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Attention Score in Context
| Title |
Quantitative Determination of Fluorine Content in Blends of Polylactide (PLA)–Talc Using Near Infrared Spectroscopy
|
|---|---|
| Published in |
Sensors, August 2016
|
| DOI | 10.3390/s16081216 |
| Pubmed ID | |
| Authors |
Elena Tamburini, Chiara Tagliati, Tiziano Bonato, Stefania Costa, Chiara Scapoli, Paola Pedrini |
| Abstract |
Near-infrared spectroscopy (NIRS) has been widely used for quantitative and/or qualitative determination of a wide range of matrices. The objective of this study was to develop a NIRS method for the quantitative determination of fluorine content in polylactide (PLA)-talc blends. A blending profile was obtained by mixing different amounts of PLA granules and talc powder. The calibration model was built correlating wet chemical data (alkali digestion method) and NIR spectra. Using FT (Fourier Transform)-NIR technique, a Partial Least Squares (PLS) regression model was set-up, in a concentration interval of 0 ppm of pure PLA to 800 ppm of pure talc. Fluorine content prediction (R²cal = 0.9498; standard error of calibration, SEC = 34.77; standard error of cross-validation, SECV = 46.94) was then externally validated by means of a further 15 independent samples (R²EX.V = 0.8955; root mean standard error of prediction, RMSEP = 61.08). A positive relationship between an inorganic component as fluorine and NIR signal has been evidenced, and used to obtain quantitative analytical information from the spectra. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Switzerland | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 18 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 18 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 3 | 17% |
| Student > Ph. D. Student | 3 | 17% |
| Student > Master | 2 | 11% |
| Other | 1 | 6% |
| Student > Bachelor | 1 | 6% |
| Other | 1 | 6% |
| Unknown | 7 | 39% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 2 | 11% |
| Biochemistry, Genetics and Molecular Biology | 1 | 6% |
| Computer Science | 1 | 6% |
| Agricultural and Biological Sciences | 1 | 6% |
| Chemistry | 1 | 6% |
| Other | 1 | 6% |
| Unknown | 11 | 61% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 04 August 2016.
All research outputs
#22,756,649
of 25,371,288 outputs
Outputs from Sensors
#16,638
of 24,293 outputs
Outputs of similar age
#339,646
of 381,627 outputs
Outputs of similar age from Sensors
#155
of 215 outputs
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