The tribological properties of zinc borate ultrafine powder as a lubricant additive in sunflower oil

This paper presents an investigation on the tribological properties of zinc borate ultrafine powder employed as a lubricant additive in sunflower oil. The stable dispersions of 0.5 wt%, 1 wt% and 2 wt% zinc borate ultrafine powder in sunflower oil were achieved by using an ultrasonic homogeniser. Both a 4-ball tester and a pin-on-disc tester were employed to evaluate the anti-wear and friction reduction capabilities of zinc borate ultrafine powder. Tribo-films with dark colour were generated on the worn surfaces and showed a good contrast with the substrate. The worn surface with different morphologies reflected as the colour alterations on the worn surface were observed when different lubricants were applied. The morphology and elemental analysis of the worn surfaces were studied using atomic force microscopy (AFM) and scanning electronic microscopy (SEM). Mechanical properties of the tribo-films and substrates were studied with a nano-indentation tester. Test results suggest that tribo-films generated on the worn surface have a relatively low hardness compared with the steel substrate. The substrates on the worn surfaces lubricated in sunflower oil with the powder demonstrated higher hardness than that of the substrate lubricated with pure sunflower oil due to the possible tribo-chemical reaction between the zinc borate additive and substrate. The combination of sunflower oil with 0.5% zinc borate ultrafine powder has delivered the most balanced performance in friction and wear reduction. This study has demonstrated the possibility of application of this industrially applicable solid lubricant additive (zinc borate) with a decomposable vegetable based lubricant oil.Peer reviewedFinal Accepted Versio

Myosin Light Chain Kinase Mediates Intestinal Barrier Disruption following Burn Injury

Background: Severe burn injury results in the loss of intestinal barrier function, however, the underlying mechanism remains unclear. Myosin light chain (MLC) phosphorylation mediated by MLC kinase (MLCK) is critical to the pathophysiological regulation of intestinal barrier function. We hypothesized that the MLCK-dependent MLC phosphorylation mediates the regulation of intestinal barrier function following burn injury, and that MLCK inhibition attenuates the burn-induced intestinal barrier disfunction. Methodology/Principal Findings: Male balb/c mice were assigned randomly to either sham burn (control) or 30 % total body surface area (TBSA) full thickness burn without or with intraperitoneal injection of ML-9 (2 mg/kg), an MLCK inhibitor. In vivo intestinal permeability to fluorescein isothiocyanate (FITC)-dextran was measured. Intestinal mucosa injury was assessed histologically. Tight junction proteins ZO-1, occludin and claudin-1 was analyzed by immunofluorescent assay. Expression of MLCK and phosphorylated MLC in ileal mucosa was assessed by Western blot. Intestinal permeability was increased significantly after burn injury, which was accompanied by mucosa injury, tight junction protein alterations, and increase of both MLCK and MLC phosphorylation. Treatment with ML-9 attenuated the burn-caused increase of intestinal permeability, mucosa injury, tight junction protein alterations, and decreased MLC phosphorylation, but not MLCK expression

The Influence of Solid Additives on the Tribological Properties of Lubricants

The present work investigates the tribological properties of solid particles as lubricant additives in lubricants. Two types of solid particles, Ceria nanoparticles (CeO2) and Zinc borate ultrafine powders (ZB UFPs), were used as the lubricant additives in this study. The friction and wear behaviours of these lubricant additives in different base lubricants were identified. With an appropriate application of these solid lubricant additives, the friction reduction and wear resistance properties of the lubricant have been successfully improved. Without assistance of surfactant or surface modification, the two types of solid particles behave very differently. Evident performance was observed that pure ZB UFPs were capable of considerably reducing the friction coefficient of sunflower oil and liquid paraffin when they were used as a lubricant additive without further treatment. On the contrary, CeO2 nanoparticles did not show noticeable contribution to friction reduction when they were used as the only additive in water. Only when surfactant Sorbitan monostearate was employed to enhance the dispersibility of CeO2 nanoparticles in water, the application of this additive was capable of reducing friction coefficient of the water based lubricant effectively. Surface modification of the solid particles was carried out to improve the dispersibility of these particles in base lubricants. Oleic acid (OA) and Hexadecyltrimethoxysilane (HDTMOS) were selected as the modification agents. Modified CeO2 nanoparticles and ZB UFPs revealed outstanding wear resistance property. An improvement of up to 15 times was identified although this improvement on wear resistance, in this case, was often companied by a rise in friction coefficient. Tribo-films generated by tribo-chemical reaction were observed on most of the worn surfaces and the formation of this tribo-film appeared to have played an important role in the friction and wear behaviours of a system. A tenacious tribo-film with good surface coverage was only generated on the worn surface when HDTMOS modified solid particles were used as lubricant additives. The mechanical properties and elemental composition of the tribo-film were studied with nano-indentation and energy-dispersive X-ray spectroscopy (EDS). Finally, based on the experimental evidence, different functionalities of CeO2 nanoparticles and ZB UFPs as solid lubricant additives were recognized

