A Feasibility Study on Enhanced Oil Recovery of Modified Janus Nano Calcium Carbonate-Assisted Alkyl Polyglycoside to Form Nanofluids in Emulsification Flooding.

Emulsification flooding can effectively enhance crude oil recovery to solve the problem of petroleum shortage. In this work, a modified Janus Nano Calcium carbonate (JNC-12) with a particle size of 30-150 nm was synthesized, and an in situ emulsification nanofluid (ISEN) was prepared with JNC-12 and alkyl polyglycoside (APG). Scanning electron microscope (SEM) showed that the dispersion of JNC-12 in air or APG solution was better than Nano Calcium carbonate (Nano CaCO3). The emulsification properties, interfacial tension, and expansion modulus of ISEN were studied, and the result showed that with the increase in salinity, the emulsification rate decreased, the water yield rate increased, the interfacial tension first decreased and then increased, and the expansion modulus first increased and then decreased. With the increase in temperature, the emulsification rate, emulsion viscosity, and interfacial tension decreased. With the increased oil-water volume, the water yield rate and the emulsion viscosity increased. With increase in the concentration of JNC-12, the water yield rate, the emulsion viscosity, and the interfacial tension decreased but the expansion modulus increased. The emulsion generated by emulsifying ISEN with crude oil was an O/W emulsion, the crude oil viscosity was 4-10 times that of emulsion, and the average particle size of emulsion was 1.107 µm. The addition of ISEN caused the decrease in interfacial tension of oil-water to 0.01-0.1 mN/m. The wettability alteration experiment found that ISEN could change the lipophilic rock to hydrophilic rock. Finally, the core displacement experiments showed that compared with the first water flooding, the oil recovery of the second water flooding after ISEN flooding enhanced by 17.6%. This research has important guiding significance for in situ emulsified nanofluid flooding to enhance oil recovery.

Surface hydrolysis-designed AuNPs-zwitterionic-glucose as a novel tool for targeting macrophage visualization and delivery into infarcted hearts.

Macrophages, innate immune cells, are key players in the maintenance of myocardial homeostasis under normal conditions and tissue repair after injury. The infiltration of macrophages into the injured heart makes them a potentially appealing vehicle for noninvasive imaging and targeted drug delivery of myocardial infarction (MI). In this study, we demonstrated the use of surface hydrolysis-designed AuNPs-zwitterionic-glucose to label macrophages and track their infiltration into isoproterenol hydrochloride (ISO)-induced MI sites noninvasively using CT. The AuNPs-zwitterionic-glucose did not affect the viability or cytokine release of macrophages and were highly taken up by these cells. The in vivo CT images were obtained on Day 4, Day 6, Day 7, and Day 9, and the attenuation was seen to increase in the heart over time compared to the Day 4 scan. In vitro analysis also confirmed the presence of macrophages around injured cardiomyocytes. Additionally, we also addressed the concern of cell tracking or merely AuNP tracking, which is the inherent problem for any form of nanoparticle-labeled cell tracking by using zwitterionic and glucose-functionalized AuNPs. The glucose coated on the surface of AuNPs-zwit-glucose will be hydrolyzed in macrophages, forming only zwitterionic protected AuNPs that cannot be taken up again by endogenous cells in vivo. This will greatly improve the accuracy and precision of imaging and target delivery. We believe this is the first study to noninvasively visualize the infiltration of macrophages into MI hearts using CT, which could be used for imaging and evaluating the possibility of macrophage-mediated delivery in infarcted hearts.

Predicting simultaneously fields of soot temperature and volume fraction in laminar sooting flames from soot radiation measurements - a convolutional neural networks approach.

An original convolutional neural network, i.e. U-net approach, has been designed to retrieve simultaneously local soot temperature and volume fraction fields from line-of-sight measurements of soot radiation fields. A five-stage U-net architecture is established and detailed. Based on a set of N2 diluted ethylene non-premixed flames, the minimum batch size requirement for U-net model training is discussed and the U-net model prediction ability is validated for the first time by fields provided by the modulated absorption emission (MAE) technique documenting the N2 diluted flame. Additionally, the U-net model's flexibility and robustness to noise are also quantitatively studied by introducing 5% & 10% Gaussian random noises into training together with the testing data. Eventually, the U-net predictive results are directly contrasted with those of Bayesian optimized back propagation neural network (BPNN) in terms of testing score, prediction absolute error (AE), soot parameter field smoothness, and time cost.

