Hydroxymethyl hydroperoxide (HMHP, HOCH2OOH) is one of the most abundant organic peroxides (POs) in the atmosphere. Owing to its extremely high solubility, HMHP readily partitions into cloudwater and aerosol liquid water, where it hydrolyzes to hydrogen peroxide (H2O2) and formaldehyde (HCHO). However, previous studies were conducted in dilute solutions and did not adequately account for the high-salinity characteristic of deliquesced aerosol particles. Here, we systematically investigate the combined effects of pH (0–6), temperature (277–313 K), ionic strength (0–10 M), and ion identity (NH4+, Na+, SO42–, and Cl–) on the hydrolysis kinetics of HMHP. For the first time, a parametrization formula describing the dependence of the hydrolysis rate constant on ionic strength is established, demonstrating that ionic strength exerts only a limited influence on HMHP hydrolysis. However, it is found that in highly concentrated ammonium salt solutions, HMHP undergoes a previously unrecognized NH3-driven reaction pathway. This new pathway competes with hydrolysis, accelerating the apparent transformation rate of HMHP by more than an order of magnitude while significantly reducing the yield of H2O2 and HCHO. Our findings highlight that future atmospheric chemical models should fully account for the NH3-driven pathway in aqueous-phase reactions of POs, thereby enabling a more accurate assessment of the role of POs in atmospheric oxidant cycling and secondary particulate matter formation.
Transistor-integrated flexible pressure sensors have received considerable interest in emerging fields such as humanoid robotics, prosthetics, and implantable electronics. However, existing designs for these integrated sensors often exhibit a trade-off between pressure response and operating voltage, thus significantly limiting their practical applications. In this letter, we report a unique device design of integrated pressure sensors based on deformable microstructured electrodes capacitively coupled with floating-gate carbon nanotube transistors. The microstructured electrodes can dramatically enhance the pressure-introduced electrostatic control of the transistor, enabling a substantial improvement in the transduced pressure response at low operating voltages. With this unique design, we achieve a high pressure response of $10^5$ and an ultrahigh sensitivity up to $10^4 \text kPa^\text - 1$ at a low operating voltage below 3 V, which holds great promise for the development of advanced functionalized flexible electronics.
We studied the horizontally oriented ice crystals (HOIC) with the combinational observations of a zenith-pointing and a slant-pointing (with a zenith angle of 15 degrees) polarization lidar in Beijing in 2022. The HOICs account for approximately 7.3 % of total ice-containing clouds. These results have the potential to enhance the parameterization scheme in climate models for this unique form of ice crystals.
Geological carbon cycle (GCC) directly impacts the global carbon cycle and climate change, where CO2 adsorption is one most critical factor, governs the GCC's efficiency and security. However, the fundamental mechanisms of CO2 adsorption and its effects on interfacial properties remain inadequately understood due to the inherent complexities of mineral compositions, pore structures, and wettability heterogeneities. To address these challenges, this review systematically explores the theoretical foundations of CO2 adsorption, providing a mechanistic elucidation of CO2 adsorption and its effects on interfacial properties. It integrates insights from mathematical modeling, molecular simulations, and experimental methodologies to elucidate the mechanisms of CO2 sorption and its competitive behavior with other fluid components such as CH4 and H2O in geological formations. We critically assess CO2 adsorption behaviors at diverse interfaces, including solid-fluid interfaces (organic, inorganic, and composite models) and fluid-fluid interfaces (e.g., CO2-water), and discuss influencing factors, such as pore shape/size, temperature, pressure, moisture, wettability, and external electric fields. Furthermore, the review specifically evaluates the key physicochemical mechanisms underlying CO2-interface interactions and its implications for interfacial properties, including wettability alteration, interfacial tension changes and adsorption-induced deformation. Meanwhile, we provide a comprehensive understanding of adsorption scenarios within the GCC. Finally, we outline current research challenges and identify prospects to advance the fundamental understanding of how CO2 adsorption influences mineral interfacial properties and the GCC processes, thereby contributing to global climate governance and carbon neutrality efforts.Keywords: CO2 adsorption; Interfacial properties; Gas–solid interactions; Minerals; Geological carbon cycle
Achieving sound field reproduction (SFR) with high sound quality and accurate spatial localization in automotive cabins is particularly challenging due to complex acoustics and constrained loudspeaker layouts. This paper proposes a learning-based method to address this challenge, integrating a spatial domain physics-informed constraint based on plane-wave decomposition (PWD) with a multi-position control strategy. Results from both objective evaluations and in-situ subjective listening tests consistently validated the superiority of the proposed approach over several baseline methods. Moreover, we show that the correlation of spatial power maps (SPMs) derived from PWD provides a reliable objective metric that closely reflects perceived spatial localization in the cabin environment.
The effects and mechanisms of carbon (C)- and nitrogen (N)-deficient nutrient conditions (prevalent in natural environment) on bacterial mobile performance in porous media are unclear. This study systematically investigated the transport/retention performance of Gram-negative Escherichia coli and Gram-positive Bacillus subtilis experiencing different nutrient conditions (i.e. nutrient-sufficient, C-deficient, or N-deficient conditions) in column, parallel plate flow chamber (PPFC) and microfluidic chamber systems. We found that compared to those in nutrient-sufficient condition, bacteria (regardless of their type) exposure to C-deficient nutrient condition exhibited 7–14% reduced mobility in porous media, whereas those experienced N-deficient condition had 7–20% enhanced transport in both simulated electrolyte solutions and real groundwater samples. The underlying mechanisms driving to different mobile performance of bacteria exposure to different nutrient conditions were correlated with the composition of proteins (one major component of extracellular polymeric substances (EPS)). Compared to nutrient-sufficient condition, C-deficient condition increased EPS hydrophobicity via enhancing hydrophobic amino acids contents and altering secondary structure within proteins thus decreased bacterial transport, while N-deficient condition decreased EPS hydrophobicity through decreasing the abundance of hydrophobic amino acids within proteins and increased cell mobility. The results showed that via changing cell surface hydrophobicity, exposure bacteria to different nutrient conditions could induce different mobile performance of bacteria.
The rapid development of multimodal epidermal sensing requires scalable, energy-efficient data processing architectures capable of processing large volumes of raw data. Conventional systems suffer from high energy consumption and transmission latency due to the physical separation of sensors and processors. Here, we present an ultrathin flexible edge computing circuit based on carbon nanotube thin-film transistors (CNT-TFTs) and machine learning (ML)-assisted design. By incorporating substrate engineering, ML-derived device modeling, and industry-compatible design methodologies, we establish a complete toolchain from device to system. The ML model achieves 91.2% prediction accuracy, enabling simulation-guided optimization of logic gates. A CNT-based standard cell library enables the construction of flexible circuits with 361 transistors and 160 logic gates. Monolithic integration with an 8-channel tilt sensor achieves 62.5% data compression while maintaining functionality after undergoing 360° deformation. This work establishes an ML-assisted CNT circuit design framework for fully integrated flexible edge computing, enabling scalable wearable applications.
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.
