Exposure Assessment Based Recommendations to Improve Nanosafety at Nanoliposome Production Sites

Bressot, Christophe; Shandilya, Neeraj; Nogueira, E.; Cavaco-Paulo, Artur; Morgeneyer, Martin; Bihan, Olivier Le; Aguerre-Chariol, Olivier

doi:10.1155/2015/931405

Cited by 17 publications

(13 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Replace steps 2-3 until convergence is achieved. 103,104 Effective parameters are calculated using the expressions (20) and (22). The parameter g is the effective parameter; m is the number of parameters; A is the Hessian matrix of the objective function S(w).…”

Section: ∂Ementioning

confidence: 99%

Application of neural networks in predictions of brake wear particulate matter emission

Vasiljević

Glišović

Stojanović

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part D:

View full text Add to dashboard Cite

According to the World Health Organization, air pollution with PM10 and PM2.5 (PM-particulate matter) is a significant problem that can have serious consequences for human health. Vehicles, as one of the main sources of PM10 and PM2.5 emissions, pollute the air and the environment both by creating particles by burning fuel in the engine, and by wearing of various elements in some vehicle systems. In this paper, the authors conducted the prediction of the formation of PM10 and PM2.5 particles generated by the wear of the braking system using a neural network (Artificial Neural Networks (ANN)). In this case, the neural network model was created based on the generated particles that were measured experimentally, while the validity of the created neural network was checked by means of a comparative analysis of the experimentally measured amount of particles and the prediction results. The experimental results were obtained by testing on an inertial braking dynamometer, where braking was performed in several modes, that is under different braking parameters (simulated vehicle speed, brake system pressure, temperature, braking time, braking torque). During braking, the concentration of PM10 and PM2.5 particles was measured simultaneously. The total of 196 measurements were performed and these data were used for training, validation, and verification of the neural network. When it comes to simulation, a comparison of two types of neural networks was performed with one output and with two outputs. For each type, network training was conducted using three different algorithms of backpropagation methods. For each neural network, a comparison of the obtained experimental and simulation results was performed. More accurate prediction results were obtained by the single-output neural network for both particulate sizes, while the smallest error was found in the case of a trained neural network using the Levenberg-Marquardt backward propagation algorithm. The aim of creating such a prediction model is to prove that by using neural networks it is possible to predict the emission of particles generated by brake wear, which can be further used for modern traffic systems such as traffic control. In addition, this wear algorithm could be applied on other vehicle systems, such as a clutch or tires.

show abstract

Section: ∂Ementioning

confidence: 99%

Application of neural networks in predictions of brake wear particulate matter emission

Vasiljević

Glišović

Stojanović

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part D:

View full text Add to dashboard Cite

show abstract

“…However, the low oxidation resistance makes TiN coatings fail because of the formation of brittle TiO 2 when the temperature is high [ 5 , 6 ]. The existence of TiO 2 showcases the potential risks of nanoparticle releases by coating or liquid dispersion and deteriorates environments and human health [ 7 , 8 , 9 , 10 , 11 ]. It is significant to inhibit the formation of TiO 2 during the oxidation process to avoid nanoparticle release [ 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Tribological Behaviors of Super-Hard TiAlN Coatings Deposited by Filtered Cathode Vacuum Arc Deposition

Zhang

Yuan

et al. 2022

Materials

View full text Add to dashboard Cite

High hardness improves the material’s load-bearing capacity, resulting in the enhancement of tribological properties. However, the high hardness is difficult to achieve for TiAlN coating due to the transformation of the close-packed structure from cubic to hexagonal and the increase in the grain size when the Al content is high. In the present study, the ultrahard TiAlN coatings (hardness > 40 GPa) are successfully developed by filtered cathodic vacuum arc technology to study the effect of nitrogen flux rate on tribological behaviors. The highest hardness of 46.39 GPa is obtained by tuning the nitrogen flux rate to achieve the regulation of Al content and the formation of nanocrystalline. The stable fcc TiAlN phase is formed via the solid-phase reaction under a high nitrogen concentration, and more aluminum atoms replace the titanium atoms in the (Ti, Al)N solid solution. The high Al content of the Ti0.35Al0.65N coating has a nanocrystalline structure and the average crystalline size is 16.52 nm. The TiAlN coating deposited at a nitrogen flux rate of 60 sccm exhibits the best properties of a combination of microhardness = 2972.91 Hv0.5, H = 46.39 GPa, E = 499.4 Gpa, ratio H/E* = 0.093 and ratio H3/E*2 = 0.403. Meanwhile, the TiAlN coating deposited at 60 sccm shows the lowest average friction coefficient of 0.43 and wear rate of 1.3 × 10−7 mm3 N−1 m−1 due to the best mechanical properties.

show abstract

“…This technique has turned out to be portable, low cost, and easier to use (Bressot et al 2015;Bressot et al 2018a) than a sampling system based on electrostatic precipitation or thermophoretic precipitation, which are more cumbersome and require energy-consuming tools to prior charge particles or to create a thermal gradient (Bau et al 2010;Li, Liu and Zhu 2010). It can be employed in the field of occupational hygiene and consumer exposure assessment since the TEM grid is directly ready for TEM analysis once sampled (R'mili et al 2013) and just a few minutes of sampling is required.…”

Section: Introductionmentioning

confidence: 99%

Airborne nanoparticle collection efficiency of a TEM grid-equipped sampling system

Xiang

Morgeneyer

Aguerre-Chariol

et al. 2021

Aerosol Science and Technology

Self Cite

View full text Add to dashboard Cite

The recently developed aerosol sampler called mini particle sampler (MPS), which is equipped with a porous Transmission electron microscopy (TEM) grid, renders nanoparticle sampling convenient. The present study aims to improve the nanoparticle sampling efficiency and to optimize the sampling technology. The sensitivity of the parameters in the whole set-up is estimated by the Taguchi method. The effects of the main parameters on the collection efficiency are 2 compared between experiments and theories. The sampling efficiencies are determined for particles with mobility diameters ranging from 5 to 100 nm. The results show that parameters: salt concentration of the atomizer, high-voltage polarity in the differential mobility analyzer (DMA), sampling efficiency assessment method, sampling temperature, and porosity of the porous TEM grid minimally affect the collection efficiency. Small filter pore size and high flowrate promote particle capturing, but may aggravate the burden on the TEM grid. Denser particles increase the deposition possibility due to impaction, and thereby increasing the overall collection efficiency. The minimum collection efficiency is up to 40% by adjusting the parameter settings of the sampling system. The results are of immediate importance for assessing nano-exposure using the MPS sampling system.

show abstract

Exposure Assessment Based Recommendations to Improve Nanosafety at Nanoliposome Production Sites

Cited by 17 publications

References 31 publications

Application of neural networks in predictions of brake wear particulate matter emission

Application of neural networks in predictions of brake wear particulate matter emission

Tribological Behaviors of Super-Hard TiAlN Coatings Deposited by Filtered Cathode Vacuum Arc Deposition

Airborne nanoparticle collection efficiency of a TEM grid-equipped sampling system

Contact Info

Product

Resources

About