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In shale reservoirs, the organic pores with various structures formed during the thermal evolution of organic matter are the main storage site for adsorbed methane. However, in the process of thermal evolution, the adsorption characteristics of methane in multi type and multi-scale organic matter pores have not been sufficiently studied. In this study, the molecular simulation method was used to study the adsorption characteristics of methane based on the geological conditions of Longmaxi Formation shale reservoir in Sichuan Basin, China. The results show that the characteristics of pore structure will affect the methane adsorption characteristics. The adsorption capacity of slit-pores for methane is much higher than that of cylindrical pores. The groove space inside the pore will change the density distribution of methane molecules in the pore, greatly improve the adsorption capacity of the pore, and increase the pressure sensitivity of the adsorption process. Although the variation of methane adsorption characteristics of different shapes is not consistent with pore size, all pores have the strongest methane adsorption capacity when the pore size is about 2 nm. In addition, the changes of temperature and pressure during the thermal evolution are also important factors to control the methane adsorption characteristics. The pore adsorption capacity first increases and then decreases with the increase of pressure, and increases with the increase of temperature. In the early stage of thermal evolution, pore adsorption capacity is strong and pressure sensitivity is weak; while in the late stage, it is on the contrary.

The object of the study is lignite. Analytical testing techniques, such as elemental analysis, 13C nuclear magnetic resonance (13C NMR) spectroscopy, Fourier transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS), and high-resolution transmission electron microscopy (HRTEM), were used to acquire information on the structural parameters of lignite. The aromaticity of Xianfeng lignite is 43.57%, and the aromatic carbon structure is mainly naphthalene and anthracene/phenanthrene. The aliphatic carbon structure is dominated by cycloalkanes, alkyl side chains, and hydrogenated aromatics. Oxygen is mainly present in ether oxygen, carboxyl, and carbonyl groups. Nitrogen is mainly in the form of pyrrole nitrogen and quaternary nitrogen. Sulfur is mainly thiophene sulfur. According to the analysis results, the molecular structure model of XF lignite was constructed. The molecular formula is C184H172O39N6S2. The 2D structure was converted to a 3D structure using computer simulation software and optimized. The optimized model has a remarkable stereoconfiguration, and the aromatic lamellae are irregularly arranged in space. The aromatic rings were mainly connected by methylene, hypomethylene, methoxy, and aliphatic rings. In addition, the simulated 13C NMR spectra are in good agreement with the experimental spectra. This shows the rationality of the 3D chemical structure model.

Neuronal cell types are essential to the comprehensive understanding of the neuronal function and neuron can be categorized by their anatomical property. However, complete morphology data for neurons with a whole brain projection, for example the pyramidal neurons in the cortex, are sparse because it is difficult to trace the neuronal fibers across the whole brain and acquire the neuron morphology at the single axon resolution. Thus the cell types of pyramidal neurons have yet to be studied at the single axon resolution thoroughly. In this work, we acquire images for a Thy1 H-line mouse brain using a fluorescence micro-optical sectioning tomography system. Then we sample 42 pyramidal neurons whose somata are in the layer 5 of the motor cortex and reconstruct their morphology across the whole brain. Based on the reconstructed neuronal anatomy, we analyze the axonal and dendritic fibers of the neurons in addition to the soma spatial distributions, and identify two axonal projection pattern of pyramidal tract neurons and two dendritic spreading patterns of intratelencephalic neurons. The raw image data are available upon request as an additional asset to the community. The morphological patterns identified in this work can be a typical representation of neuron subtypes and reveal the possible input-output function of a single pyramidal neuron.

Understanding amazingly complex brain functions and pathologies requires a complete cerebral vascular atlas in stereotaxic coordinates. Making a precise atlas for cerebral arteries and veins has been a century-old objective in neuroscience and neuropathology. Using micro-optical sectioning tomography (MOST) with a modified Nissl staining method, we acquired five mouse brain data sets containing arteries, veins, and microvessels. Based on the brain-wide vascular spatial structures and brain regions indicated by cytoarchitecture in one and the same mouse brain, we reconstructed and annotated the vascular system atlas of both arteries and veins of the whole mouse brain for the first time. The distributing patterns of the vascular system within the brain regions were acquired and our results show that the patterns of individual vessels are different from each other. Reconstruction and statistical analysis of the microvascular network, including derivation of quantitative vascular densities, indicate significant differences mainly in vessels with diameters less than 8 μm and large than 20 μm across different brain regions. Our precise cerebral vascular atlas provides an important resource and approach for quantitative studies of brain functions and diseases.

