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 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.

Sources and formation characteristics of particulate nitrate in the Pearl River Delta region of China: Insights from three-year online observations.

Nitrate (NO3-) has been identified as a key component of particulate matter (PM2.5) in China. However, there is still a lack of understanding regarding its sources and how it forms, especially in the context of high-frequency and long-term data. In this study, NO3- levels were observed on an hourly basis over an almost three-year period at an urban site in the Pearl River Delta (PRD) region, China, from January 2019 to December 2021. The results reveal an average daily NO3- concentration ranging from 0.08 µg m-3 to 61.69 µg m-3, constituting 11.9 ± 12.5 % of PM2.5. This percentage rose to as high as 57 % during pollution episodes, highlighting NO3-'s significant role in pollution formation. The ammonia-rich environment was found to be the most important factor in promoting NO3- formation. Positive Matrix Factorization (PMF) analysis indicates that the primary sources of NO3- in the PRD region were vehicle emissions (43.8 ± 21.2 %) and coal combustion (39.1 ± 21.5 %), with shipping emissions, sea salt, soil dust and industrial emissions + biomass burning following in importance. Regarding source areas, the primary contributor of vehicle emissions was predominantly from the PRD region, whereas the coal combustion, aside from local contributions, also originates from the northern region. From a long-term perspective, NO3- pollution has remained relatively stable since the summer of 2020. Concurrently, coal combustion source has shown a localization trend. These insights derived from the extensive, high-frequency observation presented in this study serve as a valuable reference for devising strategies to control NO3- and PM2.5 in the PRD region and China.

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.

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.

A new ChatGPT-empowered, easy-to-use machine learning paradigm for environmental science.

The quantity and complexity of environmental data show exponential growth in recent years. High-quality big data analysis is critical for performing a sophisticated characterization of the complex network of environmental pollution. Machine learning (ML) has been employed as a powerful tool for decoupling the complexities of environmental big data based on its remarkable fitting ability. Yet, due to the knowledge gap across different subjects, ML concepts and algorithms have not been well-popularized among researchers in environmental sustainability. In this context, we introduce a new research paradigm-"ChatGPT + ML + Environment", providing an unprecedented chance for environmental researchers to reduce the difficulty of using ML models. For instance, each step involved in applying ML models to environmental sustainability, including data preparation, model selection and construction, model training and evaluation, and hyper-parameter optimization, can be easily performed with guidance from ChatGPT. We also discuss the challenges and limitations of using this research paradigm in the field of environmental sustainability. Furthermore, we highlight the importance of "secondary training" for future application of "ChatGPT + ML + Environment".

Probing the stability of metal-organic frameworks by structure-responsive mass spectrometry imaging.

The widespread application of metal-organic frameworks (MOFs) is seriously hindered by their structural instability and it is still very challenging to probe the stability of MOFs during application by current techniques. Here, we report a novel structure-responsive mass spectrometry (SRMS) imaging technique to probe the stability of MOFs. We discovered that intact CuBTC (as a model of MOFs) could generate the characteristic peaks of organic ligands and carbon cluster anions in laser desorption/ionization mass spectrometry, but these peaks were significantly changed when the structure of CuBTC was dissociated, thus enabling a label-free probing of the stability. Furthermore, SRMS can be performed in imaging mode to visualize the degradation kinetics and reveal the spatial heterogeneity of the stability of CuBTC. This technique was successfully applied in different application scenarios (in water, moist air, and CO2) and also validated with different MOFs. It thus provides a versatile new tool for better design and application of environment-sensitive materials.

Electronic structure and optical properties of In- and Vacancy-doped 6H-SiC: a first-principles study.

PURPOSES: The paper aims to investigative the cacuses and impacts of In- and Vacancy-doped to 6H-SiC, expecting that improving optical properties of materials. Design-Using the first-principles calculations, we discuss the electronic structure and optical properties of different doped 6H-SiC systems. FINDINGS: The results show that In-doped 6H-SiC becomes a direct bandgap p-type semiconductor and the energy bandgap is reduced from the intrinsic 2.059 to 1.515 eV. We demonstrate the stability of the systems through the formation energy analysis, meanwhile identify their physical origins and discuss applications of all structures in electronic devices within optical analysis. Find the energy beginning values of the VSi-doped and VC-doped systems' optical absorption spectrums and extend to 0.4 2 eV and 0.11 eV respectively compared with the original 3.23 eV. In the visible light region, the reflectivity images of the VC/VSi and (In, VSi)-codoped systems rise obviously. CONCLUSIONS: The optical properties of all doping systems were analyzed to be improved compared with the intrinsic, all above mentioned provide a theoretical basis for the fabrication of spintronic and optical devices.

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.

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.

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.

Experimental quantum simulation of a topologically protected Hadamard gate via braiding Fibonacci anyons.

Topological quantum computation (TQC) is one of the most striking architectures that can realize fault-tolerant quantum computers. In TQC, the logical space and the quantum gates are topologically protected, i.e., robust against local disturbances. The topological protection, however, requires complicated lattice models and hard-to-manipulate dynamics; even the simplest system that can realize universal TQC-the Fibonacci anyon system-lacks a physical realization, let alone braiding the non-Abelian anyons. Here, we propose a disk model that can simulate the Fibonacci anyon system and construct the topologically protected logical spaces with the Fibonacci anyons. Via braiding the Fibonacci anyons, we can implement universal quantum gates on the logical space. Our disk model merely requires two physical qubits to realize three Fibonacci anyons at the boundary. By 15 sequential braiding operations, we construct a topologically protected Hadamard gate, which is to date the least-resource requirement for TQC. To showcase, we implement a topological Hadamard gate with two nuclear spin qubits, which reaches 97.18% fidelity by randomized benchmarking. We further prove by experiment that the logical space and Hadamard gate are topologically protected: local disturbances due to thermal fluctuations result in a global phase only. As a platform-independent proposal, our work is a proof of principle of TQC and paves the way toward fault-tolerant quantum computation.

