Analysis Of Chemical Mixtures Research Articles

Combined Paper Centrifugal Chromatographic Separation and SERS Detection for Multicomponent Substances.

The separation and chemical analysis of mixtures in an emergency situation represent major challenges, especially in remote or poverty-stricken areas. A novel method was developed for the rapid separation and detection of multiple components via paper centrifugal chromatography, which costs as little as $2.26 US. The method was realized based on the combination of portable paper centrifugal chromatography and surface-enhanced Raman scattering (SERS) detection. This coupled technique was successfully implemented for the separation and qualitative analysis of a rhodamine 6G-crystal violet mixture and a colorless aniline-pyrocatechol-benzidine mixture. A chromatographic mobile phase was collected using absorbent cotton, which was demonstrated to have no effect on the SERS results. The optimized device achieved rapid and effective separation of the colorless aniline-pyrocatechol-benzidine mixture with a high centrifugal force (0.3303π2 N). The newly developed method involving multicomponent paper centrifugal chromatography-SERS detection will be of great value for emergency-related substance separation and analysis in remote and poor areas.

Sensor Array Complexity and Analytical Capability

Introduction Chemical sensors are generally associated with tasks involving detection of chemical stimuli while conventional analytical laboratory instruments are associated with analysis of chemical mixtures. While sensing tasks may not necessarily be perceived as involving chemical mixture analysis and evaluated as such, there is always an underlying analytical context in any sensing problem. This context becomes progressively more important as the complexity of chemical environment in which the sensing task is to be conducted increases, through both the number of potential intereferents as well as uncertainty regarding the composition of the chemical background.Design of individual sensors can be reasonably predicated on maximizing the sensitivity and selectivity of a narrowly-scoped chemical stimulus against expected backgrounds. However, as one may expect, design considerations can become significantly more complicated for systems based on sensor arrays. Arrays consisting of sensors that exhibit diverse, partially-selective response to a wide range of chemical stimuli allow, in theory, selective detection of a range of chemical stimuli against more complex backgrounds. [1] This is generally supported by analogy to biological olfaction as well as experimental work over the past few decades. Intuitively, a clear consequence of moving towards applications involving more complex and uncertain sensing tasks is a requirement that the system become less “sensor-like” and more “instrument-like.” A significant challenge in sensor array design has been that these concepts are not well-understood in a quantitative and device-agnostic fashion, making it difficult appropriately match sensor array and sensing task complexity in a predictable and systematic fashion. This presentation will discuss how analytical capabilities can be characterized on a continuum between simple sensor arrays and classical analytical instruments and the implications for sensor array design and selection. Approach Sensor array capability for determining composition of fixed, finite domains of independent chemical stimuli can be expressed and predicted using information-theoretic measures and knowledge of individual chemical response functions for each chemical stimulus/sensor pair. [2-4] Moving towards expression of more general-purpose analytical capability (i.e. dynamic, high-dimensional domains of independent chemical stimuli) requires a shift from consideration of the span of specific chemical stimuli to one of the span of molecular parameters probed by the sensor technology. Sensor array capability for addressing generalized chemical analysis problems can then be expressed in terms of the adjacency in molecular parameter space induced by the underlying response mechanism of a sensor technology and the manner in which specific sensor array configurations induce particular array sensitivities across this same domain of molecular parameters. Hypothetical sensor systems based on optical detection and chemisorption are used to illustrate these concepts. In particular, this framework allows consideration of the manner in which sensor arrays and laboratory instruments occupy a continuum of complexity with which they interact with the chemical environment. Conclusions This work demonstrates the value in developing improved first-principles models of sensor response grounded in molecular parameters rather than specific chemical stimuli. Such an understanding allows expression and prediction of the extent to which a sensor array offers “instrument like” general purpose capability as opposed to narrowly scoped sensing tasks. The complexity of a given sensor technology’s interaction with the chemical environment ultimately provides a limit to the complexity of chemical sensing tasks that are addressable by sensor arrays based on this technology. Considering these aspects of chemical sensor array design allows more reliable selection and design of sensor arrays for complex, real-world applications.

