CD8+ T-cell exhaustion: Impediment to triple-negative breast cancer (TNBC) immunotherapy.

CD8+ T-cell exhaustion has been identified as a significant contributor to immunosuppression and immune escape in triple-negative breast cancer (TNBC). Dysfunction due to cell exhaustion is characterized by reduced effector capacity and sustained expression of inhibitory receptors (IRs). The factors contributing to CD8+ T-cell exhaustion are multifaceted, encompassing external influences such as the upregulation of IRs, reduction of effector cytokines, and internal changes within the immune cell, including transcriptomic alterations, epigenetic landscape remodeling, and metabolomic shifts. The impact of the altered TNBC tumor microenvironment (TME) on Tex is also a critical consideration. The production of exhausted CD8+ T-cells (CD8+ Tex) is positively correlated with poor prognosis and reduced response rates to immunotherapy in TNBC patients, underscoring the urgent need for the development of novel TNBC immunotherapeutic strategies that target the mechanisms of CD8+ T-cell exhaustion. This review delineates the dynamic trajectory of CD8+ T-cell exhaustion development in TNBC, provides an update on the latest research advancements in understanding its pathogenesis, and offers insights into potential immunotherapeutic strategies.

Intelligent alarm system for river embankment seepage based on BILSTM.

Currently, the alarm functions of existing levee seepage monitoring systems are limited to single-parameter monitoring and lack rate-of-change alarms and correlation alarms. This can lead to false alarms, missed alarms, equipment failures, or unnecessary downtime. To enhance the intelligence of levee safety monitoring and seepage alarms, a levee seepage intelligent alarm system based on a Bidirectional Long Short-Term Memory (BILSTM) network model was designed and implemented. Firstly, data cleaning and preprocessing are carried out on the engineering safety monitoring operation data to reduce the influence of dirty data such as outliers and repetitive values on the accuracy of alarms. Secondly, for the correlation between the piezometric tube water levels of the levee and the Yangtze River water levels, a correlation analysis based on Mutual Information (MI) theory was conducted to minimize the effect of piezometric tube water level change delays on correlation. Finally, the BILSTM model was used to predict trends in these potentially abnormal data intervals. Based on engineering application requirements, alarm thresholds were established, and a multi-level alarm module was developed. Field operation test results show that the proposed method can accurately predict the piezometric tube water levels of levees, achieving intelligent alarms within the engineering safety monitoring system.

Denoising Multiscale Spectral Graph Wavelet Neural Networks for Gas Utilization Ratio Prediction in Blast Furnace.

Given the crucial role of the gas utilization ratio (GUR) in reflecting blast furnace operation and energy consumption, accurately predicting its development trend holds significant value for blast furnace operators. However, in the harsh ironmaking environment, GUR-affecting variables are prone to significant nonstationary noise. Moreover, these variables are coupled and correlated, meaning that improper regulation of one variable can destabilize the furnace and lead to substantial GUR fluctuations. This poses a major challenge for achieving accurate GUR prediction. To tackle this issue, this article proposes a denoising multiscale spectral graph wavelet neural network (DMSGWNN) for online dynamic forecasting of the GUR, which is an end-to-end learning method that removes variable noise and captures complex variable correlations simultaneously. First, a regularized self-representation (RSR) model is constructed to eliminate nonstationary noise in blast furnace process variables. Then, a novel multiscale spectral graph wavelet neural network (MSGWNN) is proposed to capture the complex correlations among input variables and extract their multiscale representations through spectral graph wavelet (SGW) transform with the heat kernel scaling function and Gaussian kernel wavelet functions. Finally, the effectiveness of the proposed DMSGWNN method is verified using actual blast furnace ironmaking process data from a blast furnace in China, achieving an average predictive hit rate (HR) as high as 98.06% for GUR prediction.

Research trends on nanomaterials in triple negative breast cancer (TNBC): a bibliometric analysis from 2010 to 2024.

