Calibration curve

About: Calibration curve is a research topic. Over the lifetime, 6552 publications have been published within this topic receiving 95128 citations.

Application of laser induced breakdown spectroscopy for total carbon quantification in soil samples

Abstract: The increase of greenhouse gas (i.e., CO2) levels in the atmosphere has caused noticeable climate change. Many nations are currently looking into methods of permanent underground storage for CO2 in an attempt to mitigate this problem. The goal of this work is to develop a process for studying the total carbon content in soils before, during, and after CO2 injection to ensure that no leakage is occurring or to determine how much is leaking if it is occurring and what effect it will have on the ecosystem between the injection formation and the atmosphere. In this study, we quantitatively determine the total carbon concentration in soil using laser-induced breakdown spectroscopy (LIBS). A soil sample from Starkville, Mississippi, USA was mixed with different amounts of carbon powder, which was used as a calibration for additional carbon in soil. Test samples were prepared by adding different but known amounts of carbon powder to a soil sample and then mixing with polyvinyl alcohol binder before being pressed into pellets. LIBS spectra of the test samples were collected and analyzed to obtain optimized conditions for the measurement of total carbon in soil with LIBS. The total carbon content in the samples was also measured by a carbon analyzer, and the data (average of triplicates) were used as a reference in developing calibration curves for a modified version of the single linear regression model and the multiple linear regression model. The calibration data were then used to determine the total carbon concentration of an unknown sample. This work is intended to be used in the initial development of a miniaturized, field-portable LIBS analyzer for CO2 leak detection.

Using nonequilibrium capillary electrophoresis of equilibrium mixtures for the determination of temperature in capillary electrophoresis.

Abstract: Until now, all methods for temperature sensing in capillary electrophoresis (CE) relied on molecular probes with temperature-dependent spectral/optical properties. Here we introduce a nonspectroscopic approach to determining temperature in CE. It is based on measuring a temperature-dependent rate constant of complex dissociation by means of a kinetic CE method known as nonequilibrium capillary electrophoresis of equilibrium mixtures (NECEEM). Conceptually, a calibration curve of "the rate constant versus temperature" is built using NECEEM and a CE instrument with a reliable temperature control or, alternatively, a nonelectrophoretic method, such as surface plasmon resonance. The calibration curve is then used to find the temperature during CE in the same buffer but with another CE apparatus or under otherwise different conditions (cooling efficiency, length and diameter of the capillary, electrical field, etc.). In this proof-of-principle work, we used the dissociation of a protein-DNA complex to demonstrate that the NECEEM-based temperature determination method allows for temperature determination in CE with a precision of 2 degrees C. Then, we applied the NECEEM-based temperature determination method to study heat dissipation efficiency in CE instruments with active and passive cooling of the capillary. The nonspectroscopic nature of the method makes it potentially applicable to nonspectroscopic detection schemes, e.g. electrochemical detection. A "kinetic probe" can be coloaded into the capillary along with a sample for in situ temperature measurements. Higher order chemical reactions can also be used for temperature sensing, provided a kinetic CE method for measuring a corresponding rate constant is available.

Acidity measurement of iron ore powders using laser-induced breakdown spectroscopy with partial least squares regression.

Abstract: Laser-induced breakdown spectroscopy (LIBS) with partial least squares regression (PLSR) has been applied to measuring the acidity of iron ore, which can be defined by the concentrations of oxides: CaO, MgO, Al₂O₃, and SiO₂. With the conventional internal standard calibration, it is difficult to establish the calibration curves of CaO, MgO, Al₂O₃, and SiO₂ in iron ore due to the serious matrix effects. PLSR is effective to address this problem due to its excellent performance in compensating the matrix effects. In this work, fifty samples were used to construct the PLSR calibration models for the above-mentioned oxides. These calibration models were validated by the 10-fold cross-validation method with the minimum root-mean-square errors (RMSE). Another ten samples were used as a test set. The acidities were calculated according to the estimated concentrations of CaO, MgO, Al₂O₃, and SiO₂ using the PLSR models. The average relative error (ARE) and RMSE of the acidity achieved 3.65% and 0.0048, respectively, for the test samples.