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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5925_Библиотеки_им_академика_М_И_Перельмана.pdf
300 7 Comprehensive Insights into Atomic Emission Spectroscopy
signal intensities, while quenching leads to reduced intensities. These effects can
distort the quantification of the target element.
• Ionization interferences: Elements in the sample matrix with significantly differ-
ent ionization potentials compared to the analyte can interfere with the ionization
processes in the plasma. This can result in inaccurate measurements, particularly
in ICP-AES.
• Formatio n of complexes: Formation of chemical complexes between the analyte
and other species in the sample can change the excitation and emission
characteristics of the element, causing spectral interferences.
• Absorption of radiation: Compounds with strong absorption characteristics in the
wavelength range of interest can
reduce the
intensity of the emitted light or
interfere with the transmission of the light to the detector.
• Chemical reactions: Some chemical reactions may occur within the sample,
altering the concentration and chemical form of the analyte. These reactions can
impact the emissi on behavior.
• Ionization and matrix effects: In techniques such as ICP-AES, the ionization and
matrix effects can influence the signal intensity. These effects are especially
pronounced when the sample matrix contains elements with significantly different ionization potentials.
• Matrix matching: In some cases, the matrix of the calibration standards may not
match the sample matrix, leading to errors in quantification. Matrix matching, by
preparing standards with a similar matrix to the sample, can help mitigate this
interference.
7.8.3 Physical Interferences
Physical factors, such as the presence of particulates, can affect the sample introduction system and the stability of the plasma or flame. These interferences can result in
unstable signals and reduced precision. The most common types of physical
interferences are:
• Particulate interferences: Result from sample particles clogging the introduction
system, disrupting flow, causing unstable signals, and reduced sensitivity.
• Gas flow interferences: Irregular gas flow, such as carrier or auxiliary gases, can
destabilize the plasma or flame, causing signal fluctuations and reduced precision.
• Sample introduction issues: Efficiency in sample introduction is critical;
variations or aspiration errors can lead to inconsistent results.
• Plasma stability: In ICP-AES, plasma stability is crucial. Impurities in argon gas,
torch changes, or radiofrequency power variations can lead to spectral
interferences and imprecise measurements.
• Torch configurat
excitation efficiency, misalignment causing physical interferences.
• Sample matrix composition: Complex or viscous sample matrices affect atomiza-
tion and plasma or flame stability, further contributing to physical interferences.
ion: Torch and
nebulizer alignment impacts atomization and
7.8 Interferences of AES 301
7.8.4 Memory Effects
Memory effects in AES occur when resi dual traces of previously analyzed samples
contaminate subsequent analyses. This contamination can lead to inaccurate results,
as the carryover of analyte signals can skew the data for new samples. Memory
effects are particularly problematic in sequential analyses where different samples
are introduced without adequate cleaning. Key aspects of memory effects are
provided in the following sections.
7.8.4.1 Carryover Contamination
Residual amounts of the analyte from previous analyses can remain in the sample
introduction system, such as the nebulizer, spray chamber, or atomizer, leading to
false readings in subsequent measurements.
7.8.4.2 Influence on Calibration
Memory effects can affect the reliability of calibration curves, causing discrepancies
in the expected versus measured concentrations of the analyte.
7.8.4.3 Variability in Results
The presence of memory effects can introduce significant variability in analytical
results, making it difficult to achieve consistent and reproducible measurements.
7.8.4.4 Mitigation Strategies
To prevent memory effects, it is crucial to implement proper cleaning and rinsing
procedures, including:
• Thorough cleaning: Regular cleaning of the sample introduction system and all
relevant components to remove any residual traces of analytes.
• Rinsing protocols: Implementing systematic rinsing protocols between analyses,
using appropriate solvents to ensure that any carryover is minimized.
• Blanks and standards: Running blank samples and calibration standards between
different sample analyses to identify and account for potential memory effects.
7.8.5 Background Emission
Background emission in AES refers to the signals originating from the sample
matrix or impurities within the analytical instrument. This backgrou nd noise can
interfere with the detection of the analyte’s signal, making it difficult to accurately
quantify the target element. Key aspects of background emission are provided in the
following sections.
7.8.5.1 Source of Background Emission
Background signals can aris e from the sample matrix itself, particularly in complex
matrices where multiple elements or compounds are present. Impurities within the
302 7 Comprehensive Insights into Atomic Emission Spectroscopy
instrument, such as contaminants in the excitation source or the optical path, can also
contribute to background emission.