Breast cancer (BC) is an important cause of cancer-related death in the world. As a subtype of BC with the worst prognosis, triple-negative breast cancer (TNBC) is a serious threat to human life and health. In recent years, there has been an increasing amount of research aimed at designing and developing nanomaterials for the diagnosis and treatment of TNBC. The purpose of this study was to comprehensively evaluate the current status and trend of the application of nanomaterials in TNBC through bibliometric analysis. Studies focusing on nanomaterials and cancer were searched from the Web of Science core collection (WOSCC) database, and relevant literature meeting the inclusion criteria was selected for inclusion in the study. VOSviewer and CiteSpace were used to perform bibliometric and visual analysis of the included publications. A total of 2338 studies were included. Annual publications have increased from 2010 to 2024. China, the United States and India were the leading countries in the field, accounting for 66.1%, 11.5% and 7.2% of publications, respectively. The Chinese Academy of Sciences and Li Yaping were the most influential institutions and authors, respectively. Journal of Controlled Release was considered the most productive journal. Cancer Research was considered to be the most co-cited journal. Drug delivery and anti-cancer mechanisms related to nanomaterials were considered to be the most widely studied aspects, and green synthesis and anti-cancer mechanisms were also recent research hotspots. In this study, the characteristics of publications were summarized, and the most influential countries, institutions, authors, journals, hot spots and trends in the application of nanomaterials in cancer were identified. These findings provide valuable insights into the current state and future direction of this dynamic field.

Microbe-drug association prediction model based on graph convolution and attention networks.

The human microbiome plays a key role in drug development and precision medicine, but understanding its complex interactions with drugs remains a challenge. Identifying microbe-drug associations not only enhances our understanding of their mechanisms but also aids in drug discovery and repurposing. Traditional experiments are expensive and time-consuming, making computational methods for predicting microbe-drug associations a new trend. Currently, computational methods specifically designed for this task are still scarce. Therefore, to address the shortcomings of traditional experimental methods in predicting potential microbe-drug associations, this paper proposes a new prediction model named GCNATMDA. The model combines two deep learning models, Graph Convolutional Network and Graph Attention Network, and aims to reveal potential relationships between microbes and drugs by learning related features. Thus improve the efficiency and accuracy of prediction. We first integrated the microbe-drug association matrix from the existing dataset, and then combined the calculated microbe-drug characteristic matrix as the model input. The GCN module is used to dig deeper into the potential characterization of microbes and drugs, while the GAT module further learns the more complex interactions between them and generates the corresponding score matrix. The experimental results show that the GCNATMDA model achieves 96.59% and 93.01% in AUC and AUPR evaluation indexes, respectively, which is significantly better than the existing prediction models. In addition, the reliability of the prediction results is verified by a series of experiments.

Rapid miRNA detection enhanced by exponential hybridization chain reaction in graphene field-effect transistors.

Scalable electronic devices that can detect target biomarkers from clinical samples hold great promise for point-of-care nucleic acid testing, but still cannot achieve the detection of target molecules at an attomolar range within a short timeframe (<1 h). To tackle this daunting challenge, we integrate graphene field-effect transistors (GFETs) with exponential target recycling and hybridization chain reaction (TRHCR) to detect oligonucleotides (using miRNA as a model disease biomarker), achieving a detection limit of 100 aM and reducing the sensing time by 30-fold, from 15 h to 30 min. In contrast to traditional linear TRHCR, our exponential TRHCR enables the target miRNA to initiate an autocatalytic system with exponential kinetics, significantly accelerating the reaction speed. The resulting reaction products, long-necked double-stranded polymers with a negative charge, are effectively detected by the GFET through chemical gating, leading to a shift in the Dirac voltage. Therefore, by monitoring the magnitude of this voltage shift, the target miRNA is quantified with high sensitivity. Consequently, our approach successfully detects 22-mer miRNA at concentrations as low as 100 aM in human serum samples, achieving the desired short timeframe of 30 min, which is congruent with point-of-care testing, and demonstrates superior specificity against single-base mismatched interfering oligonucleotides.