Simultaneous soot multi-parameter fields predictions in laminar sooting flames from neural network-based flame luminosity measurement I: methodology.

We originally report the use of a neural network-based method for diagnosing multiple key parameters in axis-symmetric laminar sooting flames. A Bayesian optimized back propagation neural network (BPNN) is developed and applied to flame luminosity to predict the planar distribution of soot volume fraction, temperature, and primary particle diameter. The feasibility and robustness of this approach are firstly assessed using numerical modeling results and then further validated with experimental results of a series of laminar diffusion sooting flames. This proposed BPNN model-based flame luminosity approach shows high prediction accuracies, typically up to 114 K, 0.25 ppm, and 2.56 nm for soot temperature, volume fraction, and primary particle diameter, respectively. We believe that the present machine learning-assisted optical diagnostics paves a more efficient, lower costing, and high-fidelity way for multi-parameters simultaneous diagnosis in combustion and reacting flows.

Machine learning-assisted soot temperature and volume fraction fields predictions in the ethylene laminar diffusion flames.

Inferring local soot temperature and volume fraction distributions from radiation emission measurements of sooting flames may involve solving nonlinear, ill-posed and high-dimensional problems, which are typically conducted by solving ill-posed problems with big matrices with regularization methods. Due to the high data throughput, they are usually inefficient and tedious. Machine learning approaches allow solving such problems, offering an alternative way to deal with complex and dynamic systems with good flexibility. In this study, we present an original and efficient machine learning approach for retrieving soot temperature and volume fraction fields simultaneously from single-color near-infrared emission measurements of dilute ethylene diffusion flames. The machine learning model gathers information from existing data and builds connections between combustion scalars (soot temperature and volume fraction) and emission measurements of flames. Numerical studies were conducted first to show the feasibility and robustness of the method. The experimental Multi-Layer Perceptron (MLP) neural network model was fostered and validated by the N2 diluted ethylene diffusion flames. Furthermore, the model capability tests were carried out as well for CO2 diluted ethylene diffusion flames. Eventually, the model performance subjected to the Modulated Absorption/Emission (MAE) technique measurement uncertainties were detailed.

Urinary bladder stone due to retained indwelling ureteral stent: A case report.

RATIONALE: The indwelling ureteral stents is a common procedure in routine urological practice. The double-J (D-J) stent is the most common type of stents used and is indicated mainly for short-term urinary drainage and prevention of obstruction and infection. However, prolonged indwelling stents may result in disastrous complications, such as hematuria, infection, encrustation, and stone formation. In this context, the persistence of stent in situ might play a key role as a nidus in deposition of urinary sediment, then forming calculus. Although the encrustation may become more serious as time goes on, large bladder stones are relatively rare. However, the serious encrustation and giant stone may complicate or exacerbate the conditions in turn. PATIENT CONCERNS: A 45-year-old female patient who underwent right ureteral stent placement after open ureterolithotomy 6 years ago complained of dysuria, urinary frequency, and urgency over 2 months. DIAGNOSIS: The kidney ureter bladder (KUB) x-ray showed the presence of a giant stone in the bladder and an entire D-J stent. The computed tomography (CT) urography scans revealed normal left kidney, right hydronephrosis, and an encrusted D-J stent with the significant stone, diameter 4.2 cm with a CT value of 1211.0 ±â€Š221.6 HU, on the vesical coil. On the basis of these auxiliary examinations, the case was diagnosed as cystolith and prolonged-indwelling stents. INTERVENTIONS: Pneumatic ballistic lithotripsy was used for crushing the bladder calculi followed by the successful extraction of intact D-J ureteral stent. OUTCOMES: No residual stone was detected on postoperative KUB x-ray and CT urography scans. Patient recovered well and was discharged 10 days after surgery. Semi-annual ultrasound examination was suggested to monitor the effect of therapy. LESSONS: This case reminds us that it is crucial to take various measures to avoid the forgotten ureteral stent and its unfortunate late complication.