7.8.5.2 Impact on Signal Detection
Background emission can obscure the analyte’s spectral lines, making it challenging
to differentiate the desired signal from the noise. This can lead to reduced sensitivity
and lower detection limits for the analysis.
7.8.5.3 Fluctuations in Background Signal
Variability in background emission can result from changes in experimental
conditions, such as fluctuations in temperature or plasma stability. This variability
can complicate the interpretation of results and affect the reproducibility of
measurements.
7.8.5.4 Mitigation Strategies
To minimize the impact of background emission, several strategies can be
employed:
• Background correction techniques: Utilizing background correction methods,
such as subtracting baseline signals or using a reference spectrum, can help
isolate the analyte’ s signal from background noise.
• Optimizing instrument conditions: Fine-tuning operational parameters, such as
the excitation source and sample introduction system, can reduce background
interference and improve signal clarity.
• Regular maintenance: Ensuring proper maintenance and cleanliness of the ana-
lytical instrument can help minimize impurities that contr ibute to background
emission.
7.8.6 Interference by Molecular Emission
Interference by molecular emission occurs when molecules present in the sample
emit light, and their emission lines overlap with those of the analyte. This overlap
can lead to inaccuracies in the identification and quantification of the target element,
complicating the analysis. Key aspects of this interference are provided in the
following sections.
7.8.6.1 Source of Molecular Emission
Molecular species in the sample can emit light due to their own electronic transitions.
This emission can occur in complex mixtures where both atomic and molecular
species are present.
7.8.6.2 Overlap of Emission Lines
When the emission lines of the molecular species coincide with those of the analyte,
it becomes difficult to distinguish between the two signals. This overlap can lead to
7.9 Strategies for Overcoming and Controlling Interferences in AES 303
erroneous results, as the measured intensity may reflect contributions from both the
analyte and the interfering molecular emission.
7.8.6.3 Complex Mixtures
Interference by molecular emission is particularly problematic in complex matrices,
such as biological or environmental samples, where various organic and inorganic
compounds can coexist.
7.8.6.4 Mitigation Strategies
To address interference by molecular emission, several strategies can be employed:
• Selective wavelength measurement: Choosing specific wavelengths that are less
likely to be affected by mole cular emission can help reduce interference and
improve analytical accuracy.
• Sample preparation: Utilizing sample preparation techniques to separate or
eliminate interfering molecular species can enhance the clarity of the analyte’s
signal.
• Spectral resolution improvement: Increasing the spectral resolution of the detec-
tion system can help differentiate between closely spaced emission lines,
allowing for better identification of the analyte.
7.9 Strategies for Overcoming and Controlling Interferences
in AES
Overcoming and controlling interferences in AES is essential for obtaining accurate
and reliable analytical results. The following sections provide strategies and methods
commonly employed to address and mitigate interferences in AES.
7.9.1 Wavelength Selection
Choose emission lines for analysis that are less susceptible to spectral interferences.
Select wavelengths with minimal overlap with other elements or molecular bands.
7.9.2 Internal Standards
Incorporate internal standards with known emission lines into the analysis. These
can help identify and correct for interferences, enhancing accuracy.
304 7 Comprehensive Insights into Atomic Emission Spectroscopy
7.9.3 Spectral Deconvolution
Employ advanced data analysis techniques, such as spectral deconvolution, to
separate and quantify overlapping spectral lines, especially in complex matrices.
7.9.4 Matrix Matching
Prepare calibration standards with a matrix similar to the sample. This helps account
for matrix-induced interferences and improves accuracy.
7.9.5 Chemical Modifiers
Add chemical modifiers or ionization suppressors to the sample to reduce the impact
of chemical interferences.
7.9.6 Chemical Separation
Use sample preparation techniques such as chemical separation or preconcentration
to isolate the analyte from potential interferents.
7.9.7 Optimize Instrument Conditions
Ensure the instrument is properly maintained and optimized. This includes
optimizing plasma conditions, adjusting the torch and nebulizer alignment and
maintaining gas flow stability.
7.9.8 Background Correction
Apply background correction techniques to subtract unwanted background signals
caused by matrix or spectral interferences.
7.9.9 Sample Dilution
In cases where high analyte concentrations lead to matrix-induced interferences,
dilution of the sample can help reduce the interference effects.
