Stable isotopes are widely used to reconstruct Earth’s history. Accurate applications require mechanistic knowledge of isotope fractionation during crystal formation via nucleation followed by growth, but isotope fractionation during nucleation has not been directly measured. By experimentally isolating crystal nucleation on organic films from growth in bulk solution, this study provides a measurement of isotope fractionation associated with nucleation, and finds that nucleation produces near-equilibrium isotope signatures, distinct from kinetic fractionation during growth. These findings extend isotope fractionation theory beyond growth-dominated low supersaturation regimes and provide a mechanistic basis for interpreting paradoxes in isotope fractionation which could not be explained by an ion-by-ion growth model. These results also have broad implications for isotope fractionation during biomineralization and interface-promoted nucleation.
Test-time adaptation (TTA) aims to adapt the model trained on source domain to unseen target domain using a few unlabeled images during inference, which holds great value for the deployment of models in the clinical practice. In this setting, the model can only access online unlabeled test samples and pre-trained model on the source domain. Because unlabeled test samples may arrive sequentially, the model needs to adjust online for the cross-domain distribution shift from different medical institutions, the scale of which would change concurrently and continually over time. However, unstable optimization and abnormal distribution will lead to error accumulation and catastrophic forgetting. Considering the role of brain extracellular space in balancing neural homeostasis and signal transmission, we recognize that the existing TTA methods lack a dedicated component to ensure the stability and accuracy of the model. In this paper, we propose a robust TTA approach for cross-domain segmentation as MemTTA. Specifically, firstly, we introduce transductive batch normalization to ensure stability, which calculates the mean and the variance from the source domain and current test batch. Secondly, we propose a memorized spatial pixel-level clustering strategy to represent each category with multiple and anisotropic prototypes for feature alignment, which can be associated with the parametric classifier. During test time, we adapt the segmentation model to each test batch with self-supervision augmentation consistency learning to improve the inference performance. MemTTA needs only one epoch training on each test batch, and then is comparable to standard models as the traditional inference pipeline. The proposed method is extensively evaluated on neuron, brain metastases, cardiac, and abdominal organ image segmentation. The experimental results demonstrate that our proposed MemTTA can effectively mitigate test-time domain shift and catastrophic forgetting, and is superior to existing state-of-the-art approaches.
Shortly after the failed PKI uprisings of 1926/27, Tan Malaka and his associates established the Partai Republik Indonesia (PARI). Although he acted as the party chairman and chief strategist, his involvement in the party operation was minimal as he lived in exile. Nevertheless, he loomed large in the eyes of both his followers and enemies. Not only was Tan Malaka a legendary guru for Indonesian revolutionaries, but also an enormous threat to colonial authorities across East and Southeast Asia. This chapter explores Tan Malaka's exile in China between 1927 and 1936 and how such experiences reflect his shifting relationship with Indonesia's ongoing struggles for independence, the international communist movement, and the surveillance and policing practices of multiple colonial states.
Microbial functions and metabolism are intrinsic drivers of pollutant removal in mixotrophic denitrification systems. Four pyrite-based mixotrophic denitrifying biofilters were constructed and monitored for 304 days. Variations in pollutant characteristics indicated that the hot zones of heterotrophic denitrification, autotrophic denitrification, and sulfate reduction were located in the bottom, middle-lower, and upper parts of biofilters, respectively. These hot zones corresponded to preferential enrichment of heterotrophic denitrifying, S-based mixotrophic denitrifying, and sulfate-reducing bacteria, respectively, highlighting microbial spatial stratification. Differential functional gene analysis for S reduction revealed that only a dissimilated sulfate reduction process could consistently provide biogenic S0 as a new electron donor via the flavocytochrome c sulfide dehydrogenase (Fcc) enzyme and extracellular polymeric substance protection systems, enhancing the denitrification process. X-ray photoelectron spectroscopy confirmed the accumulation of biogenic S0 . Untargeted metabolomic analysis suggested that vitamin B12 and tryptophan might be the key metabolites for realizing synergistic promotion of autotrophic and heterotrophic denitrification. The microbe-metabolite network indicated that dominant bacteria (e.g., Thiothrix and unclassified\_f\_Rhodocyclaceae) were specialists with less cross-feeding metabolism, while rare species (e.g., Thiobacillus and Desulfobacter) were generalists with complex cross-feeding metabolism in the constructed mixotrophic denitrification systems. The electron transfer pattern indicated that most of the electrons released from S, C, and Fe oxidation were utilized in denitrification processes as the dominant nitrogen removal pathway, including S2- /S0-based autotrophic, fermentation acetic acid production-heterotrophic, and Fe(II)-based autotrophic denitrification. Some electrons were utilized for coupling dissimilatory nitrate reduction to ammonia (DNRA) and anammox processes as an auxiliary pathway for systemic nitrogen removal. The findings of this study advance our understanding of the deeper intrinsic drivers of nitrogen removal by pyrite-based mixotrophic denitrifying biofilters, facilitating their optimization. (c) 2025 THE AUTHORS. Published by Elsevier LTD on behalf of Chinese Academy of Engineering and Higher Education Press Limited Company. