Lithium Superionic Conductive Nanofiber-Reinforcing High-Performance Polymer Electrolytes for Solid-State Batteries.

Although composite solid-state electrolytes (CSEs) are considered promising ionic conductors for high-energy lithium metal batteries, their unsatisfactory ionic conductivity, low mechanical strength, poor thermal stability, and narrow voltage window limit their practical applications. We have prepared a new lithium superionic conductor (Li-HA-F) with an ultralong nanofiber structure and ultrahigh room-temperature ionic conductivity (12.6 mS cm-1). When it is directly coupled with a typical poly(ethylene oxide)-based solid electrolyte, the Li-HA-F nanofibers endow the resulting CSE with high ionic conductivity (4.0 × 10-4 S cm-1 at 30 °C), large Li+ transference number (0.66), and wide voltage window (5.2 V). Detailed experiments and theoretical calculations reveal that Li-HA-F supplies continuous dual-conductive pathways and results in stable LiF-rich interfaces, leading to its excellent performance. Moreover, the Li-HA-F nanofiber-reinforced CSE exhibits good heat/flame resistance and flexibility, with a high breaking strength (9.66 MPa). As a result, the Li/Li half cells fabricated with the Li-HA-F CSE exhibit good stability over 2000 h with a high critical current density of 1.4 mA cm-2. Furthermore, the LiFePO4/Li-HA-F CSE/Li and LiNi0.8Co0.1Mn0.1O2/Li-HA-F CSE/Li solid-state batteries deliver high reversible capacities over a wide temperature range with a good cycling performance.

Self-Assembly of Super-Uniform Covalent Organic Framework Colloidal Particles into Multi-Dimensional Ordered Superstructures.

Precise self-assembly of colloidal particles is crucial for understanding their aggregation properties and preparing macroscopic functional devices. It is currently very challenging to synthesize and self-assemble super-uniform covalent organic framework (COF) colloidal particles into well-organized multidimensional superstructures. Here, simple and versatile strategies are proposed for synthesis of super-uniform COF colloidal particles and self-assembly of them into 1D supraparticles, 2D ordered mono/multilayers, and 3D COF films. For this purpose, several self-assembly techniques are developed, including emulsion solvent evaporation, air-liquid interfacial self-assembly, and drop-casting. These strategies enable the superstructural self-assembly of particles of varying sizes and species without any additional surfactants or chemical modifications. The assembled superstructures maintain the porosity and high specific surface area of their building blocks. The feasibility of the strategies is examined with different types of COFs. This research provides a new approach for the controllable synthesis of super-uniform COF colloidal particles capable of self-assembling into multidimensional superstructures with long-range order. These discoveries hold great promise for the design of emerging multifunctional COF superstructures.

Experimental Validation of Enhanced Information Capacity by Quantum Switch in Accordance with Thermodynamic Laws.

We experimentally probe the interplay of the quantum switch with the laws of thermodynamics. The quantum switch places two channels in a superposition of orders and may be applied to thermalizing channels. Quantum-switching thermal channels has been shown to give apparent violations of the second law. Central to these apparent violations is how quantum switching channels can increase the capacity to communicate information. We experimentally show this increase and how it is consistent with the laws of thermodynamics, demonstrating how thermodynamic resources are consumed. We use a nuclear magnetic resonance approach with coherently controlled interactions of nuclear spin qubits. We verify an analytical upper bound on the increase in capacity for channels that preserve energy and thermal states, and demonstrate that the bound can be exceeded for an energy-altering channel. We show that the switch can be used to take a thermal state to a state that is not thermal, while consuming free energy associated with the coherence of a control system. The results show how the switch can be incorporated into quantum thermodynamics experiments as an additional resource.

Experimental Realization of Self-Contained Quantum Refrigeration.

A fundamental challenge in quantum thermodynamics is the exploration of inherent dimensional constraints in thermodynamic machines. In the context of two-level systems, the most compact refrigerator necessitates the involvement of three entities, operating under self-contained conditions that preclude the use of external work sources. Here, we build such a smallest refrigerator using a nuclear spin system, where three distinct two-level carbon-13 nuclei in the same molecule are involved to facilitate the refrigeration process. The self-contained feature enables it to operate without relying on net external work, and the unique mechanism sets this refrigerator apart from its classical counterparts. We evaluate its performance under varying conditions and systematically scrutinize the cooling constraints across a spectrum of scenarios, which sheds light on the interplay between quantum information and thermodynamics.

Self-Consistent Determination of Single-Impurity Anderson Model Using Hybrid Quantum-Classical Approach on a Spin Quantum Simulator.

