[Differences of Three Methods in Determining Ozone Sensitivity in Nanjing].

In recent years， ozone （O3） has become an increasingly important air pollutant in China. Identifying the sensitivity of O3 to the precursors volatile organic compounds （VOCs） and nitrogen oxides （NOx） can help make effective abatement strategies. This study compared three methods for determining O3-VOCs-NOx sensitivity： simulated photochemical indicator values and sensitivity coefficients derived from a three-dimensional air quality model and an observation-based model （OBM）， with a case study involving an O3 pollution event that occurred in Nanjing in late July 2017. The results showed that O3 sensitivity based on the photochemical indicator and sensitivity coefficients demonstrated similar spatial variations （over 50% of the grid cells of Nanjing exhibiting identical O3 sensitivity）. However， sensitivity coefficients identified a larger number of areas within a transitional O3 sensitivity regime， as opposed to the VOCs- or NOx-limited regime identified by the photochemical indicator. The determination of the latter was affected by the adopted threshold values. The OBM relied on the quality of the observational data. For example， positive biases in observed NO2 could lead to an underestimation of O3 sensitivity to NOx with the OBM. During the high pollution period， the three methods exhibited significant disparities. The photochemical indicator tended to suggest the VOCs-limited condition， whereas the OBM and sensitivity coefficients indicated the NOx-limited or transitional regimes.

Revealing the Denoising Principle of Zero-Shot N2N-Based Algorithm from 1D Spectrum to 2D Image.

Denoising is a necessary step in image analysis to extract weak signals, especially those hardly identified by the naked eye. Unlike the data-driven deep-learning denoising algorithms relying on a clean image as the reference, Noise2Noise (N2N) was able to denoise the noise image, providing sufficiently noise images with the same subject but randomly distributed noise. Further, by introducing data augmentation to create a big data set and regularization to prevent model overfitting, zero-shot N2N-based denoising was proposed in which only a single noisy image was needed. Although various N2N-based denoising algorithms have been developed with high performance, their complicated black box operation prevented the lightweight. Therefore, to reveal the working function of the zero-shot N2N-based algorithm, we proposed a lightweight Peak2Peak algorithm (P2P) and qualitatively and quantitatively analyzed its denoising behavior on the 1D spectrum and 2D image. We found that the high-performance denoising originates from the trade-off balance between the loss function and regularization in the denoising module, where regularization is the switch of denoising. Meanwhile, the signal extraction is mainly from the self-supervised characteristic learning in the data augmentation module. Further, the lightweight P2P improved the denoising speed by at least ten times but with little performance loss, compared with that of the current N2N-based algorithms. In general, the visualization of P2P provides a reference for revealing the working function of zero-shot N2N-based algorithms, which would pave the way for the application of these algorithms toward real-time (in situ, in vivo, and operando) research improving both temporal and spatial resolutions. The P2P is open-source at https://github.com/3331822w/Peak2Peakand will be accessible online access at https://ramancloud.xmu.edu.cn/tutorial.

A Superconducting Micro-Magnetometer for Quantum Vortex in Superconducting Nanoflakes.

Superconducting quantum interferometer device (SQUID) plays a key role in understanding electromagnetic properties and emergent phenomena in quantum materials. The technological appeal of SQUID is that its detection accuracy for the electromagnetic signal can precisely reach the quantum level of a single magnetic flux. However, conventional SQUID techniques normally can only be applied to a bulky sample and do not have the capability to probe the magnetic properties of micro-scale samples with small magnetic signals. Herein, it is demonstrated that, based on a specially designed superconducting nano-hole array, the contactless detection of magnetic properties and quantized vortices in micro-sized superconducting nanoflakes is realized. An anomalous hysteresis loop and a suppression of Little-Parks oscillation are observed in the detected magnetoresistance signal, which originates from the disordered distribution of the pinned vortices in Bi2 Sr2 CaCu2 O8+δ . Therefore, the density of pinning centers of the quantized vortices on such micro-sized superconducting samples can be quantitatively evaluated, which is technically inaccessible for conventional SQUID detection. The superconducting micro-magnetometer provides a new approach to exploring mesoscopic electromagnetic phenomena of quantum materials.