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Nanoconfined water—ubiquitous across both engineered nanoporous adsorbents and subsurface geological formations—plays a pivotal yet underexplored role in carbon capture and storage (CCS). This review systematically examines the physicochemical properties and functional implications of water confined within nanoporous environments, emphasizing its dualistic impact on both CO2 capture and geological CO2 storage. We first summarize recent advances from computational simulations and experimental characterizations, highlighting the altered thermodynamic and structural features, dynamic behavior, dielectric properties, and chemical reactivity of nanoconfined water. We then integrate insights from surface chemistry, materials science, and geoscience to elucidate how nanoconfined water influences CCS processes through competitive adsorption, pore accessibility, wettability, solubility, and mineralization kinetics, spanning systems from nanoporous adsorbents such as zeolites, metal–organic frameworks (MOFs), and activated carbon (AC) to unconventional formations including shale and tight sandstone. These findings also suggest opportunities for practical applications, such as guiding the design of hydrophobic MOFs for improved CO2 capture and supporting strategies to preserve caprock integrity in subsurface storage. Finally, we identify key challenges in bridging molecular-level understanding with material- and reservoir-scale performance, emphasizing the need for multiscale experimental techniques, realistic molecular modeling, and cross-disciplinary strategies to fully harness the functional potential of nanoconfined water in CCS.
Wastewater treatment plants simultaneously emit greenhouse gases and rely on external carbon sources, presenting both a challenge and an opportunity for carbon circularity. We develop a comprehensive life-cycle assessment of 32 Waste-to-Chemical pathways that integrate thermochemical and electrochemical conversions in sequential or parallel architectures to convert CH4 and CO2 into reusable carbon sources for in-plant utilization. Sequential thermochemical-electrochemical coupling consistently delivers the strongest climate benefit, with formate identified as the optimal product, reducing emissions up to ~35% reduction relative to direct-emission baselines. We further validate this pathway experimentally using commercially available Pd/Al2O3 catalysts for CH4 thermochemical oxidation and Bi2O3 catalysts for CO2 electroreduction in a porous solid electrolyte reactor. Residual heat generated during CH4 oxidation enhances downstream CO2 electroreduction, enabling 97.2 ± 1.2% Faradaic efficiency toward separation-free formate at 150 mA cm−2 and 45 °C. These results validate that commercially relevant catalysts and thermally coupled operation can deliver experimentally validated performance aligned with system-level projections. This framework establishes a scalable strategy for embedding carbon circularity into wastewater infrastructure and transforming wastewater treatment plants into distributed platforms for low-carbon chemical production.
Ice crystal orientation strongly influences cloud radiative properties and remote sensing retrievals, but long-term, high-resolution quantitative observations remain scarce. This study presents comprehensive case studies and statistical analyses of horizontally oriented ice crystals (HOICs) based on full-year (2022) synergistic observations in Beijing, China, combining a zenith-pointing micropulse lidar, a collocated 15° off-zenith polarization lidar, and a Ka-band cloud Doppler radar. Applying a novel height-resolved classification method based on dual-angle polarization lidars, HOICs are identified with high spatiotemporal resolution. HOICs are found to be common, accounting for 15.0 % of all ice-containing cloud data points annually with a peak of 24.6 % in summer, and showing maximum occurrence at temperatures from −20 to −10 °C. Macroscopically, typical HOIC layers exhibit horizontal extents of 10–100 km and durations of several hours within their optimal formation temperature ranges. Furthermore, the Euclidean-distance analysis between HOICs and past overlying cloud layers reveals a strong linkage of HOIC occurrence to supercooled liquid water clouds (SWCs), being much closer to HOIC events than randomly oriented ice crystals (ROICs). Dynamically, cloud radar observations further reveal that HOICs preferentially occur in stable environments with turbulent eddy dissipation rates below 10−2 m2 s−3 and exhibit lower fall velocities than ROICs. Estimates based on radar-observed vertical velocity indicate typical HOIC equivalent diameters of approximately 1200 µm with Reynolds numbers predominantly below 100. These findings provide key observational constraints for improving ice cloud microphysics and orientation parameterizations in numerical models.