Machine learning-aided metallomic profiling in serum and urine of thyroid cancer patients and its environmental implications.

The incidence rate of thyroid cancer has been growing worldwide. Thyroid health is closely related with multiple trace metals, and the nutrients are essential in maintaining thyroid function while the contaminants can disturb thyroid morphology and homeostasis. In this study, we conducted metallomic analysis in thyroid cancer patients (n = 40) and control subjects (n = 40) recruited in Shenzhen, China with a high incidence of thyroid cancer. We found significant alterations in serumal and urinary metallomic profiling (including Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Sr, Cd, I, Ba, Tl, and Pb) and elemental correlative patterns between thyroid cancer patients and controls. Additionally, we also measured the serum Cu isotopic composition and found a multifaceted disturbance in Cu metabolism in thyroid disease patients. Based on the metallome variations, we built and assessed the thyroid cancer-predictive performance of seven machine learning algorithms. Among them, the Random Forest model performed the best with the accuracy of 1.000, 0.858, and 0.813 on the training, 5-fold cross-validation, and test set, respectively. The high performance of machine learning has demonstrated the great promise of metallomic analysis in the identification of thyroid cancer. Then, the Shapley Additive exPlanations approach was used to further interpret the variable contributions of the model and it showed that serum Pb contributed the most in the identification process. To the best of our knowledge, this is the first study that combines machine learning and metallome data for cancer identification, and it supports the indication of environmental heavy metal-related thyroid cancer etiology.

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.

Metallomic profiling and natural copper isotopic signatures of childhood autism in serum and red blood cells.

Excessive exposure to metals directly threatens human health, including neurodeve lopment. Autism spectrum disorder (ASD) is a neurodevelopmental disorder, leaving great harms to children themselves, their families, and even society. In view of this, it is critical to develop reliable biomarkers for ASD in early childhood. Here we used inductively coupled plasma mass spectrometry (ICP-MS) to identify the abnormalities in ASD-associated metal elements in children blood. Multi-collector inductively coupled plasma mass spectrometry (MC-ICP-MS) was applied to detect isotopic differences in copper (Cu) for further assessment on account of its core role in the brain. We also developed a machine learning classification method for unknown samples based on a support vector machine (SVM) algorithm. The results indicated significant differences in the blood metallome (chromium (Cr), manganese (Mn), cobalt (Co), magnesium (Mg), and arsenic (As)) between cases and controls, and a significantly lower Zn/Cu ratio was observed in the ASD cases. Interestingly, we found a strong association of serum copper isotopic composition (δ65Cu) with autistic serum. SVM was successfully applied to discriminate cases and controls based on the two-dimensional Cu signatures (Cu concentration and δ65Cu) with a high accuracy (94.4%). Overall, our findings revealed a new biomarker for potential early diagnosis and screening of ASD, and the significant alterations in the blood metallome also helped to understand the potential pathogenesis of ASD in terms of metallomics.

Significantly enhanced photothermal catalytic CO2 reduction over TiO2/g-C3N4 composite with full spectrum solar light.

Using solar energy to drive catalytic conversion of CO2 into value-added chemicals has great potential to alleviate the global energy shortage and anthropogenic climate change. Herein, a "hitting three birds with one stone" strategy was reported to prepared boron-doped g-C3N4/TiO2-x composite (BCT) by a one-step thermal reduction process. A series of characterizations showed that the composite catalyst has extended full-spectrum absorption, rapid photogenerated charge separation, and outstanding CO2 photoreduction performance (265.2 µmol g-1h-1), which is 7.5 and 9.2 times higher than that of pure TiO2 and g-C3N4, respectively. In addition, the CO2 conversion rate can be further increased to 345.1 µmol g-1h-1 at 70 °C due to its excellent photothermal conversion. Mechanistic studies reveal that synergistic effects alter the charge density distribution, thereby lowering the energy barrier for CO2 conversion by adsorbing and activating CO2 molecules. This work provides a novel three-in-one integrated strategy for fabricating high-efficiency catalysts.

Spatial Allocation Method of Evacuation Guiders in Urban Open Public Spaces: A Case Study of Binjiang Green Space in Xuhui District, Shanghai, China.

Evacuation guiders play an important role when emergency events occur in urban open public spaces. Considering the shortcomings of the existing studies, an optimization method based on the Particle Swarm Optimization (PSO) algorithm and gradual covering model for spatial allocation of evacuation guiders in urban open public spaces is proposed. This method considers the impact of obstacles on intervisibility between guiders and evacuees, and the non-linear changing characteristics of the evacuation guiding quality based on the distances between guiders and evacuees to optimize the space allocation of evacuation guiders in urban open public spaces. Based on the emergency evacuation simulation, the evacuation efficiencies before and after the optimization of evacuation guider allocation can be compared to verify the validity of the proposed method. Furthermore, in order to improve the applicability of this method, the responsibility areas of the evacuation guiders are zoned according to different time periods. A case study of Binjiang Green Space in Xuhui District, Shanghai, China was conducted to demonstrate the feasibility of the proposed method. The results showed that the spatial allocation of evacuation guiders was highly correlated with the dynamic spatial change of evacuees. The reasonable spatial allocation optimization of evacuation guiders can effectively improve the emergency evacuation quality and reduce evacuation risks. The zoning of the evacuation guiders' responsibility areas can help to clarify the responsibility area of each guider and provide a daily safety precaution scheme under a limited number of guiders. The method can provide detailed decision support for the security precaution of security staff and emergency evacuation management in urban open public spaces.

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.

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.