Supramolecular copolymerization through self-correction of non-polymerizable transient intermediates.

Kinetic control over structures and functions of complex assembly systems has aroused widespread interest. Understanding the complex pathway and transient intermediates is helpful to decipher how multiple components evolve into complex assemblies. However, for supramolecular polymerizations, thorough and quantitative kinetic analysis is often overlooked. Challenges remain in collecting the information of structure and content of transient intermediates in situ with high temporal and spatial resolution. Here, the unsolved evolution mechanism of a classical self-sorting supramolecular copolymerization system was addressed by employing multidimensional NMR techniques coupled with a microfluidic technique. Unexpected complex pathways were revealed and quantitatively analyzed. A counterintuitive pathway involving polymerization through the 'error-correction' of non-polymerizable transient intermediates was identified. Moreover, a 'non-classical' step-growth polymerization process controlled by the self-sorting mechanism was unraveled based on the kinetic study. Realizing the existence of transient intermediates during self-sorting can encourage the exploitation of this strategy to construct kinetic steady state assembly systems. Moreover, the strategy of coupling a microfluidic technique with various characterization techniques can provide a kinetic analysis toolkit for versatile assembly systems. The combined approach of coupling thermodynamic and kinetic analyses is indispensable for understanding the assembly mechanisms, the rules of emergence, and the engineering of complex assembly systems.

Modeling particulate nitrate in China: Current findings and future directions.

Particulate nitrate (pNO3) is now becoming the principal component of PM2.5 during severe winter haze episodes in many cities of China. To gain a comprehensive understanding of the key factors controlling pNO3 formation and driving its trends, we reviewed the recent pNO3 modeling studies which mainly focused on the formation mechanism and recent trends of pNO3 as well as its responses to emission controls in China. The results indicate that although recent chemical transport models (CTMs) can reasonably capture the spatial-temporal variations of pNO3, model-observation biases still exist due to large uncertainties in the parameterization of dinitrogen pentoxide (N2O5) uptake and ammonia (NH3) emissions, insufficient heterogeneous reaction mechanism, and the predicted low sulfate concentrations in current CTMs. The heterogeneous hydrolysis of N2O5 dominates nocturnal pNO3 formation, however, the contribution to total pNO3 varies among studies, ranging from 21.0% to 51.6%. Moreover, the continuously increasing PM2.5 pNO3 fraction in recent years is mainly due to the decreased sulfur dioxide emissions, the enhanced atmospheric oxidation capacity (AOC), and the weakened nitrate deposition. Reducing NH3 emissions is found to be the most effective control strategy for mitigating pNO3 pollution in China. This review suggests that more field measurements are needed to constrain the parameterization of heterogeneous N2O5 and nitrogen dioxide (NO2) uptake. Future studies are also needed to quantify the relationships of pNO3 to AOC, O3, NOx, and volatile organic compounds (VOCs) in different regions of China under different meteorological conditions. Research on multiple-pollutant control strategies involving NH3, NOX, and VOCs is required to mitigate pNO3 pollution, especially during severe winter haze events.

Multiscale photoacoustic imaging without motion using single-pixel imaging.

The conventional photoacoustic microscopy usually uses mechanical raster scanning to obtain three-dimensional information, and the imaging speed is limited. Meanwhile, the conventional photoacoustic microscopy can only be performed at one single scale due to fixed resolution, it cannot make full use of multiscale information for integrated imaging. Here, we proposed a multiscale photoacoustic microscopy based on single-pixel imaging. A sequence of sinusoidal fringes with varying spatial frequencies is used to obtain the Fourier coefficients in the case of a single ultrasonic transducer. By controlling the spatial frequency of fringe, the acquisition of Fourier coefficients can be controlled and multiscale imaging can be achieved. The feasibility of this method is verified by theory and simulation. The results show that the lateral resolution can be tuned from several microns to tens of microns without mechanical scanning. This method will expand the application of photoacoustic imaging in biomedical research.

Dynamic Traffic Assignment Model Based on GPS Data and Point of Interest (POI) in Shanghai.

