ABSRACT:The use of nanoparticles (NP) to improve reservoir characterisation or to enhance oil recovery (EOR) has recently received intensive interest; however there are still many un-resolved questions. This work reports a systematic study of the effect of rutile TiO 2 nanoparticle-assisted brine flooding. Rutile ellipsoid TiO 2 nanoparticles were synthesised and stabilised by tri-sodium citrate dehydrate for brine flooding of water-wet Berea sandstone cores. Careful characterisation of the rock samples and nanomaterials before and after the flooding was conducted, and the relative contributions to the modified flooding results from the stabiliser and the nanoparticles of different concentrations were examined. The oil recovery performance was evaluated both at break-through (BT) point and at the end of flooding (~3.2 pore volumes). Nanoparticle migration behavior was also investigated in order to understand the potential mechanisms for oil recovery. The results showed that both nanoparticle transport rate and EOR effect were strongly dependent on the particle concentration. The oil recovery efficiency at the BT point was found to increase at low nanoparticle concentrations but decrease at higher values. A maximum 33% increase of the recovery factor was observed at the BT point for a TiO 2 concentration of 20 ppm, but higher nanoparticle concentrations usually had higher ultimate recovery factors. The presence of oil phase was found to accelerate the particle migration though the core. The discussion of various mechanisms suggested that the improvement in the mobility ratio, possible wettability change and log-jamming effect were responsible for the observed phenomena.

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Volumetric solar absorption using nanofluids can minimize the thermal loss by trapping the light inside the fluid volume. A strong surface boiling with the underneath fluid still subcooled could have many interesting applications, whose mechanism is however still under strong debate. This work advanced our understanding on volumetric fluid heating by performing a novel experiment under a unique uniform solar heating setup at 280 Suns, with a particular focus on the steam production phenomenon using gold nanofluids. To take the temperature distribution into account, a new integration method was used to calculate the sensible heating contribution. The results showed that the photothermal conversion efficiency was enhanced significantly by gold nanofluids. A three-stage heating scenario was identified and during the first stage most of the energy was absorbed by the surface fluid, resulting in rapid vapor generation with the underneath fluid still subcooled. The condensed vapor analysis showed no nanoparticle escaping even under vigorous boiling conditions. Such results reveal that nanoparticle enabled volumetric solar heating could have many promising applications including clean water production in arid areas where abundant solar energy is available. highlights  Novel experiment was performed for nanofluids at a focused solar flux of 280 Suns. Strong surface evaporation was enabled while the bulk fluid was still subcooled  A new integration method was used to calculate photothermal conversion efficiency  Gold nanofluid (0.04w%) increased photothermal conversion efficiency by 95%. The authors are grateful for all the constructive comments from the reviewer and the Editor. Most of the comments were concerned on the presentation of the work. We have addressed all these concerns in the revised version, and a point-by-point reply is supplied below Reviewer #1:The authors of the present work experimentally investigated the surface boiling and steam production mechanism of gold nanofluids under uniform solar heating of 280 Suns. Various concentrations of gold nanofluids were produced and the generated steam was condensed and tested to reveal the presence of any nanoparticles.The study provides good insight to the surface boiling phenomenon of nanofluids and is in consonant with recent trend of investigation. However, there are several problems that need to be addressed before considering for publication in Applied Energy.1. The Abstract, in its current state is incomplete. It is more like a conclusion and needs to be re-written.Action: the abstract was rewritten with more focus on the novelty 2. The use of "gold nanofluids" should be mentioned in the Title and Abstract.Action: The title was slightly changed to reflect the content, and the was used in the title and the abstract in the revised version.3. In the statement: "For example, researchers [43] from Rice University", the Institution name should be replaced by the Authors' name.Action: T was used to replace the institution name in the revised version....

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Nanoparticle morphology is 8 expected to play a significant role in the 9 stability, aggregation behaviour and 10 ultimate fate of engineered nanomaterials 11 in natural aquatic environments. The 12 aggregation kinetics of ellipsoidal and 13 spherical titanium dioxide (TiO 2 ) 14 nanoparticles (NP) under different 15 surfactant loadings, pH values and ionic strengths are investigated in this study. The stability results 16 reveal that alteration of surface charge is the stability determining factor. Among five different 17 surfactants investigated, sodium citrate and Suwannee river fulvic acid (SRFA) were the most 18 effective stabilizers. It was observed that both types of NP were more stable in monovalent salts 19 (NaCl and NaNO 3 ) as compared with divalent salts (Ca(NO 3 ) 2 and CaCl 2 ). The aggregation of 20 spherical TiO 2 NP demonstrated a strong dependency on the ionic strength regardless of the presence 21 of mono or divalent salts; while the ellipsoids exhibited a lower dependency on the ionic strength but 22 is more stable . This work acts as a benchmark study towards understanding the fate of stabilized NP 23 in natural environments that are rich in Ca(CO 3 ) 2 , NaNO 3 , NaCl and CaCl 2 along with natural organic 24 matters. 25