7.10 Types of Atomic Emission Spectroscopy 305
7.9.10 Rinsing and Cleaning
Implement rigorous cleaning and rinsing procedures for the sample introduction
system and the analytical instrument to prevent memory effects from previous
samples.
7.9.11 Data Quality Control
Regularly monitor the data quality, check for drift or baseline shifts, and verify the
linearity of the instrument. Reanalyze samples or standards if deviations are
observed.
7.9.12 Blank Corrections
Run blank samples to account for the background signal from the matrix and subtract
it from the sample signal to correct for interference.
7.9.13 Calibration Standards
Ensure that calibration standards closely match the sample matrix to improve the
accuracy of quan tification.
7.9.14 Standard Addition Method
In complex matrices, use the standard addition method to directly measure the
interference and correct the results accordingly.
7.9.15 Selective Spectroscopy
For complex matrices with known interference sources, consider using selective
spectroscopy or a wavelength range where interference is minimal.
7.10 Types of Atomic Emission Spectroscopy
AES encompasses several variations and techniques for analyzing the elemental
composition of samples. The following sections provide some of the common types
of AES.
306 7 Comprehensive Insights into Atomic Emission Spectroscopy
7.10.1 Flame Emission Spectroscopy (FES)
FES, also known as flame photometry or flame atomic emission spectroscopy
(FAES), is a specialized analytical technique used for the quantitative analysis of
alkali and alkaline earth metal elements in various sample types. While FES and
flame photometry refer to the same analytical method, the latter term emphasizes the
measurement of the intensity of light emitted by excited atoms in a flame. The
following sections provide a detailed discussion of flame photometry.
7.10.1.1 Principle
Flame photometry is based on the principle that when a samp le containing alkali or
alkaline earth metal ions is introduced into a flame, the atoms of these metal ions are
vaporized and excited by the high-temperature flame. As these excited atoms return
to their ground state, they emit light at characteristic wavelengths, which are specific
to each element. The intensity of this emitted light is directly proportional to the
concentration of the metal in the sample.
7.10.1.2 Key Components
Following are the key components of FES:
• Flame source: A flame source, typically fueled by a mixture of
acetylene and air, provides the high-temperature environment required for
vaporizing and exciting the sample.
• Sample introduction system: The sample is introduced into the flame using a
nebulizer, which generates a fine aerosol of the sample solution. The aerosol is
carried into the flame by an inert gas.
• Burner: The burner, where the sample aerosol is introduced into the flame, is a
critical component. It helps in atomizing the sample and creating a stable flame.
• Optical system: The emitted light from
an optical system, which typically includes a monochromator or filters to select
the specific wavelengths corresponding to the elements of interest.
• Detector: A photodetector,
measures the inte nsity of the selected emission lines.
• Data analysis system: The detector output is processed by a data analysis system,
which calculates the concentration of the analyzed elements based on the
measured intensities.
such as
the flame
a photomultiplier tube or photodiode,
is collected and passed through
gases
such as
7.10.1.3 Applications
Flame photometry is primarily used for the quantitative analysis of alkali metals
(e.g., sodium, potassium, lithium) and alkaline earth metals (e.g., calcium, magnesium) in various sample types. It is widely employed in clinical laboratories for
analyzing blood and urine samples, in environmental laboratories for soil and water
analysis, and in industries such as agriculture for fertilizer analysis.
7.10 Types of Atomic Emission Spectroscopy 307
7.10.2 ICP-AES
ICP-AES, which is also known as ICP optical emission spectroscopy (ICP-OES), is
a highly advanced analytical technique used for the quantitative and qualitative
analysis of a wide range of elements in various sample types. ICP-AES is known
for its exceptional sensitivity, precision, and the ability to simultaneously analyze
multiple elements. The following sections provide an overview of ICP-AES.
7.10.2.1 Principle
ICP-AES operates on the principle of using a high-temperature argon plasma to
atomize and excite the atoms of elements in the sample. When the atoms are excited,
they emit characteristic wavelengths of light. The emitted light is dispersed and
detected, allowing for the identification and quantification of elements based on their
unique spectral lines.
7.10.2.2 Key Components
Following are the key components of ICP-AES:
• Plasma source: The heart of ICP-AES is the inductively coupled plasma source,
which is a high-temperature, ionized argon gas discharge. This plasma provides
the energy required to atomize and excite the sample atoms.
• Sample introduction syst em: Samples are introduced into the plasma using a
nebulizer or an aerosol generator. The sample is typically in the form of a liquid
solution, but solid samples can be converted into solutions for analysis.