The accurate determination of the electronic structure of strongly correlated materials using first principle methods is of paramount importance in condensed matter physics, computational chemistry, and material science. However, due to the exponential scaling of computational resources, incorporating such materials into classical computation frameworks becomes prohibitively expensive. In 2016, Bauer et al. proposed a hybrid quantum-classical approach to correlated materials [B. Bauer et al., Hybrid quantum-classical approach to correlated materials, Phys. Rev. X 6, 031045 (2016).PRXHAE2160-330810.1103/PhysRevX.6.031045] that can efficiently tackle the electronic structure of complex correlated materials. Here, we experimentally demonstrate that approach to tackle the computational challenges associated with strongly correlated materials. By seamlessly integrating quantum computation into classical computers, we address the most computationally demanding aspect of the calculation, namely the computation of the Green's function, using a spin quantum processor. Furthermore, we realize a self-consistent determination of the single impurity Anderson model through a feedback loop between quantum and classical computations. A quantum phase transition in the Hubbard model from the metallic phase to the Mott insulator is observed as the strength of electron correlation increases. As the number of qubits with high control fidelity continues to grow, our experimental findings pave the way for solving even more complex models, such as strongly correlated crystalline materials or intricate molecules.

Demonstrating Path-Independent Anyonic Braiding on a Modular Superconducting Quantum Processor.

Anyons, exotic quasiparticles in two-dimensional space exhibiting nontrivial exchange statistics, play a crucial role in universal topological quantum computing. One notable proposal to manifest the fractional statistics of anyons is the toric code model; however, scaling up its size through quantum simulation poses a serious challenge because of its highly entangled ground state. In this Letter, we demonstrate that a modular superconducting quantum processor enables hardware-pragmatic implementation of the toric code model. Through in-parallel control across separate modules, we generate a 10-qubit toric code ground state in four steps and realize six distinct braiding paths to benchmark the performance of anyonic statistics. The path independence of the anyonic braiding statistics is verified by correlation measurements in an efficient and scalable fashion. Our modular approach, serving as a hardware embodiment of the toric code model, offers a promising avenue toward scalable simulation of topological phases, paving the way for quantum simulation in a distributed fashion.

Precisely Identifying the Sources of Magnetic Particles by Hierarchical Classification-Aided Isotopic Fingerprinting.

Magnetic particles (MPs), with magnetite (Fe3O4) and maghemite (γ-Fe2O3) as the most abundant species, are ubiquitously present in the natural environment. MPs are among the most applied engineered particles and can be produced incidentally by various human activities. Identification of the sources of MPs is crucial for their risk assessment and regulation, which, however, is still an unsolved problem. Here, we report a novel approach, hierarchical classification-aided stable isotopic fingerprinting, to address this problem. We found that naturally occurring, incidental, and engineered MPs have distinct Fe and O isotopic fingerprints due to significant Fe/O isotope fractionation during their generation processes, which enables the establishment of an Fe-O isotopic library covering complex sources. Furthermore, we developed a three-level machine learning model that not only can distinguish the sources of MPs with a high precision (94.3%) but also can identify the multiple species (Fe3O4 or γ-Fe2O3) and synthetic routes of engineered MPs with a precision of 81.6%. This work represents the first reliable strategy for the precise source tracing of particles with multiple species and complex sources.

Enoxaparin sodium combined with magnesium sulfate in the treatment of severe preeclampsia.

Objective: To observe the treatment of severe preeclampsia in newborns with enoxaparin sodium combined with magnesium sulfate. Methods: A retrospective analysis was conducted on the clinical data of 80 patients with severe preeclampsia admitted to Hefei Second People's Hospital, China from January 2019 to December 2020. Treatment records showed that 40 cases received magnesium sulfate treatment (single group), and 40 cases received enoxaparin sodium combined with magnesium sulfate treatment (combination group). Levels of D-dimer, soluble fms-like tyrosine kinase 1 (sFlt-1), placental growth factor (PLGF), Apgar scores of newborns delivered before and after treatment were compared. Gestation weeks and incidence of adverse reactions were analyzed. Results: After treatment, levels of D-dimer, sfit-1 and adverse reactions in the combination group were significantly lower than those in the single group (P<0.05), and the level of PLGF, newborn Apgar score and length of gestation were significantly higher than those in the single group (P<0.05). Conclusion: Compared to magnesium sulfate alone, the combination of enoxaparin sodium and magnesium sulfate in the treatment of pregnant women with severe preeclampsia can more effectively regulate the cytokine level of patients, improve pregnancy outcome, and improve neonatal Apgar score. The incidence of adverse reactions is low, making it a safe and efficient treatment modality.