Understanding the pore structure of high-rank coals is essential in evaluating gas storage and transport. Here, twelve semianthracite samples from the early Permian Shanxi Formation were investigated by proximate analysis, optical microscopy, low-temperature N2 adsorption, and fractal analysis, coupled with diffusion coefficient modeling. The coals exhibit diverse pore types (plant-cellular, interparticle, and dissolution pores) shaped by coalification and minerals and show Type IV (a) isotherms with H4 hysteresis loops, indicating complex pore networks. Pore-size partitioning reveals that mesopores and macropores dominate total pore volume, whereas mesopores contribute most of the specific surface area. The pore structure exhibits strong fractal characteristics with an average comprehensive fractal dimension (Fc) of 2.628. The calculated gas diffusion coefficient decreases monotonically with increasing pressure from 1 MPa to 5.8 MPa, with a more pronounced decline at low pressure, indicating a clear pressure-dependent attenuation effect. Diffusion capacity is weakly related to average pore diameter but shows positive correlations with total pore volume and, particularly, macropore volume. Multiple linear regression further demonstrates that pore volume structure is the dominant control on diffusion under both low- and high-pressure conditions, with the relative importance ranked as macropores > mesopores > micropores. Macropores provide the main low-resistance transport framework, mesopores serve as transitional pathways linking storage and transport domains, whereas micropores mainly contribute to gas storage and may even suppress apparent diffusion when overly developed. These results reveal a clear functional differentiation of multiscale pore systems and highlight that gas migration in semianthracite is jointly governed by pore size distribution, connectivity, tortuosity, and fractal network topology.

As an advanced function of the human brain, emotion has a significant influence on human studies, works, and other aspects of life. Artificial Intelligence has played an important role in recognizing human emotion correctly. EEG-based emotion recognition (ER), one application of Brain Computer Interface (BCI), is becoming more popular in recent years. However, due to the ambiguity of human emotions and the complexity of EEG signals, the EEG-ER system which can recognize emotions with high accuracy is not easy to achieve. Based on the time scale, this paper chooses the recurrent neural network as the breakthrough point of the screening model. According to the rhythmic characteristics and temporal memory characteristics of EEG, this research proposes a Rhythmic Time EEG Emotion Recognition Model (RT-ERM) based on the valence and arousal of Long–Short-Term Memory Network (LSTM). By applying this model, the classification results of different rhythms and time scales are different. The optimal rhythm and time scale of the RT-ERM model are obtained through the results of the classification accuracy of different rhythms and different time scales. Then, the classification of emotional EEG is carried out by the best time scales corresponding to different rhythms. Finally, by comparing with other existing emotional EEG classification methods, it is found that the rhythm and time scale of the model can contribute to the accuracy of RT-ERM.

Neuronal soma segmentation is a crucial step for the quantitative analysis of neuronal morphology. Automated neuronal soma segmentation methods have opened up the opportunity to improve the time-consuming manual labeling required during the neuronal soma morphology reconstruction for large-scale images. However, the presence of touching neuronal somata and variable soma shapes in images brings challenges for automated algorithms. This study proposes a neuronal soma segmentation method combining 3D U-shaped fully convolutional neural networks with multi-task learning. Compared to existing methods, this technique applies multi-task learning to predict the soma boundary to split touching somata, and adopts U-shaped architecture convolutional neural network which is effective for a limited dataset. The contour-aware multi-task learning framework is applied to the proposed method to predict the masks of neuronal somata and boundaries simultaneously. In addition, a spatial attention module is embedded into the multi-task model to improve neuronal soma segmentation results. The Nissl-stained dataset captured by the micro-optical sectioning tomography system is used to validate the proposed method. Following comparison to four existing segmentation models, the proposed method outperforms the others notably in both localization and segmentation. The novel method has potential for high-throughput neuronal soma segmentation in large-scale optical imaging data for neuron morphology quantitative analysis.