A method of solution impregnation and calcination has been demonstrated for synthesizing nanoparticles of Ag-TiO 2 composite photocatalysts for use in the disinfection of water. Only a small proportion of the TiO 2 surface is covered by nano-islands of Ag corresponding to a loading of 4 wt.% of Ag; thus, most of the TiO 2 surface is available for photocatalytic function. Although the primary particles of both Ag and TiO 2 are in the 10-to 20-nm range, microscopic studies indicate that the primary particles of Ag are deposited on nano-agglomerates of 30-to 70-nmsized TiO 2 . It is seen that the relatively small loading of Ag has not caused any UV-vis spectral shift but has enhanced the rate of photocatalytic antibacterial action of TiO 2 , presumably by electron trapping.

The understanding of near-wall motion, evaporation behavior and dry pattern of sessile nanofluid droplets is fundamental to a wide range of applications such as painting, spray drying, thin film coating, fuel injection and inkjet printing. However, a deep insight into the heat transfer, fluid flow, near-wall particle velocity and their effects on the resulting dry patterns is still much needed to take the full advantage of these nano-sized particles in the droplet. This work investigates the effect of direct absorptive silicon/silver (Si/Ag) hybrid nanofluids via two experiments. The first experiment identifies the motion of tracer particles near the triple line of a sessile nanofluid droplet on a super-hydrophilic substrate under ambient conditions by the multilayer nanoparticle image velocimetry (MnPIV) technique. The second experiment reveals the effect of light-sensitive Si/Ag composite nanoparticles on the droplet evaporation rate and subsequent drying patterns under different radiation intensities. The results show that the presence of nanoparticle in a very small proportion significantly affects the motion of tracer particles, leading to different drying patterns and evaporation rates, which can be very important for the applications such as spray coating and inkjet printing.

Breast cancer (BC) infection, which is peculiar to women, brings about the high rate of deaths among women in every part of the world. The early investigation of BC has minimized the severe effects of cancer as compared to the last stage diagnosis. Doctors for diagnostic tests usually suggest the medical imaging modalities like mammograms or biopsy histopathology (Hp) images. However, Hp image analysis gives doctors more confidence to diagnose BC as compared to mammograms. Many studies used Hp images to develop BC classification models to assist doctors in early BC diagnosis. However, these models lack better and reliable results in terms of reporting multiple performance evaluation metrics. Therefore, the goal of this study is to create a reliable, more accurate model that consumes minimum resources by using transfer learning based convolution neural network model. The proposed model uses the trained model after fine tuning, hence requires less number of images and can show better results on minimum resources. BreakHis dataset, which is available publicly has been employed in overall experiments in this research. BreakHis dataset is separated into training, testing, and validation for the experimentation. In addition, the dataset for training was augmented followed by stain normalization. By using the concept of transfer learning (TL), AleNext was retained after fine-tuning the last layer for binary classification like benign and malignant. Afterward, preprocessed images are fed into the TL based model for training. The model training was performed many times by changing the hyper-parameters randomly until the minimum validation loss was achieved. Now the trained model was used for feature extraction. The extracted features were further evaluated by using six ML classifiers (i.e. softmax, Decision tree, Naïve Bayes, Linear discriminant analysis, Support vector machine, k-nearest neighbor) through five performance measures such as precision, F-measure, accuracy, specificity, and sensitivity for experimental evaluation. The softmax has outperformed among all classifiers. Furthermore, to reduce the wrong prediction, a misclassification reducing (MR) algorithm was developed. After using the MR algorithm the proposed model produced better and reliable results. The observed accuracy, specificity, sensitivity, precision and F measure are 81.25%, 77.47%, 82.49%, 91.70%, and 86.80% respectively. These results show that the proposed TL based model along with misclassification reduction algorithm produced comparable results to the current baseline models. Hence, the expected model could serve as a second opinion for BC classification in any healthcare center.