Chronic Ambient Ozone Exposure Aggravates Autism-Like Symptoms in a Susceptible Mouse Model.

Although there is evidence that exposure to ground-level ozone (O3) may cause an increased risk of neurological disorders (e.g., autistic spectrum disorder), low-dose chronic ozone exposure and its adverse effects on the nervous system have not been fully understood. Here, we evaluated the potential neurotoxic effects of long-term exposure to environmentally relevant O3 concentration (200 µg/m3 via a whole-body inhalation system, 12 h/day for 5 days/week) using a susceptible mouse model of autism induced by valproic acid. Various indicators of oxidative stress, mitochondria, and synapse in the brain tissues were then measured to determine the overall damage of O3 to the mouse brain. The results showed an aggravated risk of autism in mice offspring, which was embodied in decreased antioxidant contents, disturbed energy generation in mitochondria, as well as reduced expressions of protein kinase Mζ (PKMζ) and synaptic proteins [e.g., Synapsin 1 (SYN 1), postsynaptic density protein-95 (PSD-95)]. Overall, our study indicates that prenatal exposure to environmentally relevant O3 may exacerbate the symptoms of autism, shedding light on possible molecular mechanisms and providing valuable insights into the pathogenesis of autism, especially concerning low-dose levels of those pollutants.

PM2.5-bound polyhalogenated carbazoles (PHCZs) in urban Beijing, China: Occurrence and the source implication.

Polyhalogenated carbazoles (PHCZs) are recently raising much attention due to their toxicity and ubiquitous environmental distribution. However, little knowledge is known about their ambient occurrences and the potential source. In this study, we developed an analytical method based on GC-MS/MS to simultaneously determine 11 PHCZs in PM2.5 from urban Beijing, China. The optimized method provided low method limit of quantifications (MLOQs, 1.45-7.39 fg/m3) and satisfied recoveries (73.4%-109.5%). This method was applied to analyze the PHCZs in the outdoor PM2.5 (n = 46) and fly ash (n = 6) collected from 3 kinds of surrounding incinerator plants (steel plant, medical waste incinerator and domestic waste incinerator). The levels of ∑11PHCZs in PM2.5 ranged from 0.117 to 5.54 pg/m3 (median 1.18 pg/m3). 3-chloro-9H-carbazole (3-CCZ), 3-bromo-9H-carbazole (3-BCZ), and 3,6-dichloro-9H-carbazole (36-CCZ) were the dominant compounds, accounting for 93%. 3-CCZ and 3-BCZ were significantly higher in winter due to the high PM2.5 concentration, while 36-CCZ was higher in spring, which may be related to the resuspending of surface soil. Furthermore, the levels of ∑11PHCZs in fly ash ranged from 338 to 6101 pg/g. 3-CCZ, 3-BCZ and 36-CCZ accounted for 86.0%. The congener profiles of PHCZs between fly ash and PM2.5 were highly similar, indicating that combustion process could be an important source of ambient PHCZs. To the best of our knowledge, this is the first research providing the occurrences of PHCZs in outdoor PM2.5.

Entanglement-Enhanced Quantum Metrology in Colored Noise by Quantum Zeno Effect.

In open quantum systems, the precision of metrology inevitably suffers from the noise. In Markovian open quantum dynamics, the precision can not be improved by using entangled probes although the measurement time is effectively shortened. However, it was predicted over one decade ago that in a non-Markovian one, the error can be significantly reduced by the quantum Zeno effect (QZE) [Chin, Huelga, and Plenio, Phys. Rev. Lett. 109, 233601 (2012)PRLTAO0031-900710.1103/PhysRevLett.109.233601]. In this work, we apply a recently developed quantum simulation approach to experimentally verify that entangled probes can improve the precision of metrology by the QZE. Up to n=7 qubits, we demonstrate that the precision has been improved by a factor of n^{1/4}, which is consistent with the theoretical prediction. Our quantum simulation approach may provide an intriguing platform for experimental verification of various quantum metrology schemes.

Experimental Realization of a Quantum Refrigerator Driven by Indefinite Causal Orders.