Dynamic traffic flow, which can facilitate the efficient operation of traffic road networks, is an important prerequisite for the application of reasonable assignment of traffic demands in an urban road network. In order to improve the accuracy of dynamic traffic flow assignment, this paper proposes a dynamic traffic flow assignment model based on GPS trajectory data and the influence of POI. First, this paper explores the impact patterns of POI on regional road network congestion during peak hours through qualitative and quantitative analysis. Then, based on the user equilibrium theory, a dynamic traffic flow assignment model, in which the effect of POI on links is reflected using the link-node impedance function, is proposed. Finally, the accuracy of the model is validated by the GPS trajectory data and origin-destination (OD) traffic data of motor vehicles in Xuhui District, Shanghai, China. The results show that the model can be used to coordinate and optimize the traffic assignment of the regional road network under the influence of POI during peak hours and alleviate the congestion of the road network. The findings can provide a powerful reference for developing scientific and rational traffic assignment decisions and management strategies for urban road network traffic.

Developing a Peak Extraction and Retention (PEER) Algorithm for Improving the Temporal Resolution of Raman Spectroscopy.

In spectroscopic analysis, push-to-the-limit sensitivity is one of the important topics, particularly when facing the qualitative and quantitative analyses of the trace target. Normally, the effective recognition and extraction of weak signals are the first key steps, for which there has been considerable effort in developing various denoising algorithms for decades. Nevertheless, the lower the signal-to-noise ratio (SNR), the greater the deviation of the peak height and shape during the denoising process. Therefore, we propose a denoising algorithm along with peak extraction and retention (PEER). First, both the first and second derivatives of the Raman spectrum are used to determine Raman peaks with a high SNR whose peak information is kept away from the denoising process. Second, an optimized window smoothing algorithm is applied to the left part of the Raman spectrum, which is combined with the untreated Raman peaks to obtain the denoised Raman spectrum. The PEER algorithm is demonstrated with much better signal extraction and retention and successfully improves the temporal resolution of Raman imaging of a living cell by at least 1 order of magnitude higher than those by traditional algorithms.

Improving SERS Sensitivity toward Trace Sulfonamides: The Key Role of Trade-Off Interfacial Interactions among the Target Molecules, Anions, and Cations on the SERS Active Surface.

In recent years, ensuring the rational use and effective control of antibiotics has been a major focus in the eco-environment, which requires an effective monitoring method. However, on-site rapid detection of antibiotics in water environments remains a challenging issue. In this study, surface-enhanced Raman spectroscopy (SERS) was used to systematically achieve selective, rapid, and highly sensitive detection of sulfonamides, based on their fingerprint characteristics. The results show that the trade-off between the competitive and coadsorption behaviors of target molecules and agglomerates (inorganic salts) on the surface of the SERS substrate determines whether the molecules can be detected with high sensitivity. Based on this, the qualitative differentiation and quantitative detection of three structurally similar antibiotics, sulfadiazine, sulfamerazine, and sulfamethazine, were achieved, with the lowest detectable concentration being 1 µg/L for sulfadiazine and 50 µg/L for sulfamerazine and sulfamethazine.

Revealing unconventional host-guest complexation at nanostructured interface by surface-enhanced Raman spectroscopy.

Interfacial host-guest complexation offers a versatile way to functionalize nanomaterials. However, the complicated interfacial environment and trace amounts of components present at the interface make the study of interfacial complexation very difficult. Herein, taking the advantages of near-single-molecule level sensitivity and molecular fingerprint of surface-enhanced Raman spectroscopy (SERS), we reveal that a cooperative effect between cucurbit[7]uril (CB[7]) and methyl viologen (MV2+2I-) in aggregating Au NPs originates from the cooperative adsorption of halide counter anions I-, MV2+, and CB[7] on Au NPs surface. Moreover, similar SERS peak shifts in the control experiments using CB[n]s but with smaller cavity sizes suggested the occurrence of the same guest complexations among CB[5], CB[6], and CB[7] with MV2+. Hence, an unconventional exclusive complexation model is proposed between CB[7] and MV2+ on the surface of Au NPs, distinct from the well-known 1:1 inclusion complexation model in aqueous solutions. In summary, new insights into the fundamental understanding of host-guest interactions at nanostructured interfaces were obtained by SERS, which might be useful for applications related to host-guest chemistry in engineered nanomaterials.