Sensor Array Complexity and Analytical Capability

Introduction Chemical sensors are generally associated with tasks involving detection of chemical stimuli while conventional analytical laboratory instruments are associated with analysis of chemical mixtures. While sensing tasks may not necessarily be perceived as involving chemical mixture analysis and evaluated as such, there is always an underlying analytical context in any sensing problem. This context becomes progressively more important as the complexity of chemical environment in which the sensing task is to be conducted increases, through both the number of potential intereferents as well as uncertainty regarding the composition of the chemical background.Design of individual sensors can be reasonably predicated on maximizing the sensitivity and selectivity of a narrowly-scoped chemical stimulus against expected backgrounds. However, as one may expect, design considerations can become significantly more complicated for systems based on sensor arrays. Arrays consisting of sensors that exhibit diverse, partially-selective response to a wide range of chemical stimuli allow, in theory, selective detection of a range of chemical stimuli against more complex backgrounds. [1] This is generally supported by analogy to biological olfaction as well as experimental work over the past few decades. Intuitively, a clear consequence of moving towards applications involving more complex and uncertain sensing tasks is a requirement that the system become less “sensor-like” and more “instrument-like.” A significant challenge in sensor array design has been that these concepts are not well-understood in a quantitative and device-agnostic fashion, making it difficult appropriately match sensor array and sensing task complexity in a predictable and systematic fashion. This presentation will discuss how analytical capabilities can be characterized on a continuum between simple sensor arrays and classical analytical instruments and the implications for sensor array design and selection. Approaches Sensor array capability for determining composition of fixed, finite domains of independent chemical stimuli can be expressed and predicted using information-theoretic measures and knowledge of individual chemical response functions for each chemical stimulus/sensor pair. [2-4] Moving towards expression of more general-purpose analytical capability (i.e. dynamic, high-dimensional domains of independent chemical stimuli) requires a shift from consideration of the span of specific chemical stimuli to one of the span of molecular parameters probed by the sensor technology. Sensor array capability for addressing generalized chemical analysis problems can then be expressed in terms of the adjacency in molecular parameter space induced by the underlying response mechanism of a sensor technology and the manner in which specific sensor array configurations induce particular array sensitivities across this same domain of molecular parameters. Hypothetical sensor systems based on optical detection and chemisorption are used to illustrate these concepts. In particular, this framework allows consideration of the manner in which sensor arrays and laboratory instruments occupy a continuum of complexity with which they interact with the chemical environment. Conclusions This work demonstrates the value in developing improved first-principles models of sensor response grounded in molecular parameters rather than specific chemical stimuli. Such an understanding allows expression and prediction of the extent to which a sensor array offers “instrument like” general purpose capability as opposed to narrowly scoped sensing tasks. The complexity of a given sensor technology’s interaction with the chemical environment ultimately provides a limit to the complexity of chemical sensing tasks that are addressable by sensor arrays based on this technology. Considering these aspects of chemical sensor array design allows more reliable selection and design of sensor arrays for complex, real-world applications.

Direct inversion in the spectral subspace: A novel method for quantitative and qualitative analysis of chemical mixtures

A novel method, called Direct Inversion in the Spectral Subspace (DISS), has been developed for the quantitative (and partly qualitative) analysis of chemical mixtures. The method belongs to the broad group of “supervised classification” methods: its use necessitates the components’ “pure” spectra, either experimental or computed. On the basis of three simple conditions, an elegant, linearized system of equations has been deduced, taking into account a sole restriction via the Lagrange’ multiplier method. This restriction is seemingly redundant but it has been shown that with its use the unknown normalization constant of the components’ descriptive weighted average (CDWA) spectrum can be taken into consideration. The system of linearized equations can be solved repeatedly until convergence. Any kind of spectra can be used; the method does not require the non-negativity of spectral data points. Two versions of the new method have been developed: the normalized and the non-normalized versions regarding the components’ spectra. In ideal cases, the non-normalized version of the DISS method provides a mixture’s accurate composition due to the iteration for getting the correct norm of the CDWA spectrum. Realistically, the normalized version of the DISS method identifies a mixture’s composition within a few molar percentage points accuracy, according to the test results in IR and 1H-NMR spectroscopy. The normalized method functions without any calibration measurements and needs only a control of accuracy; it is hoped that it will be a useful tool for chemical and biochemical analysis as well as for spectral databases. The DISS method is also useful for qualitative analyses in a limited sense: in the case of computed spectra of the components the set of the de facto components determined could be somewhat wider than those existing in the real system.

Analysis of resulting data from estrogen receptor reporter gene assays

Screening methods for detecting single chemicals and chemical mixtures that have endocrine effects are of increasing importance. One such assay used by many laboratories to screen for potential estrogenicity is the estrogen receptor (ER) transcriptional activation assay, also known as an ER “reporter gene assay.” When applied in screening situations or to mechanistic studies, the assay appears to be quite reliable in giving a qualitative indication of a compound’s potential to activate ER-regulated target genes. Reporter gene assays also are advantageous for more complex applications, such as the analysis of chemical mixtures, but such applications present some challenges for statistical analysis. In particular, the need to transiently transfect cells with DNA constructs each time a reporter gene experiment is run may be a major factor contributing to a significant amount of interexperiment variability in strictly reproducing concentration-effect curves. This article reports on the use of nonlinear mixed models to account for the different sources of variability in analyzing such data. A population-averaged model is selected for use in the analysis of mixtures of chemicals. The models are illustrated with data from an ER reporter gene assay used to analyze tertiary mixtures of chemicals expected to exhibit additivity or synergy based on prior scientific reports. The models yielded the expected conclusions, and thus, validated the statistical approach.

Chemical and radiochemical separation of strontium and yttrium

A procedure is presented for the chemical and radiochemioal separation of strontium and yttrium in mixtures which is based on the selective carrying of strontium by ignited barium sulphate leaving yttrium in the solution phase; the carried strontium is recovered by leaching with aqueous hydrochloric acid solution. In order to make strontium radiochemically free from yttrium activity, an initial isotopic dilution of the latter with ≥ 1 mg of inactive yttrium is an essential requirement. The method is applicable to chemical analysis of mixtures involving 1 to 3 mg of strontium and 1 to 16 mg of yttrium, both components being determined complexometrically after separation.