A Study of Tribological Properties of Water-Based Ceria Nanofluids

This is an Accepted Manuscript of an article published by Taylor & Francis in Tribology Transactions on 10 January 2013, available online: http://www.tandfonline.com/doi/abs/10.1080/10402004.2012.748948.This paper presents an investigation on the potential tribological properties of the water-based cerium dioxide nanofluids. The nanofluids with different nanoparticle concentrations were prepared in a materials laboratory. A stable dispersion of nanoparticles in the fluids was achieved with an appropriate percentage of surfactant sorbitan monostearate. The stability of particle dispersion was studied using a Zeta-potential measuring device. Additive conglomerate size in the nanofluids was measured using Dynamic Light Scattering (DLS) device. It has been observed that the dispersibility of nanoparticles played an important role in the frictional properties of the nanofluids. The tribological properties of the water-based nanofluids were evaluated using a Pin-on-disc tester under different loading conditions. A significant improvement on tribological properties of the water-based cerium dioxide nanofluids was observed. The worn surfaces of the contact elements were characterised using SEM and a Nano-tester. According to the test results, the significant reductions of the friction coefficient and the anti-wear property of water-based cerium dioxide nanofluids are attributed to the deposition of nanoparticles on worn contact surfaces.Peer reviewe

The preparation and tribological properties of surface modified zinc borate ultrafine powder as a lubricant additive in liquid paraffin

This paper investigates the effects of surface modification of zinc borate ultrafine powders (ZB UFPs) on their tribological properties as lubricant additives in liquid paraffin (LP). ZB UFPs were successfully modified by hexadecyltrimethoxysilane (HDTMOS) and oleic acid (OA). It is evident that HDTMOS modified zinc borate ultrafine powder (HDTMOS-ZB UFP) delivered a small conglomerate size, good stability in the organic solvent and sound anti-wear property. It has been observed that a continuous and tenacious tribo-film on the worn surface generated from HDTMOS modified ZB UFP as a lubricant additive in LP plays an important role in the outstanding anti-wear property. It is suggested that HDTMOS modified ZB UFP as a lubricant additive in LP has a great potential.Peer reviewedSubmitted Versio

SINGLE MACHINE SCHEDULING WITH A LEARNING EFFECT AND A RATE-MODIFYING ACTIVITY

In the paper, single machine scheduling problems with a learning effect and a rate-modifying activity are considered. Under the learning effect, the processing time of a job is a decreasing function of its position in the sequence. The rate-modifying activity is an event that can change the speed of the machine, and hence the processing time of jobs after the activity. The following objective functions are considered: the makespan, the total earliness, tardiness and completion time penalty, and the total earliness, tardiness, due-window starting time and due-window size penalty. Polynomial time algorithms are proposed to optimally solve the problems.Scheduling, single machine, learning effect, rate-modifying activity

Frictional properties of cerium oxide nanoparticles in water

Non peer reviewe

Integration of Vis–NIR Spectroscopy and Machine Learning Techniques to Predict Eight Soil Parameters in Alpine Regions