Systematic cellular and vascular configurations are essential for understanding fundamental brain anatomy and metabolism. We demonstrated a 3D brainwide cellular and vascular (called 3D BrainCV) visualization and quantitative protocol for a whole mouse brain. We developed a modified Nissl staining method that quickly labeled the cells and blood vessels simultaneously in an entire mouse brain. Terabytes 3D datasets of the whole mouse brains, with unprecedented details of both individual cells and blood vessels, including capillaries, were simultaneously imaged at 1-μm voxel resolution using micro-optical sectioning tomography (MOST). For quantitative analysis, we proposed an automatic image-processing pipeline to perform brainwide vectorization and analysis of cells and blood vessels. Six representative brain regions from the cortex to the deep, including FrA, M1, PMBSF, V1, striatum, and amygdala, and six parameters, including cell number density, vascular length density, fractional vascular volume, distance from the cells to the nearest microvessel, microvascular length density, and fractional microvascular volume, had been quantitatively analyzed. The results showed that the proximity of cells to blood vessels was linearly correlated with vascular length density, rather than the cell number density. The 3D BrainCV made overall snapshots of the detailed picture of the whole brain architecture, which could be beneficial for the state comparison of the developing and diseased brain. •Faster brainwide Nissl staining for labeling cells and blood vessels•Terabytes 3D dataset of whole mouse brain with 1mm voxel resolution•Image-processing for brainwide analysis of cellular and vascular configuration.

Despite the implementation of hydraulic fracturing technologies, the oil recovery in tight oil reservoirs is still poor. In this study, cationic, anionic, and nonionic surfactants of various sorts were investigated to improve oil recovery in tight carbonate cores from the Middle Bakken Formation in the Williston Basin. Petrophysical investigations were performed on the samples prior to the imbibition and core-flooding experiments. The composition of the minerals was examined using the XRD technique. To investigate the pore-size distribution and microstructures, nitrogen adsorption and SEM techniques were applied. The next step involved brine and surfactant imbibition for six Bakken cores and two Berea sandstone cores. The core samples were completely saturated with Bakken crude oil prior to the experiments. The core plugs were then submerged into the brine and surfactant solutions. The volume of recovered oil was measured using imbibition cells as part of experiments involving brine and surfactant ingestion into oil-filled cores. According to the findings, oil recovery from brine imbibition ranges from 4.3% to 15%, whereas oil recovery from surfactant imbibition can range from 9% to 28%. According to the findings, core samples with more clay and larger pore diameters produce higher levels of oil recovery. Additionally, two tight Bakken core samples were used in core-flooding tests. Brine and a separate surfactant solution were the injected fluids. The primary oil recovery from brine flooding on core samples is between 23% and 25%, according to the results. The maximum oil recovery by second-stage surfactant flooding is approximately 33% and 35%. The anionic surfactants appear to yield a better oil recovery in tight Bakken rocks, possibly due to their higher carbonate mineral concentrations, especially clays, according to both the core-scale imbibition and flooding experiments. For studied samples with larger pore sizes, the oil recovery is higher. The knowledge of the impacts of mineral composition, pore size, and surfactant types on oil recovery in tight carbonate rocks is improved by this study.

Coal-bearing strata belong to sedimentary strata, and there are multiple aquifers. The accurate detection of deep aquifers is helpful to the safe mining of the working face. In order to provide guidance for the interpretation of the surface-to-underground transient electromagnetic method (SUTEM) that can be used to detect deep aquifers, we used theoretical analysis and numerical simulation methods in this study. Taking uniform half-spaces, single aquifers, and double aquifers as examples, we systematically studied the data characteristics and degree of influence of SUTEM under the influence of shallow aquifers. The results indicate the following: Under the influence of the primary field distribution, the x or y component of the induced electromotive force received by the underground receiving point has a positive and negative inflection point, which increases the difficulty of data interpretation, and the z component is easier to use for data interpretation. The influence of the aquifer on the early data of the underground receiving point is much greater than that of the ground receiving point, and the late influence is closer to the ground receiving point. The change in resistivity of the shallow aquifer has the greatest influence on the ability of each measuring point to detect the data of the deep aquifer; this influence is followed by change in thickness, and change in depth has the least influence on the detection capability of each measuring point.