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Hybrid Materials Based on Carbon Nanotubes and Nanofibers for Environmental Applications.

ABSTRACT: With the advances in material science, hybrid nanomaterials with unique mechanical, electrical, thermal and optical characteristics have been developed. Among them, hybrids based on filamentous forms of carbon, such as carbon nanotubes and carbon nanofibers, in combination with inorganic nanoparticles attract particular attention. Due to the structure and morphology, charge and energy transfer processes lead to synergistic effects that allow the use of less material with higher productivity. To clarify these issues, this review will summarize and discuss the relevant studies of the use of inorganic compounds of various chemical groups in modifying carbon nanomaterials for ecological applications.

SUBMITTER: Navrotskaya AG

PROVIDER: S-EPMC7338791 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Hybrid Materials Based on Carbon Nanotubes and Nanofibers for Environmental Applications.

Navrotskaya Anastasiya G AG Aleksandrova Darya D DD Krivoshapkina Elena F EF Sillanpää Mika M Krivoshapkin Pavel V PV

Frontiers in chemistry 20200630

With the advances in material science, hybrid nanomaterials with unique mechanical, electrical, thermal and optical characteristics have been developed. Among them, hybrids based on filamentous forms of carbon, such as carbon nanotubes and carbon nanofibers, in combination with inorganic nanoparticles attract particular attention. Due to the structure and morphology, charge and energy transfer processes lead to synergistic effects that allow the use of less material with higher productivity. To ...[more]

PMID: 32695748

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Project description:BACKGROUND:Recent cross-sectional epidemiologic studies have examined the association between human health effects and carbon nanotube and nanofiber (CNT/F) workplace exposures. However, due to the latency of many health effects of interest, cohort studies with sufficient follow-up will likely be needed. The objective of this study was to identify workplace determinants that contribute to exposure and develop predictive models to estimate CNT/F exposures for future use in epidemiologic studies. METHODS:Exposure measurements were compiled from 15 unique facilities for the metrics of elemental carbon (EC) mass at both the respirable and inhalable aerosol size fractions as well as a quantitative analysis performed by transmission electron microscopy (TEM). These metrics served as the dependent variables in model development. Repeated personal samples were collected from most of the 127 CNT/F worker participants for 252 total observations. Determinants were categorized as company-level or worker-level and used to describe the exposure relationship within the dependent variables. The influence of determinants on variance components was explored using mixed linear models that utilized a backwards stepwise selection process with a lowering of the AIC for model determinant selection. Additional ridge regression models were created that examined predictive performance with and without all two-way interactions. Cross-validation was performed on each model to evaluate the generalizability of its predictive capabilities while predictive performance was evaluated according to the corresponding R2 value and root mean square error (RMSE). RESULTS:Determinants at the company-level that increased exposure included an inadequate or semi-adequate engineering control rating, increasing average CNT/F diameter/length, daily quantities of material handled from 101 g to >1 kg and >1 kg, the use of CNF materials, the industry type of hybrid producer/user, and the expert assessment of a high exposure potential. Worker-level determinants associated with higher exposure included handling the dry-powdered form of CNT/F, handling daily quantities of material >1 kg, direct/indirect exposure, having the job title of engineer, using a respirator, using a ventilated or unventilated enclosure, and the job task of powder handling. The mixed linear models explained >60% of the total variance when using all company- and worker-level determinants to create the three exposure models. The cross-validated RMSE values for each of the three mixed models ranged from 2.50 to 4.23. Meanwhile, the ridge regression models, without all two-way interactions, estimated cross-validated RMSE values of 2.85, 2.23, and 2.76 for the predictive models of inhalable EC, respirable EC, and TEM, respectively. CONCLUSIONS:The ridge regression models demonstrated the best performance for predicting exposures to CNT/F for each exposure metric, although they only provided a modest predictive capability. Therefore, it was concluded that the models alone would not be adequate in predicting workplace exposures and would need to be integrated with other methods.

Project description:The continuous real-time monitoring of human health using biomedical sensing devices has recently become a promising approach to the realization of distant health monitoring. In this paper, the piezoelectric characteristics of the silk fibroin (SF) natural polymer were analyzed as the material used for obtaining sensing information in the application of distance health monitoring. To enhance the SF piezoelectricity, this paper presents the development of a novel SF-based sensor realized by combining SF with different carbon nanofiber (CNF) densities, and for such newly developed SF-based sensors comprehensive performance analyses have been performed. Versatile methods including the scanning electron microscope, Fourier transform infrared spectroscopy, Raman and X-ray diffraction measurements and impedance analysis were used to study the morphologic, mechanical and electrical properties of the developed SF-based sensor. The SF with CNF samples was analyzed for three different pressure loads (40 N, 60 N and 80 N) in 500 compression test cycles. The analyses thoroughly describe how combining natural polymer SF with different CNF densities impacts the piezoelectricity and mechanical strength of the proposed SF-based sensor. The developed piezoelectric SF-based sensors were further tested on humans in real medical applications to detect generated piezoelectric voltage in versatile body movements. The maximum piezoelectricity equal to 2.95 ± 0.03 V was achieved for the jumping movement, and the SF sample with a CNF density equal to 0.4% was tested. Obtained results also show that the proposed SF-based sensor has an appropriate piezoelectric sensitivity for each of the analyzed body movement types, and that the proposed SF-based sensor can be applied in real medical applications as a biomedical sensing device. The proposed SF-based sensor’s practical implementation is further confirmed by the results of cytotoxicity analyses, which show that the developed sensor has a non-toxic and biocompatible nature and can be efficiently used in skin contact for biomedical wearable health monitoring applications.

Dataset Information

Hybrid Materials Based on Carbon Nanotubes and Nanofibers for Environmental Applications.

Publications

Hybrid Materials Based on Carbon Nanotubes and Nanofibers for Environmental Applications.

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