REV-ERBα Mitigates Astrocyte Activation and Protects Dopaminergic Neurons from Damage.

Parkinson's disease (PD) is characterized by astrocyte activation and disruptions in circadian rhythm. Within the astrocyte population, two distinct reactive states exist: A1 and A2. A1 astrocytes are associated with neurotoxicity and inflammation, while A2 astrocytes exhibit neuroprotective functions. Our investigation focused on the role of REV-ERBα, a member of the nuclear receptor superfamily and a key regulator of the circadian clock, in astrocyte activation. We observed that REV-ERBα expression in A1 astrocytes was reduced to one-third of its normal level. Notably, activation of REV-ERBα prompted a transformation of astrocytes from A1 to A2. Mechanistically, REV-ERBα inhibition was linked to the classical NF-κB pathway, while it concurrently suppressed the STAT3 pathway. Furthermore, astrocytes with low REV-ERBα expression were associated with dopaminergic neurons apoptosis. Intriguingly, the opposite effect was observed when using a REV-ERBα agonist, which mitigated astrocyte activation and reduced dopaminergic neuron damage by 50%. In summary, our study elucidates the pivotal role of REV-ERBα in modulating astrocyte function and its potential implications in PD pathogenesis.

Gasdermin C promotes Stemness and Immune Evasion in Pancreatic Cancer via Pyroptosis-Independent Mechanism.

Pancreatic ductal adenocarcinoma (PDAC) is a highly metastatic and lethal disease. Gasdermins are primarily associated with necrosis via membrane permeabilization and pyroptosis, a lytic pro-inflammatory type of cell death. In this study, GSDMC upregulation during PDAC progression is reported. GSDMC directly induces genes related to stemness, EMT, and immune evasion. Targeting Gsdmc in murine PDAC models reprograms the immunosuppressive tumor microenvironment, rescuing the recruitment of anti-tumor immune cells through CXCL9. This not only results in diminished tumor initiation, growth and metastasis, but also enhances the response to KRASG12D inhibition and PD-1 checkpoint blockade, respectively. Mechanistically, it is discovered that ADAM17 cleaves GSDMC, releasing nuclear fragments binding to promoter regions of stemness, metastasis, and immune evasion-related genes. Pharmacological inhibition of GSDMC cleavage or prevention of its nuclear translocation is equally effective in suppressing GSDMC's downstream targets and inhibiting PDAC progression. The findings establish GSDMC as a potential therapeutic target for enhancing treatment response in this deadly disease.

Efficient deep-blue fluorescent OLEDs based on a D‒π‒A emitter profiting from intramolecular hydrogen bonds and a hybridized excited state.

High performance deep-blue emitters with a Commission International de l'Eclairage (CIE) coordinate of CIEy ≤ 0.08 are highly desired in ultrahigh-definition displays. Herein, we designed and synthesized an efficient D‒π‒A deep-blue emitter, 2-(6-([1,1':3',1''-terphenyl]-5'-yl)pyridin-3-yl)-1-phenyl-1H-phenanthro[9,10-d] imidazole (mPTPH), using the synergistic effect of intramolecular hydrogen bond (H-bond) and hybridized excited state. Single-crystal structure analysis confirmed that there exist intra- and intermolecular H-bond interactions which could inhibit the structure vibration and increase photoluminescence efficiency. The photophysical and theoretical results show that mPTPH exhibited hybridized local and charge-transfer (HLCT) feature with strong deep-blue emission. Ultimately, the non-doped device based on mPTPH exhibited high maximum luminance of 20610 cd m-2. The doped device achieved high maximum external quantum efficiency of 5.4% and small efficiency roll-off with deep-blue emission peak of 413 nm and CIE coordinate of (0.16, 0.08).

Ultrafast Sintered Composite Cathode Incorporating a Negative Thermal Expansion Material for High-Performance Protonic Ceramic Fuel Cells.

