Density Analysis of Adsorption Phase in the Thermodynamic Study of Shale Gas Adsorption.

Understanding the adsorption mechanism and precisely predicting the thermodynamic adsorption properties of methane at high pressure are crucial while very challenging for shale gas development. In this study, we demonstrated that the Langmuir adsorption model combining with different empirical methods to determine the adsorption phase density makes the calculated isothermal adsorption heat violate Henry's law at low pressure. For instance, the isothermal adsorption heat calculated by the Langmuir-Freundlich model contradicts Henry's law when the absolute adsorption quantity is zero. Given the current challenge in accurately calculating the adsorption phase density, it is necessary to impose constraints on the parameters of the adsorption model by adhering to Henry's law to maintain thermodynamic consistency. We found that the adsorption phase volume of methane molecules lies between the micropore volume and the total pore volume when shale adsorption reaches saturation. The adsorption mechanism involves not only filling micropores but also monolayer adsorption in meso-macro pores. The high-energy adsorption sites for methane are primarily concentrated in organic matter, while within these methane adsorption areas in shale, the high-energy adsorption sites for water are mainly located in kaolinite within clay minerals. The zero-pressure heat of adsorption is a temperature-independent thermodynamic index, yet it is influenced by the water content. It can therefore be selected as a quantitative measure to evaluate the impact of methane adsorption on water.

A stimuli-responsive hydrogel for reversible information storage, encryption and decryption.

Smart hydrogel materials, known for their sensitivity to external stimuli, exhibit a reversible dynamic response and find applications in diverse fields, particularly in information storage. Despite significant efforts in this domain, developing a hydrogel with high-resolution, repeatable recording, and robust information encryption/decryption capabilities still remains a challenge. In this study, we synthesized a polymer hydrogel, namely polyvinyl alcohol-n-isopropylacrylamide-octadecyl polyoxyethylene ether acrylate hydrogel (PPNS), which features multiple hydrogen bonds through copolymerization, by using N-isopropylacrylamide, polyvinyl alcohol, and octadecyl polyoxyethylene ether acrylate (SGA15) as raw materials. The PPNS hydrogel demonstrated outstanding high-resolution, repeatable recording capabilities, enabling reversible recording, encryption, and decryption of information using anhydrous ethanol as the inducer. Varying the SGA15 monomer concentration revealed that the PPNS-2% hydrogel, prepared with 2% SGA15, outperformed the other hydrogels in terms of information recording and encryption/decryption when immersed in anhydrous ethanol and deionized water. Furthermore, the PPNS-2% hydrogel exhibited the ability to undergo multiple information cycles while maintaining excellent mechanical properties even after 25 cycles. Notably, ethanol served as a specialized ink for inscribing different patterns on the hydrogel surface for information recording. The recorded information could be erased through water wiping or ethanol volatilization, enabling reversible information recording, encryption, and decryption. Due to their responsive and dynamic nature of PPNS hydrogels are positions them as promising candidates for use as innovative information storage platforms.

Study on Multifunctional Drag-Reducing Suspension Agents.

In the continuous liquid distribution process, the emulsion drag-reducing agent has poor viscosity-increasing ability and a low solid content, resulting in a high concentration and high cost. To solve this problem, a nanosuspension agent with a "shelf structure," a dispersion accelerator, and a density regulator were used as auxiliary agents to realize the stable suspension of a polymer dry powder in an oil phase. The results show that the molecular weight of the synthesized polymer powder could reach nearly 28 million when the mass ratio of acrylamide (AM) to acrylic acid (AA) was 80:20 and a chain extender was added. The synthesized polymer powder was dissolved in tap water and 2% brine separately, and the viscosity of the solutions was measured. The dissolution rate of up to 90% was reached at 30 °C, and the viscosity was 33 and 23 mPa s in tap water and in 2% brine, respectively. A stable suspension can be obtained without obvious stratification in one week and with good dispersion after 6 months by using the following composition: 37% oil phase + 1% nanosuspension agent + 10% dispersion accelerator + 50% polymer dry powder + 2% density regulator. The drag-reduction performance is good, remaining close to 73% with increasing time. The viscosity of the suspension solution is 21 mPa s in 50% standard brine, and the salt resistance is good. The rate at which the suspension fracturing fluid damages the formation is 7.56%, and the reservoir damage is unsubstantial. Its performance in field applications illustrated that its sand-carrying capacity, referring to the capacity of the fracturing fluid to carry proppants into the fracture and place them in a predetermined position, reaches 10%. The results show that the fracturing fluid can be used as a pre-fluid to break the formation, form fractures, and expand fracture networks under low-viscosity conditions and can be used as a sand-carrying fluid to carry proppants into the formation under high-viscosity conditions. Additionally, the fracturing fluid can directly realize the fast conversion between high and low viscosities and allow for multiple uses of one agent.

Rheological properties of an ultra-high salt hydrophobic associated polymer as a fracturing fluid system.

Herein, a novel ultra-high salt hydrophobic associated polymer, UUCPAM, was prepared using acrylamide, acrylic acid, 2-acrylamide-2-methyl propane sulfonic acid and the hydrophobic monomer UUC. Polymerization exothermic test results indicated that the increase in the hydrophobic monomer content led to an increase in the exothermic time, which is considerably conducive to the formation of hydrophobic structures. The scanning electron microscopy and transmission electron microscopy studies showed that the polymer had complex network structures and that this phenomenon was considerably obvious in NaCl solution. The fluorescence probe experiment verified that the critical association concentration of this polymer decreased with an increase in the hydrophobic monomer. Rheology studies indicated that the polymer had good temperature and shear resistance in NaCl solution. Moreover, the apparent viscosity of the polymer remained above 80 mPa s when 0.3 wt% UUCPAM was added at 170 s-1 in 20 000 mg L-1 NaCl solution at 90 °C. The storage modulus that indicated strong elasticity increased with an increase in the polymer concentration. Meanwhile, the number of hydrophobic micro-zones increased, thus forming dense network structures. Therefore, the polymer was found to have excellent salt resistance and extensive application prospects.