Indefinite causal order (ICO) is playing a key role in recent quantum technologies. Here, we experimentally study quantum thermodynamics driven by ICO on nuclear spins using the nuclear magnetic resonance system. We realize the ICO of two thermalizing channels to exhibit how the mechanism works, and show that the working substance can be cooled or heated albeit it undergoes thermal contacts with reservoirs of the same temperature. Moreover, we construct a single cycle of the ICO refrigerator based on the Maxwell's demon mechanism, and evaluate its performance by measuring the work consumption and the heat energy extracted from the low-temperature reservoir. Unlike classical refrigerators in which the coefficient of performance (COP) is perversely higher the closer the temperature of the high-temperature and low-temperature reservoirs are to each other, the ICO refrigerator's COP is always bounded to small values due to the nonunit success probability in projecting the ancillary qubit to the preferable subspace. To enhance the COP, we propose and experimentally demonstrate a general framework based on the density matrix exponentiation (DME) approach, as an extension to the ICO refrigeration. The COP is observed to be enhanced by more than 3 times with the DME approach. Our Letter demonstrates a new way for nonclassical heat exchange, and paves the way towards construction of quantum refrigerators on a quantum system.

Data-Driven Machine Learning in Environmental Pollution: Gains and Problems.

The complexity and dynamics of the environment make it extremely difficult to directly predict and trace the temporal and spatial changes in pollution. In the past decade, the unprecedented accumulation of data, the development of high-performance computing power, and the rise of diverse machine learning (ML) methods provide new opportunities for environmental pollution research. The ML methodology has been used in satellite data processing to obtain ground-level concentrations of atmospheric pollutants, pollution source apportionment, and spatial distribution modeling of water pollutants. However, unlike the active practices of ML in chemical toxicity prediction, advanced algorithms such as deep neural networks in environmental process studies of pollutants are still deficient. In addition, over 40% of the environmental applications of ML go to air pollution, and its application range and acceptance in other aspects of environmental science remain to be increased. The use of ML methods to revolutionize environmental science and its problem-solving scenarios has its own challenges. Several issues should be taken into consideration, such as the tradeoff between model performance and interpretability, prerequisites of the machine learning model, model selection, and data sharing.

Internal Exposure and Distribution of Airborne Fine Particles in the Human Body: Methodology, Current Understandings, and Research Needs.

Exposure to airborne fine particles (PM2.5, particulate matter with aerodynamic diameter <2.5 µm) severely threatens global human health. Understanding the distribution and processes of inhaled PM2.5 in the human body is crucial to clarify the causal links between PM2.5 pollution and diseases. In contrast to extensive research on the emission and formation of PM2.5 in the ambient environment, reports about the occurrence and fate of PM2.5 in humans are still limited, although many studies have focused on the exposure and adverse effects of PM2.5 with animal models. It has been shown that PM2.5, especially ultrafine particles (UFPs), have the potential to go across different biological barriers and translocate into different human organs (i.e., blood circulation, brain, heart, pleural cavity, and placenta). In this Perspective, we summarize the factors affecting the internal exposure of PM2.5 and the relevant analytical methodology and review current knowledge about the exposure pathways and distribution of PM2.5 in humans. We also discuss the research challenges and call for more studies on the identification and characterization of key toxic species of PM2.5, quantification of internal exposure doses in the general population, and further clarification of translocation, metabolism, and clearance pathways of PM2.5 in the human body. In this way, it is possible to develop toxicity-based air quality standards instead of the currently used mass-based standards.

New Insights into Unexpected Severe PM2.5 Pollution during the SARS and COVID-19 Pandemic Periods in Beijing.

During the SARS period in 2003 and COVID-19 pandemic period in 2020, unexpected severe particulate matter pollution occurred in northern China, although the anthropogenic activities and associated emissions have assumed to be reduced dramatically. This anomalistic increase in PM2.5 pollution raises a question about how source emissions impact the air quality during these pandemic periods. In this study, we investigated the stable Cu and Si isotopic compositions and typical source-specific fingerprints of PM2.5 and its sources. We show that the primary PM2.5 emissions (PM2.5 emitted directly from sources) actually had no reduction but redistribution during these pandemic periods, rather than the previous thought of being greatly reduced. This finding provided critical evidence to interpret the anomalistic PM2.5 increase during the pandemic periods in north China. Our results also suggested that both the energy structure adjustment and stringent regulations on primary emissions should be synergistically implemented in a regional scale for clean air actions in China.

Mass spectrometry for multi-dimensional characterization of natural and synthetic materials at the nanoscale.

Characterization of materials at the nanoscale plays a crucial role in in-depth understanding the nature and processes of the substances. Mass spectrometry (MS) has characterization capabilities for nanomaterials (NMs) and nanostructures by offering reliable multi-dimensional information consisting of accurate mass, isotopic, and molecular structural information. In the last decade, MS has emerged as a powerful nano-characterization technique. This review comprehensively summarizes the capabilities of MS in various aspects of nano-characterization that greatly enrich the toolbox of nano research. Compared with other characterization techniques, MS has unique capabilities for real-time monitoring and tracking reaction intermediates and by-products. Moreover, MS has shown application potential in some novel aspects, such as MS imaging of the biodistribution and fate of NMs in animals and humans, stable isotopic tracing of NMs, and risk assessment of NMs, which deserve update and integration into the current knowledge framework of nano-characterization.