Protonic ceramic fuel cells (PCFCs) offer a promising, clean, and efficient energy conversion solution. However, thermal mismatch between cathodes and electrolytes remains a critical obstacle, leading to interfacial damage such as cracking and delamination. Incorporating negative thermal expansion (NTE) materials into the cathode can mitigate this issue. The challenge lies in integrating NTE materials without compromising electrochemical performance or causing unwanted reactions during sintering. This study introduces a novel BaFe0.9Zr0.1O3-δ (BFZ)-NdMnO3-δ composite cathode fabricated using an ultrafast high-temperature sintering (UHS) process. This approach mitigates thermal expansion while boosting the cathode's catalytic activity compared to a single-phase BFZ cathode. The resulting fuel cell achieves a high peak power density of ∼550 mW cm-2 at 600 °C and demonstrates excellent stability during a 100 h test at 550 °C. These findings highlight the potential of UHS for developing high-performance, thermally compatible cathode materials that advance the field of PCFCs.

Virtual Inspection System for Pumping Stations with Multimodal Feedback.

Pumping stations have undergone significant modernization and digitalization in recent decades. However, traditional virtual inspections often prioritize the visual experience and fail to effectively represent the haptic physical properties of devices during inspections, resulting in poor immersion and interactivity. This paper presents a novel virtual inspection system for pumping stations, incorporating virtual reality interaction and haptic force feedback technology to enhance immersion and realism. The system leverages a 3D model, crafted in 3Ds Max, to provide immersive visualizations. Multimodal feedback is achieved through a combination of haptic force feedback provided by a haptic device and visual information delivered by a VR headset. The system's data platform integrates with external databases using Unity3D to display relevant information. The system provides immersive 3D visualizations and realistic force feedback during simulated inspections. We compared this system to a traditional virtual inspection method that demonstrated statistically significant improvements in task completion rates and a reduction in failure rates when using the multimodal feedback approach. This innovative approach holds the potential to enhance inspection safety, efficiency, and effectiveness in the pumping station industry.

Microwave ablation for lymph node metastasis in thyroid cancer: the impact of lymph node diameter.

Objectives: To explore the impact of lymph node diameter on the efficacy and safety of ultrasound-guided microwave ablation (MWA) in the treatment of cervical metastatic lymph nodes (CMLNs) from thyroid cancer. Methods: A total of 32 patients with 58 CMLNs from thyroid cancer underwent ultrasound-guided MWA and were included in the retrospective study. Patients were divided into three groups based on the mean largest diameter of the CMLNs: Group A (diameter ≤10mm), Group B (10mm < diameter ≤20mm), and Group C (diameter >20mm). The research involved comparing changes in cervical metastatic lymph nodes and serum thyroglobulin (sTg) levels, as well as the incidence of complications, before and after microwave ablation across three groups of patients. Results: The technical success rate of this study was 100% (32/32), and they showed no major complications. Compared with measurements taken before MWA, the mean largest diameter and volume of CMLNs, as well as the sTg level, showed significant reductions (p <0.05) at the last follow-up in all three patient groups. Group A and B exhibited higher lymph node volume reduction rates and complete disappearance rates compared to Group C. However, the recurrence rate in the three groups were in the following order: Group C > Group B > Group A. The occurrence rate of mild complications was Group A > Group C > Group B. Conclusion: MWA is a safe and effective method for treating CMLNs, with advantages for localized nodes but limitations for larger ones. Careful consideration and personalized plans are advised, based on comprehensive evidence assessment.

Comparison of clinical efficacy between autologous partially demineralized dentin matrix and deproteinized bovine bone mineral for bone augmentation in orthodontic patients with alveolar bone deficiency: a randomized controlled clinical trial.