Histopathological Whole Slide Image Analysis Using Context-Based CBIR.

Histopathological image classification (HIC) and content-based histopathological image retrieval (CBHIR) are two promising applications for the histopathological whole slide image (WSI) analysis. HIC can efficiently predict the type of lesion involved in a histopathological image. In general, HIC can aid pathologists in locating high-risk cancer regions from a WSI by providing a cancerous probability map for the WSI. In contrast, CBHIR was developed to allow searches for regions with similar content for a region of interest (ROI) from a database consisting of historical cases. Sets of cases with similar content are accessible to pathologists, which can provide more valuable references for diagnosis. A drawback of the recent CBHIR framework is that a query ROI needs to be manually selected from a WSI. An automatic CBHIR approach for a WSI-wise analysis needs to be developed. In this paper, we propose a novel aided-diagnosis framework of breast cancer using whole slide images, which shares the advantages of both HIC and CBHIR. In our framework, CBHIR is automatically processed throughout the WSI, based on which a probability map regarding the malignancy of breast tumors is calculated. Through the probability map, the malignant regions in WSIs can be easily recognized. Furthermore, the retrieval results corresponding to each sub-region of the WSIs are recorded during the automatic analysis and are available to pathologists during their diagnosis. Our method was validated on fully annotated WSI data sets of breast tumors. The experimental results certify the effectiveness of the proposed method.

Generating region proposals for histopathological whole slide image retrieval.

BACKGROUND AND OBJECTIVE: Content-based image retrieval is an effective method for histopathological image analysis. However, given a database of huge whole slide images (WSIs), acquiring appropriate region-of-interests (ROIs) for training is significant and difficult. Moreover, histopathological images can only be annotated by pathologists, resulting in the lack of labeling information. Therefore, it is an important and challenging task to generate ROIs from WSI and retrieve image with few labels. METHODS: This paper presents a novel unsupervised region proposing method for histopathological WSI based on Selective Search. Specifically, the WSI is over-segmented into regions which are hierarchically merged until the WSI becomes a single region. Nucleus-oriented similarity measures for region mergence and Nucleus-Cytoplasm color space for histopathological image are specially defined to generate accurate region proposals. Additionally, we propose a new semi-supervised hashing method for image retrieval. The semantic features of images are extracted with Latent Dirichlet Allocation and transformed into binary hashing codes with Supervised Hashing. RESULTS: The methods are tested on a large-scale multi-class database of breast histopathological WSIs. The results demonstrate that for one WSI, our region proposing method can generate 7.3 thousand contoured regions which fit well with 95.8% of the ROIs annotated by pathologists. The proposed hashing method can retrieve a query image among 136 thousand images in 0.29 s and reach precision of 91% with only 10% of images labeled. CONCLUSIONS: The unsupervised region proposing method can generate regions as predictions of lesions in histopathological WSI. The region proposals can also serve as the training samples to train machine-learning models for image retrieval. The proposed hashing method can achieve fast and precise image retrieval with small amount of labels. Furthermore, the proposed methods can be potentially applied in online computer-aided-diagnosis systems.

Size-Scalable Content-Based Histopathological Image Retrieval From Database That Consists of WSIs.

Content-based image retrieval (CBIR) has been widely researched for histopathological images. It is challenging to retrieve contently similar regions from histopathological whole slide images (WSIs) for regions of interest (ROIs) in different size. In this paper, we propose a novel CBIR framework for database that consists of WSIs and size-scalable query ROIs. Each WSI in the database is encoded into a matrix of binary codes. When retrieving, a group of region proposals that have similar size with the query ROI are firstly located in the database through an efficient table-lookup approach. Then, these regions are ranked by a designed multi-binary-code-based similarity measurement. Finally, the top relevant regions and their locations in the WSIs as well as the corresponding diagnostic information are returned to assist pathologists. The effectiveness of the proposed framework is evaluated on a fine-annotated WSI database of epithelial breast tumors. The experimental results have proved that the proposed framework is effective for retrieval from database that consists of WSIs. Specifically, for query ROIs of 4096 4096 pixels, the retrieval precision of the top 20 return has reached 96% and the retrieval time is less than 1.5 s.

Breast Histopathological Image Retrieval Based on Latent Dirichlet Allocation.

In the field of pathology, whole slide image (WSI) has become the major carrier of visual and diagnostic information. Content-based image retrieval among WSIs can aid the diagnosis of an unknown pathological image by finding its similar regions in WSIs with diagnostic information. However, the huge size and complex content of WSI pose several challenges for retrieval. In this paper, we propose an unsupervised, accurate, and fast retrieval method for a breast histopathological image. Specifically, the method presents a local statistical feature of nuclei for morphology and distribution of nuclei, and employs the Gabor feature to describe the texture information. The latent Dirichlet allocation model is utilized for high-level semantic mining. Locality-sensitive hashing is used to speed up the search. Experiments on a WSI database with more than 8000 images from 15 types of breast histopathology demonstrate that our method achieves about 0.9 retrieval precision as well as promising efficiency. Based on the proposed framework, we are developing a search engine for an online digital slide browsing and retrieval platform, which can be applied in computer-aided diagnosis, pathology education, and WSI archiving and management.