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Abnormally low pressure commonly develops in petroliferous basins. It is closely associated with hydrocarbon distribution and can hinder safe drilling operations. Potentiometric surface is one of the causes of abnormally low pressure, but the relationship between such surface and abnormal pressure has not attracted much research attention. In this paper, a method is proposed for quantifying the potentiometric surface of the tight oil reservoir of the Upper Triassic Yanchang Formation in the Zhenjing area, southern Ordos Basin, China. This method involves screening pressure data that reflect real pore pressure conditions underground and analyzing the formation sealing conditions in the reservoir based on fracture development and the chemical characteristics of the formation water. The relationship between the potentiometric surface and the abnormally low pressure observed in the study area is discussed. The results show that the pressure coefficient of the Yanchang Formation in the Zhenjing area is 0.85–1.02, with an average of 0.93, indicating a state between abnormally low pressure and normal pressure. The Yanchang Formation has complex sealing conditions, with favorable overall sealing and a locally open hydrodynamic environment controlled by sand body connectivity and fault activity. We proposed a method to calculate the potentiometric surface using the measured pressure data with a pressure coefficient close to 1. The potentiometric surface is 1040–1200 m, with a high value in the northwest and a low value in the southeast, and it is 0–350 m below the ground level. The pressure coefficient was calibrated with an average of 1, with the potentiometric surface used as the starting point, indicating that the low pressure coefficient may be caused by the potentiometric surface below the ground level. The results have theoretical implications for the analysis of formation pressure characteristics in areas or basins with comparable geological conditions, and practical implications for the analysis and development of oil and gas.

Beginning in 2013, China launched two phases (2013–2017 and 2018–2020) of clean air actions that have led to substantial reductions in PM2.5concentrations. However, improvement in PM2.5pollution was notably slowing down during Phase II. Here we quantify the efficacy and drivers of PM2.5improvement and evaluate the associated cost during 2013–2020 using an integrated framework that combines an emission inventory model, a chemical transport model and detailed cost information. We found that national population-weighted mean PM2.5concentrations decreased by 19.8 μg m−3and 10.9 μg m−3in the two phases, and the contribution of clean air policies in Phase II (2.3 μg m−3yr−1) was considerably lower than that of Phase I (4.5 μg m−3yr−1), after excluding the impacts from meteorological condition changes and COVID-19 lockdowns. Enhanced structure transitions and targeted volatile organic compounds and NH3reduction measures have successfully reduced emissions in Phase II, but measures focusing on the end-of-pipe control were less effective after 2017. From 2013 to 2020, PM2.5abatement became increasingly challenging, with the average cost of reducing one unit of PM2.5concentration in Phase II twice that of Phase I. Our results suggest there is a need for strengthened, well-balanced, emission control strategies for multi-pollutants.

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Online map matching is essential for some location-based services, such as car navigation. However, due to GPS measurement errors and/or the lack of sufficient information, the performance of most existing online algorithms will degrade in the increasingly complex traffic environment. In this paper, we propose an adaptive online map matching algorithm called AMM. The basic idea is that AMM should be able to calibrate GPS observation data for various measurement errors and under complex urban conditions. First, we establish a collaborative evaluation model between GPS points and candidate points to effectively filter low-quality GPS measurement points, dynamically set the weights of different features and comprehensively select the best candidate points. Second, we propose a retrospective correction mechanism to correct the previous matching results when more information is available, which will help improve the accuracy of future GPS points. Furthermore, we define parameter self-tuning rules for AMM to enhance its portability by avoiding time-consuming parameter tuning steps. We conduct extensive experiments to evaluate the performance of AMM on real vehicle trajectory datasets. The experiment results show that AMM outperforms its counterparts by up to 32% in terms of accuracy and its performance in different traffic conditions is more stable.