BACKGROUND: It is common to see patients who need orthodontic treatment but with insufficient alveolar bone volume. However, safe and effective tooth movement requires sufficient alveolar bone width and height. The aim of this study is to compare the bone augmentation efficacy of Autologous Partially Demineralized Dentin Matrix (APDDM) and Deproteinized Bovine Bone Mineral (DBBM) in orthodontic patients with insufficient bone by using a randomized controlled clinical trial approach. MATERIALS AND METHODS: Twenty-seven orthodontic patients involving 40 posterior teeth alveolar sites (n = 40) with insufficient alveolar bone volume were randomly divided into a control group (n = 20) and an experimental group (n = 20). The patients in the experimental group were treated with APDDM, and those in the control group were treated with DBBM. After surgery, the adjacent teeth are moved toward the bone grafting sites according to the orthodontic treatment plan. Patients completed a postoperative response questionnaire by the Visual Analogue Scale (VAS) score to indicate pain and swelling in the bone grafted area at the time of suture removal; and CBCT scans were conducted before surgery, 6 months and 2 years after surgery to assess changes in buccal and central alveolar heights, as well as widths at the alveolar ridge apex and 3 mm, 5 mm below the apex, respectively. The CBCT image sequences were imported into Mimics 21.0 software in DICOM format. The data of the patients in both groups were collected and analyzed by SPSS 25.0. RESULTS: The VAS scores were significantly lower in the APDDM group than in the DBBM group (p < 0.05). Significant increases were observed in alveolar bone height and width at 6 months and 2 years postoperative (p < 0.05); At 2 years, the APDDM group exhibited a reduction in buccal crest height and in 3 mm, 5 mm width below alveolar ridge apex, relative to 6 months (p < 0.05), while the DBBM group showed a decrease only in the central height of the alveolar bone (p < 0.05). There was a significant bone augmentation increase found only 3 mm below the alveolar ridge apex in the APDDM group compared with the DBBM group among all 6 months group comparison (p < 0.05). At 2 years, the augmentation effects were similar across both groups (p > 0.05). CONCLUSION: Radiomics analysis indicates that APDDM serves as a viable bone augmentation material for orthodontic patients with insufficient alveolar bone volume, achieving comparable clinical efficacy to DBBM. Additionally, APDDM is associated with a milder postoperative response than DBBM. THE REGISTRATION NUMBER (TRN): ChiCTR2400084607.

"Find Your Y": histological differences in early stage (pT) and post-treatment (ypT) oesophageal adenocarcinoma with implications for salvage endoscopic resection.

AIMS: Current guidelines offer limited strategies for managing recurrent/persistent oesophageal adenocarcinoma (EAC). Salvage endoscopic mucosal/submucosal resection (ER) shows promise in oesophageal squamous cell carcinoma, however its success in EAC is limited. We aimed to elucidate histological characteristics influencing salvage ER success in patients with low-stage, pretreated EAC. METHODS: We retrospectively reviewed 272 EAC tumours postoesophagectomy from five US centres and collected clinicopathological data including discontinuous growth (DG), defined as separate tumour foci ≥2 mm from the main tumour. We selected 101 patients with low-stage disease and divided them into treatment-naïve (n=70) and neoadjuvant therapy (n=31) groups. We compared the two groups and differences in clinical, histological and outcome characteristics were identified. RESULTS: In the entire cohort (n=272), DGs were identified in 22% of cases. Multivariate analysis revealed DGs as an independent prognostic factor for recurrence and positive oesophagectomy margins. Lymphovascular invasion (LVI) and background intestinal metaplasia predicted DG presence and absence, respectively. Compared with the treatment-naïve low T-stage subgroup, the pretreated subgroup exhibited higher incidence of poorly differentiated carcinoma (16% vs 46%, p=0.007), larger tumours (14 vs 30 mm, p<0.001), higher tumour, node, metastases stage (7% vs 30%, p=0.004), more nodal disease (7% vs 36%, p<0.001) and frequent DGs (1% vs 13%, p=0.030). CONCLUSIONS: In treated low T-stage EACs, DGs may contribute to suboptimal outcomes following salvage ER. Presence of LVI (as a surrogate for DGs) and poor differentiation in the absence of intestinal metaplasia in biopsy samples may serve as histological poor prognosticators in treated patients with EAC being considered for salvage ER.

Trend Prediction and Operation Alarm Model Based on PCA-Based MTL and AM for the Operating Parameters of a Water Pumping Station.

