Novel Calibration Approach for Monitoring Aerosol Hydrogen Isotopes Using Laser-Induced Breakdown Spectroscopy for Molten Salt Reactor Off-Gas Streams

Online monitoring is a key challenge for the continued development of molten salt reactor (MSR) technology. Laser-induced breakdown spectroscopy (LIBS) has previously been demonstrated to be a viable tool for monitoring aerosolized species and noble gases in real time, but the ability to discern varying isotopes in these streams has not yet been investigated for MSR applications. Tritium will form in MSRs from ternary fission and from (n,α)-reactions occurring in lithium-containing salts. This study compares three spectrometers of varying resolutions and types for measuring hydrogen isotope shifts in LIBS spectra of wetted filter paper. For each spectrometer, multivariate models were built (i.e., principal component regression, partial least squares regression, and multivariate curve resolution) to quantify the isotope ratio. The top models were then modified and corrected to apply the models to aerosol samples with varying isotope ratios. This novel calibration strategy offers an 82% reduction in volume of the calibration samples needed and is a more viable pathway for calibrating deployable LIBS systems. Lastly, this calibration model was compared with an all-aerosol trained model for monitoring hydrogen isotopes during a real-time test where the protium/deuterium ratio, along with representative salt species (i.e., lithium, sodium, and potassium) were adjusted dynamically. Results of this test validated the predictive capabilities of the transferred model and highlighted the capabilities of LIBS for real-time monitoring of MSR effluent streams.

Optimization of machine learning classification models for tumor cells based on cell elements heterogeneity with laser-induced breakdown spectroscopy

The rapid and accurate diagnosis of cancer is an important topic in clinical medicine. In the present work, an innovative method based on laser-induced breakdown spectroscopy (LIBS) combined with machine learning was developed to distinguish and classify different tumor cell lines. The LIBS spectra of cells were first acquired. Then the spectral pre-processing was performed as well as detailed optimization to improve the classification accuracy. After that, the convolutional neural network (CNN), support vector machine (SVM), and K-nearest neighbors were further compared for the optimized classification ability of tumor cells. Both the CNN algorithm and SVM algorithm have achieved impressive discrimination performances for tumor cells distinguishing, with an accuracy of 97.72%. The results show that the heterogeneity of elements in tumor cells plays an important role in distinguishing the cells. It also means that the LIBS technique can be used as a fast classification method for classifying tumor cells.

Hybrid Raman and Laser-Induced Breakdown Spectroscopy for Food Authentication Applications

The issue of food fraud has become a significant global concern as it affects both the quality and safety of food products, ultimately resulting in the loss of customer trust and brand loyalty. To address this problem, we have developed an innovative approach that can tackle various types of food fraud, including adulteration, substitution, and dilution. Our methodology utilizes an integrated system that combines laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy. Although both techniques emerged as valuable tools for food analysis, they have until now been used separately, and their combined potential in food fraud has not been thoroughly tested. The aim of our study was to demonstrate the potential benefits of integrating Raman and LIBS modalities in a portable system for improved product classification and subsequent authentication. In pursuit of this objective, we designed and tested a compact, hybrid Raman/LIBS system, which exhibited distinct advantages over the individual modalities. Our findings illustrate that the combination of these two modalities can achieve higher accuracy in product classification, leading to more effective and reliable product authentication. Overall, our research highlights the potential of hybrid systems for practical applications in a variety of industries. The integration and design were mainly focused on the detection and characterization of both elemental and molecular elements in various food products. Two different sets of solid food samples (sixteen Alpine-style cheeses and seven brands of Arabica coffee beans) were chosen for the authentication analysis. Class detection and classification were accomplished through the use of multivariate feature selection and machine-learning procedures. The accuracy of classification was observed to improve by approximately 10% when utilizing the hybrid Raman/LIBS spectra, as opposed to the analysis of spectra from the individual methods. This clearly demonstrates that the hybrid system can significantly improve food authentication accuracy while maintaining the portability of the combined system. Thus, the successful implementation of a hybrid Raman-LIBS technique is expected to contribute to the development of novel portable devices for food authentication in food as well as other various industries.

