grain boundaries and grain interiors, respectively. Then, the efficiencies of MHP polycrystalline solar cells are increased.

Industrial bifacial n-type front and back contact (nFAB) silicon solar cells, consisting of a boron-doped p+ emitter and a phosphorus-doped n+ back surface field (BSF), are known to give good bifaciality, high and stabilized efficiency. One possible approach to further enhance the cell efficiency is to convert conventional passivated emitter and rear totally diffused (PERT) into rear locally diffused (PERL) structure. Herein, bifacial nFAB PERT and PERL cells are fabricated by combining atmospheric pressure chemical vapor deposition (APCVD) of phosphosilicate glass (PSG) as doping source and laser processing. For PERL cells, two approaches are studied to locally form phosphorus-doped BSF: 1) laser doping, and 2) laser ablation of a diffusion barrier layer. For ablation approach, an alkaline treatment is introduced immediately after laser process, which leads to the formation of locally textured BSF. Due to this locally textured contact, the resultant fill factor (FF) and series resistance (Rs) loss of the PERL cells are even less than that of the reference PERT cells. As a result, the champion cell of PERL shows a good efficiency of 21.3% with open-circuit voltage (Voc) of 662 mV, short-circuit current density (Jsc) of 39.6 mA cm−2, and a high FF of 81.1%.

The optimization of water networks supports the decision-making process by identifying the optimal trade-off between costs and performance (e.g., resilience and leakage). A major challenge in the domain of water distribution systems (WDSs) is the network (re)design. While the complex nature of WDS has already been explored with complex network analysis (CNA), literature is still lacking a CNA of optimal water networks. Based on a systematic CNA of Pareto-optimal solutions of different WDSs, several graph characteristics are identified, and a newly developed CNA design approach for WDSs is proposed. The results show that obtained designs are comparable with results found by evolutionary optimization, but the CNA approach is applicable for large networks (e.g., 150,000 pipes) with a substantially reduced computational effort (runtime reduction up to 5 orders of magnitude).