In order to effectively predict the changing trend of operating parameters in the pump unit and carry out fault diagnosis and alarm processes, a trend prediction model is proposed in this paper based on PCA-based multi-task learning (MTL) and an attention mechanism (AM). The multi-task learning method based on PCA was used to process the operating data of the pump unit to make full use of the historical data to extract the key common features reflecting the operating state of the pump unit. The attention mechanism (AM) is introduced to dynamically allocate the weight coefficient of common feature mapping for highlighting the key common features and improving the prediction accuracy of the model when predicting the trend of data change for new working conditions. The model is tested with the actual operating data of a pumping station unit, and the calculation results of different models are compared and analyzed. The results show that the introduction of multi-task learning and attention mechanisms can improve the stability and accuracy of the trend prediction model compared with traditional single-task learning and static common feature mapping weights. According to the threshold analysis of the monitoring statistical parameters of the model, a multi-stage alarm model of pump unit operation condition monitoring can be established, which provides a theoretical basis for optimizing operation and maintenance management strategy in the process of pump station management.

Lignin-Based Carbon Fiber/Epoxy Resin Biocomposites with Excellent Fire Resistance and Mechanical Properties.

Carbon fiber (CF)-reinforced epoxy resin (EP) composites are lightweight materials with excellent comprehensive performance. However, the flammability of EP and the poor interfacial bonding between CF and EP are two key disadvantages that limit their further applications. Here, a kind of water-soluble lignin-based CF sizing agent (ELBEDK) is prepared through hydrophilic modification of enzymatic lignin, which can significantly enhance the interfacial interaction between CF and EP. Additionally, a highly efficient intumescent flame retardant (LMA) is prepared. The EP, enzymatic lignin, LMA and CF sized ELBEDK are compounded to obtain the fire-safety CF reinforced composites (SCF/FEP/L). The flame retardancy of SCF/FEP/L with 7% LMA (SCF/FEP7) reached V-0 rating. Moreover, SCF/FEP/L with 7% LMA and 15% lignin (SCF/FEP7/L15) present an limiting oxygen index (LOI)of 30.2% and V-0 of UL-94. Specifically, the total smoke production and the heat release rate are 47.8% and 46.81% lower than that of SCF/EP, respectively, indicating the improved smoke suppression and flame retardancy. The IFSS and flexural strength of SCF/FEP7/L15 are improved to be 59.4 MPa and 511.1 MPa, respectively. This study presents a simple approach to fabricate low-cost high performance lignin-based flame retardant CF/EP biocomposites with wide application potential.

Exceptional figure of merit achieved in boron-dispersed GeTe-based thermoelectric composites.

GeTe is a promising p-type material with increasingly enhanced thermoelectric properties reported in recent years, demonstrating its superiority for mid-temperature applications. In this work, the thermoelectric performance of GeTe is improved by a facile composite approach. We find that incorporating a small amount of boron particles into the Bi-doped GeTe leads to significant enhancement in power factor and simultaneous reduction in thermal conductivity, through which the synergistic modulation of electrical and thermal transport properties is realized. The thermal mismatch between the boron particles and the matrix induces high-density dislocations that effectively scatter the mid-frequency phonons, accounting for a minimum lattice thermal conductivity of 0.43 Wm-1K-1 at 613 K. Furthermore, the presence of boron/GeTe interfaces modifies the interfacial potential barriers, resulting in increased Seebeck coefficient and hence enhanced power factor (25.4 µWcm-1K-2 at 300 K). Consequently, we obtain a maximum figure of merit Zmax of 4.0 × 10-3 K-1 at 613 K in the GeTe-based composites, which is the record-high value in GeTe-based thermoelectric materials and also superior to most of thermoelectric systems for mid-temperature applications. This work provides an effective way to further enhance the performance of GeTe-based thermoelectrics.

Associations of proton pump inhibitors with susceptibility to influenza, pneumonia, and COVID-19: Evidence from a large population-based cohort study.

