Iron (oxyhydr)oxide nanoparticles (IONPs), which are ubiquitous in many natural aquatic and soil systems, can strongly interact with nutrient and contaminant species in the environment through their large specific surface areas and redox reactivity, thus controlling the transport and fate of these elements. Following their formation, IONPs often undergo aggregation and phase transformation processes that collectively determine their long-term environmental stability. The aggregation of IONPs reduces colloidal stability and can lead to deposition and immobilization, whereas stable dispersed colloids can remain mobile and transport associated elements over long distances. The phase transformations of metastable, poorly crystalline IONPs (e.g., ferrihydrite) into more crystalline iron (oxhydr)oxides (e.g., goethite, hematite, and magnetite) profoundly alter particle properties and influence the retention or release of sorbed or structurally incorporated species. This review focuses on IONP aggregation and phase transformation as key processes controlling long-term IONP stability and critically examines how they are influenced by three common environmental factors: metal ions, organic matter (OM), and microbial activity. Metal ions can adsorb to IONP surfaces to modify surface charges or be structurally incorporated to affect IONP crystallography, thereby modulating inter-particle forces and transformation rates. OM can adsorb to IONP surfaces, and, depending on its concentration and molecular characteristics, it can either stabilize particles via electrostatic and/or steric repulsion, or promote aggregation through charge neutralization and bridging effects. Further, organic ligands can also often inhibit IONP transformation or alter transformation pathways by binding to reactive surface sites. Microbial activity influences IONP stability through extracellular polymeric substances (EPS) that coat or bridge particles, and through redox processes that generate or consume Fe(II), thereby either dispersing IONPs or accelerating their transformation into more stable mineral phases. This review summarizes present research on the effects of IONP interactions with metals, organics, and microbes on IONP aggregation and transformation. Such an understanding is crucial for predicting IONP stability and transport in the environment and the long-term cycling of associated organic and inorganic contaminants and nutrients.
Cyanobacteria are promising platforms for light-driven carbon fixation and carbohydrate biosynthesis. However, optimization strategies that focus solely on carbon allocation are insufficient to achieve substantial improvements in yield and sustainability. Here, Synechococcuselongatus PCC 7942 was engineered to enhance sucrose production by simultaneously increasing total carbon input and reinforcing the artificial sink. The engineered strain secreted 5.821 g L−1 sucrose, which was 27.4 times higher than the wild-type. Transcriptomic analysis revealed upregulation of abundant genes involved in carbon fixation, sucrose biosynthesis, and electron transport chains. Furthermore, a synthetic light-driven consortium was established to directly convert CO2 into value-added compounds. This system produced 323.5 mg L−1 polyhydroxybutyrate, reducing CO2 emissions by 12.4 g per g of polyhydroxybutyrate compared to conventional heterotrophic processes. These findings highlight the potential of cyanobacteria-based systems for carbon-negative biomanufacturing, demonstrating their role in advancing sustainable carbohydrate and biochemical production while exemplifying circular bioeconomy principles.
Traditional methods for inferring room geometry from sound signals are predominantly based on Room ImpulseResponse (RIR) or prior knowledge of the sound source location. This significantly restricts the applicability ofthese approaches. This paper presents a method for estimating room geometry based on the localization of directsound source and its early reflections from First-Order Ambisonics (FOA) signals without the prior knowledge ofthe environment. First, this method simultaneously estimates the Direction of Arrival (DOA) of the direct sourceand the detected first-order reflected sources. Then, a Cross-attention-based network for implicitly extractingthe features related to Time Difference of Arrival (TDOA) between the direct source source and the first-orderreflected sources is proposed to estimate the distances of the direct and the first-order reflected sources. Finally,the room geometry is inferred from the localization results of the direct and the first-order reflected sources. Theeffectiveness of the proposed method was validated through simulation experiments. The experimental resultsdemonstrate that the method proposed achieves accurate localization results and performs well in inference of roomgeometry.
Abstract Vanadium dioxide (VO2), renowned for its reversible metal-to-insulator transition (MIT), has been widely used in configurable photonic and electronic devices. Precisely tailoring the MIT of VO2 on micro-/nano-scale is crucial for miniaturized and integrated devices. However, existing tailoring techniques like scanning probe microscopy, despite their precision, fall short in efficiency and adaptability, particularly on complex or curved surfaces. Herein, this work achieves the local engineering of the phase of VO2 films in high efficiency by employing laser writing to assist in the hydrogen doping or dedoping process. The laser doping and laser dedoping technique is also highly flexible, enabling the fabrication of reconfigurable, non-volatile, and multifunctional VO2 devices. This approach establishes a new paradigm for creating reconfigurable micro/nanophotonic and micro/nanoelectronic devices.
This paper presents CPL-IQA, a novel semi-supervised blind image quality assessment (BIQA) framework for authentic distortion scenarios. To address the challenge of limited labeled data in IQA area, our approach leverages confidence-quantifiable pseudo-label learning to effectively utilize unlabeled authentically distorted images. The framework operates through a preprocessing stage and two training phases: first converting MOS labels to vector labels via entropy minimization, followed by an iterative process that alternates between model training and label optimization. The key innovations of CPL-IQA include a manifold assumption-based label optimization strategy and a confidence learning method for pseudo-labels, which enhance reliability and mitigate outlier effects. Experimental results demonstrate the framework's superior performance on real-world distorted image datasets, offering a more standardized semi-supervised learning paradigm without requiring additional supervision or network complexity.