Visible and near-infrared spectroscopy (Vis–NIR, 350–1100 nm) has great potential for predicting soil properties. However, current research on the hyperspectral prediction of soil parameters in agricultural areas of alpine regions and the types of parameters included is limited, and optimal spectral treatments and predictive models applicable to different parameters have not been sufficiently investigated. Therefore, we evaluated the accuracy of predicting total nitrogen (TN), phosphorus pentoxide (TP2O5), total potassium oxide (TK2O), alkali-hydrolyzable nitrogen (AHN), effective phosphorus (AP), effective potassium (AK), soil organic matter (SOM), and pH in the Qinghai–Tibet Plateau using the Vis–NIR technique in combination with spectral transformations, correlation analysis, feature selection, and machine learning. The results show that spectral transformations improve the correlation between spectra and parameters but are dependent on the parameter type and the method used. Continuum removal (CR), logarithmic first-order differential (FDL), and inverse first-order differential (FDR) had the most significant effects. The feature bands were extracted using the SPA and modeled using partial least squares (PLSR), random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost), and backpropagation neural networks (BPNNs). The accuracy was evaluated based on R2, RMSE, RPD, and RPIQ. We found that the PLSR model only enables the prediction of SOM and pH with lower accuracy than the remaining models. XGBoost can predict all of the parameters but only for AHN; the prediction performance is better than other methods (R2 = 0.776, RMSE = 0.043 g/kg, and RPIQ = 2.88). The RF, SVM, and BPNN models cannot predict AK, AP, and AHN, respectively. In addition, TP2O5, AP, and pH are best suited for modeling using RF (RPIQ = 2.776, 3.011, and 3.198); TN, AK, and SOM are best suited for modeling using BPNN (RPIQ = 2.851, 2.394, and 3.085); and AHN and TK2O are best suited for XGBoost and SVM, respectively (RPIQ = 2.880 and 3.217). Therefore, this study can provide technical and data support for the accurate and efficient acquisition of soil parameters in alpine agriculture

Vis–NIR Spectroscopy Combined with GAN Data Augmentation for Predicting Soil Nutrients in Degraded Alpine Meadows on the Qinghai–Tibet Plateau

Soil nutrients play vital roles in vegetation growth and are a key indicator of land degradation. Accurate, rapid, and non-destructive measurement of the soil nutrient content is important for ecological conservation, degradation monitoring, and precision farming. Currently, visible and near-infrared (Vis–NIR) spectroscopy allows for rapid and non-destructive monitoring of soil nutrients. However, the performance of Vis–NIR inversion models is extremely dependent on the number of samples. Limited samples may lead to low prediction accuracy of the models. Therefore, modeling and prediction based on a small sample size remain a challenge. This study proposes a method for the simultaneous augmentation of soil spectral and nutrient data (total nitrogen (TN), soil organic matter (SOM), total potassium oxide (TK2O), and total phosphorus pentoxide (TP2O5)) using a generative adversarial network (GAN). The sample augmentation range and the level of accuracy improvement were also analyzed. First, 42 soil samples were collected from the pika disturbance area on the QTP. The collected soils were measured in the laboratory for Vis–NIR and TN, SOM, TK2O, and TP2O5 data. A GAN was then used to augment the soil spectral and nutrient data simultaneously. Finally, the effect of adding different numbers of generative samples to the training set on the predictive performance of a convolutional neural network (CNN) was analyzed and compared with another data augmentation method (extended multiplicative signal augmentation, EMSA). The results showed that a GAN can generate data very similar to real data and with better diversity. A total of 15, 30, 60, 120, and 240 generative samples (GAN and EMSA) were randomly selected from 300 generative samples to be included in the real data to train the CNN model. The model performance first improved and then deteriorated, and the GAN was more effective than EMSA. Further shortening the interval for adding GAN data revealed that the optimal ranges were 30–40, 50–60, 30–35, and 25–35 for TK2O, TN, TP2O5, and SOM, respectively, and the validation set accuracy was maximized in these ranges. Therefore, the above method can compensate to some extent for insufficient samples in the hyperspectral prediction of soil nutrients, and can quickly and accurately estimate the content of soil TK2O, TN, TP2O5, and SOM