• Spectrom eter: ICP-AES employs a spectrometer with
prisms to disperse the emitted light into its individual wavelengths. This allows
for the selection of specific wavelengths for analysis.
• Detector: A photodetector, often a charge-
photomultiplier tube, measures the intensity of the emitted light at selected
wavelengths.
• Data analysis system: The detector output is processed by a computer-based data
analysis system, which calculates the concentrations of the analyzed elements
based on the measured intensities.
diffraction gratings
coupled
device (CCD) or
or
7.10.2.3 Applications
ICP-AES is widely used in various fields, including environmental analysis,
pharmaceuticals, metallurgy, food and beverages, and geological research. It is
particularly valuable for analyzing trace and ultra-trace elements, making it suitable
for compliance with regulatory requirements and quality control.
7.10.3 Spark Emission Spectroscopy
Spark emission spectroscopy uses electrical discharges, or sparks, to excite the
atoms in solid samples. It is often used in metallurgy and the analysis of metals.
308 7 Comprehensive Insights into Atomic Emission Spectroscopy
7.10.4 Arc Emission Spectroscopy
Similar to spark emission spectroscopy, arc emission spectroscopy uses an electric
arc to excite atoms in solid samples. It is also commonly used for metals and alloys
analysis.
7.10.5 Laser-Induced Breakdown Spectroscopy (LIBS)
LIBS employs laser pulses to generate micro-plasmas on the sample’s surface. The
resulting emission spectrum is used for elemental analysis and is particula rly useful
for in situ and remote analysis.
7.10.6 Glow Discharge Emission Spectroscopy (GD-ES)
GD-ES uses a low-pressure glow discharge to excite atoms in solid samples. It is
suitable for depth profiling and bulk analysis of conductive materials.
7.10.7 Dielectric Barrier Discharge Atomic Emission Spectroscopy
(DBD-AES)
DBD-AES employs a dielectric barrier discharge to excite atoms in the sample. It is
useful for the analysis of volatile organic compounds and environmental samples.
7.10.8 Microwave-Induced Plasma Atomic Emission Spectroscopy
(MIP-AES)
MIP-AES uses a microwave-induced plasma to excite atoms in the sample. It offers
advantages for some specific applications.
7.10.9 Optical Emission Spectroscopy (OES)
OES is a general term for techniques that use the optical emission of excited atoms
for analysis. This can include FES, ICP-AES, spark and arc emission spectroscopy,
and others.
7.11 Recent Advancements in AES 309
7.11 Recent Advancements in AES
7.11.1 Miniaturization and Portable AES Devices
• Trend: There has been a growing trend toward the development of miniaturized
and portable AES instruments. These devices allow for on-site analysis, increasing the accessibility of elemental analysis in various fields.
• Advancement: New designs incorporate compact spectrometers and portable
plasma sources, making
for bulky laboratory equipment.
• Case study: A study conducted by researchers demonstrated the effectiveness of a
portable AES system for detecting heavy metals in contaminated water sources.
The miniaturized system provided rapid results with detection limits comparable
to traditional lab-based systems, showcasing its potential for environmental
monitoring.
ble to conduct field analyses without the need
it feasi
7.11.2 Hyphenation Techniques
• Trend: Hyphenating AES with other analyt ical techniques, such as chromatogra-
phy and mass spectrometry, is becoming increasingly common. This integration
enhances the overall analytical capabilities and provides more
comprehensive data.
• Advancement: Techniques such as AES-GC or
and identification of complex mixtures before elemental analysis.
• Case study: Researchers utilized AES coupled with HPLC to analyze trace
elements in pharmaceuticals. This method allowed for the simultaneous determination of multiple elements and enhanced the understanding of the interactions
between drug components and trace elements.
AES-LC allow for
the separation
7.11.3 Improved Calibration Methods
• Trend: Advanced calibration techniques are being developed to enhance the
accuracy and precision of AES. These methods include the use of internal
standards and advanced mathematical modeling to compensate for potential
interferences.
• Advancement: Machi
calibration models that can adapt to varying sample matrices and reduce the
impact of spectral interferences.
• Case study: A study by a research team in Asia employed machine learning
algorithms to improve the calibration of an AES system for analyzing soil
samples. By incorporating various environmental parameters into the model,
they achieved enhanced accuracy in determining the concentrations of heavy
metals in agricultural soils.
ne learn
ing algorithms are increasingly used to develop