Identification, Quantification, and Imaging of the Biodistribution of Soot Particles by Mass Spectral Fingerprinting.

Soot is ubiquitous and has large detrimental effects on climate, air quality, and human health. However, identification of soot in carbonaceous media is very challenging due to its nanoscale carbon nature and complex sources. Due to the shortage in the methodology, until now, the fate and health effect of soot particles after inhalation are still poorly understood. Here, we report a new method for label-free identification, quantification, and imaging of soot particles in complex media based on laser desorption/ionization mass spectrometry fingerprinting. We found that soot particles from different origins and with different morphologies showed highly consistent mass spectral fingerprints deriving from peak ratios of small carbon cluster anions (C2--C10-), which enabled both accurate quantification of soot in fine particulate matter (PM2.5) samples and label-free imaging of soot particles in biological media. By using this technique, we tracked and imaged the suborgan distribution of soot particles in mice after exposure to PM2.5. The results showed that the lung is the main target organ for short-term inhalation exposure to soot particles. This study helps to better understand the inhalation toxicology of soot and also provides a practical novel methodological platform for identification, tracing, and toxicological studies of elemental carbon-based nanomaterials.

Experimental Quantum Principal Component Analysis via Parametrized Quantum Circuits.

Principal component analysis (PCA) is a widely applied but rather time-consuming tool in machine learning techniques. In 2014, Lloyd, Mohseni, and Rebentrost proposed a quantum PCA (qPCA) algorithm [Lloyd, Mohseni, and Rebentrost, Nat. Phys. 10, 631 (2014)NPAHAX1745-247310.1038/nphys3029] that still lacks experimental demonstration due to the experimental challenges in preparing multiple quantum state copies and implementing quantum phase estimations. Here, we propose a new qPCA algorithm using the hybrid classical-quantum control, where parameterized quantum circuits are optimized with simple measurement observables, which significantly reduces the experimental complexity. As one important PCA application, we implement a human face recognition process using the images from the Yale Face Dataset. By training our quantum processor, the eigenface information in the training dataset is encoded into the parameterized quantum circuit, and the quantum processor learns to recognize new face images from the test dataset with high fidelities. Our work paves a new avenue toward the study of qPCA applications in theory and experiment.

COVID-19-Induced Lockdowns Indicate the Short-Term Control Effect of Air Pollutant Emission in 174 Cities in China.

The contradiction between the regional imbalance and an one-size-fits-all policy is one of the biggest challenges in current air pollution control in China. With the recent implementation of first-level public health emergency response (FLPHER) in response to the COVID-19 pandemic in China (a total of 77 041 confirmed cases by February 22, 2020), human activities were extremely decreased nationwide and almost all economic activities were suspended. Here, we show that this scenario represents an unprecedented "base period" to probe the short-term emission control effect of air pollution at a city level. We quantify the FLPHER-induced changes of NO2, SO2, PM2.5, and PM10 levels in 174 cities in China. A machine learning prediction model for air pollution is established by coupling a generalized additive model, random effects meta-analysis, and weather research and forecasting model with chemistry analysis. The short-term control effect under the current energy structure in each city is estimated by comparing the predicted and observed results during the FLPHER period. We found that the short-term emission control effect ranges within 53.0%-98.3% for all cities, and southern cities show a significantly stronger effect than northern cities (P < 0.01). Compared with megacities, small-medium cities show a similar control effect on NO2 and SO2 but a larger effect on PM2.5 and PM10.

Fatigue Detection with Spatial-Temporal Fusion Method on Covariance Manifolds of Electroencephalography.

With the increasing pressure of current life, fatigue caused by high-pressure work has deeply affected people and even threatened their lives. In particular, fatigue driving has become a leading cause of traffic accidents and deaths. This paper investigates electroencephalography (EEG)-based fatigue detection for driving by mining the latent information through the spatial-temporal changes in the relations between EEG channels. First, EEG data are partitioned into several segments to calculate the covariance matrices of each segment, and then we feed these matrices into a recurrent neural network to obtain high-level temporal information. Second, the covariance matrices of whole signals are leveraged to extract two kinds of spatial features, which will be fused with temporal characteristics to obtain comprehensive spatial-temporal information. Experiments on an open benchmark showed that our method achieved an excellent classification accuracy of 93.834% and performed better than several novel methods. These experimental results indicate that our method enables better reliability and feasibility in the detection of fatigued driving.