Background: Adverse effects of proton pump inhibitors (PPIs) have raised wide concerns. The association of PPIs with influenza is unexplored, while that with pneumonia or COVID-19 remains controversial. Our study aims to evaluate whether PPI use increases the risks of these respiratory infections. Methods: The current study included 160,923 eligible participants at baseline who completed questionnaires on medication use, which included PPI or histamine-2 receptor antagonist (H2RA), from the UK Biobank. Cox proportional hazards regression and propensity score-matching analyses were used to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs). Results: Comparisons with H2RA users were tested. PPI use was associated with increased risks of developing influenza (HR 1.32, 95% CI 1.12-1.56) and pneumonia (hazard ratio [HR] 1.42, 95% confidence interval [CI] 1.26-1.59). In contrast, the risk of COVID-19 infection was not significant with regular PPI use (HR 1.08, 95% CI 0.99-1.17), while the risks of severe COVID-19 (HR 1.19. 95% CI 1.11-1.27) and mortality (HR 1.37. 95% CI 1.29-1.46) were increased. However, when compared with H2RA users, PPI users were associated with a higher risk of influenza (HR 1.74, 95% CI 1.19-2.54), but the risks with pneumonia or COVID-19-related outcomes were not evident. Conclusions: PPI users are associated with increased risks of influenza, pneumonia, as well as COVID-19 severity and mortality compared to non-users, while the effects on pneumonia or COVID-19-related outcomes under PPI use were attenuated when compared to the use of H2RAs. Appropriate use of PPIs based on comprehensive evaluation is required. Funding: This work is supported by the National Natural Science Foundation of China (82171698, 82170561, 81300279, 81741067, 82100238), the Program for High-level Foreign Expert Introduction of China (G2022030047L), the Natural Science Foundation for Distinguished Young Scholars of Guangdong Province (2021B1515020003), the Guangdong Basic and Applied Basic Research Foundation (2022A1515012081), the Foreign Distinguished Teacher Program of Guangdong Science and Technology Department (KD0120220129), the Climbing Program of Introduced Talents and High-level Hospital Construction Project of Guangdong Provincial People's Hospital (DFJH201923, DFJH201803, KJ012019099, KJ012021143, KY012021183), and in part by VA Clinical Merit and ASGE clinical research funds (FWL).

Review of the Interfacial Structure and Properties of Surfactants in Petroleum Production and Geological Storage Systems from a Molecular Scale Perspective.

Surfactants play a crucial role in tertiary oil recovery by reducing the interfacial tension between immiscible phases, altering surface wettability, and improving foam film stability. Oil reservoirs have high temperatures and high pressures, making it difficult and hazardous to conduct lab experiments. In this context, molecular dynamics (MD) simulation is a valuable tool for complementing experiments. It can effectively study the microscopic behaviors (such as diffusion, adsorption, and aggregation) of the surfactant molecules in the pore fluids and predict the thermodynamics and kinetics of these systems with a high degree of accuracy. MD simulation also overcomes the limitations of traditional experiments, which often lack the necessary temporal-spatial resolution. Comparing simulated results with experimental data can provide a comprehensive explanation from a microscopic standpoint. This article reviews the state-of-the-art MD simulations of surfactant adsorption and resulting interfacial properties at gas/oil-water interfaces. Initially, the article discusses interfacial properties and methods for evaluating surfactant-formed monolayers, considering variations in interfacial concentration, molecular structure of the surfactants, and synergistic effect of surfactant mixtures. Then, it covers methods for characterizing microstructure at various interfaces and the evolution process of the monolayers' packing state as a function of interfacial concentration and the surfactants' molecular structure. Next, it examines the interactions between surfactants and the aqueous phase, focusing on headgroup solvation and counterion condensation. Finally, it analyzes the influence of hydrophobic phase molecular composition on interactions between surfactants and the hydrophobic phase. This review deepened our understanding of the micro-level mechanisms of oil displacement by surfactants and is beneficial for screening and designing surfactants for oil field applications.