Virtual optical-resolution photoacoustic microscopy using the k-Wave method.

Deep learning has been widely used in image processing, quantitative analysis, and other applications in optical-resolution photoacoustic microscopy (OR-PAM). It requires a large amount of photoacoustic data for training and testing. However, due to the complex structure, high cost, slow imaging speed, and other factors of OR-PAM, it is difficult to obtain enough data required by deep learning, which limits the research of deep learning in OR-PAM to a certain extent. To solve this problem, a virtual OR-PAM based on k-Wave is proposed. The virtual photoacoustic microscopy mainly includes the setting of excitation light source and ultrasonic probe, scanning and signal processing, which can realize the common Gaussian-beam and Bessel-beam OR-PAMs. The system performance (lateral resolution, axial resolution, and depth of field) was tested by imaging a vertically tilted fiber, and the effectiveness and feasibility of the virtual simulation platform were verified by 3D imaging of the virtual vascular network. The ability to the generation of the dataset for deep learning was also verified. The construction of the virtual OR-PAM can promote the research of OR-PAM and the application of deep learning in OR-PAM.

Key Role of Direct Adsorption on SERS Sensitivity: Synergistic Effect among Target, Aggregating Agent, and Surface with Au or Ag Colloid as Surface-Enhanced Raman Spectroscopy Substrate.

It is widely accepted that the sensitivity of surface-enhanced Raman spectroscopy (SERS) is mainly manipulated by the electromagnetic enhancement mechanism (EM). Herein, we determined that the direct adsorption of the target on the SERS active surface is vital as well, through the systematic investigation of the SERS behavior of three positively charged molecules on negatively charged gold (Au) or silver nanoparticles (Ag NPs). Facilitated by the synergistic effect among the molecule, the surface, and the specific adsorbed halide ions (Cl-, Br-, and I-), high SERS sensitivity for trace target was realized, which was mainly from the directly adsorbed molecules. Noteworthy, little contribution from the nondirectly adsorbed molecules was discernible, although the EM enhancement was at the same level for these two surface species dwelling within a distance significantly less than 1 nm from the surface. Further, the related strategy for trace detection sheds light on how to realize sensitive SERS detection of new targets.

Tape-Assisted Photolithographic-Free Microfluidic Chip Cell Patterning for Tumor Metastasis Study.

Cancer metastatic dissemination is a complex event during tumor progression which involves cell-cell and cell-matrix interactions. Micropatterning is one of the most efficient ways to study tumor development because it can tune the distribution of cells with spatial and temporal control. Extensive studies have shown that microfluidics can provide a feasible method for cell patterning. However, the current technique requires a microfabrication laboratory to manufacture the chip, which results in inaccessibility to researchers, especially biologists who focus on disclosing biological mechanisms rather than the methods. In this work, we developed a new methodology (tape-assisted photolithographic-free microfluidic chip, TAPMiC) that can realize homogeneous and heterogeneous micropatterning (45 features, 300 µm diameter of each) on a culture dish without the photolithographic procedure. We have applied this method to study critical biological problems, such as tumor cell migration under different conditions, including antitumor pharmaceutics and candidate gene RNAi assay that was relevant to tumor translocation and invasion. Moreover, this platform can achieve copatterning to recapitulate the tumor invasion scenario with single-cell trackable analysis. To decode regulation during metastasis, we conducted in situ recovering for quantitative polymerase chain reaction (qPCR) analysis from each cell type from tumor-fibroblast copairing. Regulation of several essential genes has unveiled that matrix degradation gene MMP2 and angiogenesis associated gene VEGFA were up-regulated in tumor cells in the fibroblast-enriched niche compared with homogeneous cultivation. Therefore, this approach constitutes a novel tool for investigating metastasis with quantitative measurements both on phenotype and genetical information.