CO2emission inventory provides fundamental data for climate research and emission mitigation. Currently, most global CO2emission inventories were developed with energy statistics from International Energy Agency (IEA) and were available at country level with limited source categories. Here, as the first step toward a high-resolution and dynamic updated global CO2emission database, we developed a data-driven approach to construct seamless and highly-resolved energy consumption data cubes for 208 countries/territories, 797 sub-country administrative divisions in 29 countries, 42 fuel types, and 52 sectors, with the fusion of activity data from 24 international statistics and 65 regional/local statistics. Global CO2emissions from fossil fuel combustion and cement production in 1970–2021 were then estimated with highly-resolved source category (1,484 of total) and sub-country information (797 of total). Specifically, 73% of global CO2emissions in 2021 were estimated with sub-country information, providing considerably improved spatial resolution for global CO2emission accounting. With the support of detailed information, the dynamics of global CO2emissions across sectors and fuel types were presented, representing the evolution of global economy and progress of climate mitigation. Remarkable differences of sectoral contribution were found across sub-country administrative divisions within a given country, revealing the uneven distribution of energy and economic structure among different regions. Our estimates were generally consistent with existing databases at aggregated level for global total or large emitters, while large discrepancies were observed for middle and small emitters. Our database, named the Multi-resolution Emission Inventory model for Climate and air pollution research (MEIC) is publicly available through http://meicmodel.org.cnwith highly-resolved information and timely update, which provides an independent carbon emission accounting data source for climate research.

Neurodegenerative diseases pose significant challenges to global healthcare, exacerbated by complexities of the central nervous system and blood–brain barrier. While FDA-approved magnetic nanocarriers offer promising solutions for targeted drug delivery, inherent challenges in predicting delivery performance still hinder clinical practice. Existing brain vasculature transport models often lack accuracy in the 3D construction of the brain vasculature network and physiology of blood circulation, limiting progress in targeted drug delivery. This paper introduced the Circle of Willis’s novel computational fluid dynamics framework to address these challenges. Utilizing patient-specific vascular geometries and incorporating complexities of blood circulation, hemodynamics, and the rheology for non-Newtonian fluid effect, our approach provides unprecedented insights into drug carrier dynamics in the mouse brain vasculature. Furthermore, we performed a comparative study simulating the dynamic transport using three types of magnetic nanocarriers—gold-coated superparamagnetic iron oxide (Au-SPIO), hollow-gold nano-shell enclosed superparamagnetic iron oxide (HGNS-SPIO), and metal–organic frameworks loaded with iron oxide (MOF-Fe3O4)—to predict their transport in adult mice’s brain under magnetic targeting. The simulation was validated by in vivo results by comparing the bioavailability of nanoparticles in different brain regions. Under a non-magnetic field, simulations revealed a capture efficiency of around 10.5% for all three types of nanoparticles, with size-dependent patterns favoring smaller sizes. With the presence of a magnetic field, MOF-Fe3O4 demonstrated the highest capture efficiency with “single magnet” at 11.19%, while Au-SPIO in “linear Halbach array” and MOF-Fe3O4in “circular Halbach array” layouts reached 10.9%. Finally, we demonstrated high biocompatibility for all three nanocarriers, with no toxicity for Au-SPIO and MOF-Fe3O4at 40 µg/mL and for HGNS-SPIO at 20 µg/mL. Effective cell uptake was also observed for all three nanocarriers. This comprehensive study addresses critical knowledge gaps, providing insights into the dynamics of magnetic nanocarrier transport within the brain and paving the way for highly effective, personalized therapies for neurological disorders.

Magneto-plasmonic nanoparticles (MPNPs), such as solid gold (Au) or hollow gold (HG) coated superparamagnetic iron oxide (SPIO) nanoparticles (NPs), have attracted increasing attention for brain-targeted therapeutics. This is due to their supreme magnetic targeting capability, light-to-heat conversion efficiency, and biocompatibility. Though promising, their therapeutic efficiency is difficult to predict because of the complex absorption, distribution, metabolism, and excretion process and the intrinsic and extrinsic properties of the blood–brain barrier (BBB). This paper presents a modern physiologically based pharmacokinetic (PBPK) model to predict pharmacokinetic (PK) behaviors of brain-targeting MPNPs and investigate their morphology and surface function-dependent BBB crossing efficiency. This model quantifies intrinsic and extrinsic properties of PK parameters, including phagocytic cellular uptake rate and brain permeability. This model successfully predicts the biodistribution of functionalized Au-SPIO (18.42 ± 0.23 nm) and HG-SPIO (73.65 ± 1.46 nm) MPNPs in 8-week-old adult mice in a 16-h window after intraperitoneal (IP) injection. These predictions agree well with the experimental data with a low absolute average fold error (1.5381 for Au-SPIO and 1.1225 for HG-SPIO NPs). Interestingly, Au-SPIO MPNPs with thinner plasmonic layers result in higher magnetization levels and thus lead to more efficient BBB crossing. Static magnetic field stimulation could improve brain accumulation of IP-injected Au-SPIO and HG-SPIO NPs by up to 4.9% and 1.4%, respectively. Additionally, IP injection led to higher brain accumulation compared to intravenous injection. This modern PBPK model can guide MPNP design optimization for brain-specific therapeutics.

The decomposition of levulinic acid (LA) is a critical factor limiting the yield of LA produced by biomass hydrolysis. In this study, the effects of influencing factors, especially furfural, on the decomposition of LA were investigated. Results show that furfural, as a hemicellulose hydrolysis product, led to an increase in LA decomposition; 25.3% of LA decomposed when furfural concentration was 0.3 mol/L at 200 °C. Furthermore, sulfuric acid, as a hydrolysis catalyst, was found to have a synergistic effect with furfural on LA decomposition, giving a maximum promotion of 9.86% on LA decomposition compared with the effect of acid or furfural alone. A kinetic model of LA decomposition considering the synergistic effect of furfural and acid concentration was established, and the activation energy of LA decomposition was found to be 34.46 kJ/mol.

Stable and durable piezoelectric nanogenerators (PENGs) with good flexibility, high performance and superior radiation resistance under harsh environments are promising for space exploration. Here, a novel PENG based on P(VDF-TrFE)/boron nitride nanotubes (BNNTs) nanocomposite micropillar arrays with enhanced performance and excellent neutron radiation shielding is prepared by a reliable nanoimprint lithography. The PENG comprised of a microstructured P(VDF-TrFE)/0.3 wt% BNNTs nanocomposite demonstrates an outstanding output voltage of 22 V and a sensitivity of 55 V/MPa under the pressure of 0.4 MPa, which are 11-fold higher than those of pristine P(VDF-TrFE) film. This dramatic enhancement in performance is ascribed to synergistic contributions from strong piezoelectric BNNTs and a strain confinement effect of the nanocomposite microstructure. In practice, the PENG is capable of scavenging various mechanical and biomechanical energy for lighting up commercial LEDs, an LCD screen and a digital watch. More importantly, the as-obtained PENG exhibited 9% neutron radiation shielding with neutron cross section increase reaching 260% when compared to the film without BNNTs. Moreover, the high output is retained after 2 h of neutron radiation exposure. Overall, the as-prepared microstructured nanocomposites look promising for high-efficiency piezoelectric nanogenerators for the self-powered and wearable electronic devices, in particularly, under the extreme space environments.

Artificial photosynthesis for high-value hydrogen peroxide (H2O2) through a two-electron reduction reaction is a green and sustainable strategy. However, the development of highly active H2O2photocatalysts is impeded by severe carrier recombination, ineffective active sites, and low surface reaction efficiency. We developed a dual optimization strategy to load dense Ni nanoparticles onto ultrathin porous graphitic carbon nitride (Ni-UPGCN). In the absence and presence of sacrificial agents, Ni-UPGCN achieved H2O2production rates of 169 and 4116 μmol g–1h–1with AQY (apparent quantum efficiency) at 420 nm of 3.14% and 17.71%. Forming a Schottky junction, the surface-modified Ni nanoparticles broaden the light absorption boundary and facilitate charge separation, which act as active sites, promoting O2adsorption and reducing the formation energy of *OOH (reaction intermediate). This results in a substantial improvement in both H2O2generation activity and selectivity. The Schottky junction of dual modulation strategy provides novel insights into the advancement of highly effective photocatalytic agents for the photosynthesis of H2O2.

It is commonly believed that the Hamiltonian approach applies only to linear systems, or nonlinear systems with proper linearization procedures. In this Letter, we show that the Hamiltonian approach based on linear coupled-mode theory applies very well for cross-mode modulation, i.e., field variation of the probe light induced by a strong co-propagating pump light in a degenerate mode group. By deriving eigenvalues and eigenvectors of the nonlinear system, evolution of the probe light can be obtained, which agrees with numerical simulations of multimode nonlinear Schrödinger equations. Using the Hamiltonian approach, a simple approach for determining the pump mode for realizing arbitrary mode conversion is discussed. Two concrete scenarios of the optically induced vector mode conversions within the LP_11-mode group are further exemplified.