Explorative Data Analysis Methods: Application to Laser-Induced Breakdown Spectroscopy Field Data Measured on the Island of Vulcano, Italy

One of the strengths of laser-induced breakdown spectroscopy (LIBS) is that a large amount of data can be measured relatively easily in a short time, which makes LIBS interesting in many areas, from geomaterial analysis with portable handheld instruments to applications for the exploration of planetary surfaces. Statistical methods, therefore, play an important role in analyzing the data to detect not only individual compositions but also trends and correlations. In this study, we apply two approaches to explore the LIBS data of geomaterials measured with a handheld device at different locations on the Aeolian island of Vulcano, Italy. First, we use the established method, principal component analysis (PCA), and second we adopt the principle of the interesting features finder (IFF), which was recently proposed for the analysis of LIBS imaging data. With this method it is possible to identify spectra that contain emission lines of minor and trace elements that often remain undetected with variance-based methods, such as PCA. We could not detect any spectra with IFF that were not detected with PCA when applying both methods to our LIBS field data. The reason for this may be the nature of our field data, which are subject to more experimental changes than data measured in laboratory settings, such as LIBS imaging data, for which the IFF was introduced first. In conclusion, however, we found that the two approaches complement each other well, making the exploration of the data more intuitive, straightforward, and efficient.

Fast Detection of Heavy Metal Content in Fritillaria thunbergii by Laser-Induced Breakdown Spectroscopy with PSO-BP and SSA-BP Analysis

Fast detection of heavy metals is important to ensure the quality and safety of herbal medicines. In this study, laser-induced breakdown spectroscopy (LIBS) was applied to detect the heavy metal content (Cd, Cu, and Pb) in Fritillaria thunbergii. Quantitative prediction models were established using a back-propagation neural network (BPNN) optimized using the particle swarm optimization (PSO) algorithm and sparrow search algorithm (SSA), called PSO-BP and SSA-BP, respectively. The results revealed that the BPNN models optimized by PSO and SSA had better accuracy than the BPNN model without optimization. The performance evaluation metrics of the PSO-BP and SSA-BP models were similar. However, the SSA-BP model had two advantages: it was faster and had higher prediction accuracy at low concentrations. For the three heavy metals Cd, Cu and Pb, the prediction correlation coefficient (R_p²) values for the SSA-BP model were 0.972, 0.991 and 0.956; the prediction root mean square error (RMSEP) values were 5.553, 7.810 and 12.906 mg/kg; and the prediction relative percent deviation (RPD) values were 6.04, 10.34 and 4.94, respectively. Therefore, LIBS could be considered a constructive tool for the quantification of Cd, Cu and Pb contents in Fritillaria thunbergii.

Depth-Resolved Elemental Analysis on Moving Electrode Foils with Laser-Induced Breakdown Spectroscopy

In this study, a new method for the inline measurement of depth profiles on a continuously moving sample with laser-induced breakdown spectroscopy is presented. The ablation profile is generated by ablating the sample with a burst of laser pulses, where the emission spectrum of each laser-induced plasma is analyzed on a spectrometer. A Q-switched Nd:YAG laser at 1064 nm with 10 mJ pulse energy, 6 ns pulse duration and 100 Hz repetition rate was used. The focusing lens for the pulsed laser and a deflection mirror are mounted on a moving stage, which is precisely aligned in height and orientation to the movement of a conveyor belt transporting the sample. The stage speed is actively synchronized to the speed of the moving sample by a wheel encoder to assure that all laser pulses hit the same position at the sample. The feasibility for depth-resolved elemental analysis on moving samples is shown for coatings of electrode foils for lithium-ion batteries. The coating homogeneity was measured at a speed up to 17 m/min. For a 100 μm coating, 10 laser pulses were needed to measure a full depth profile.

Impact of Glass Irradiation on Laser-Induced Breakdown Spectroscopy Data Analysis

Increased absorption of optical materials arising from exposure to ionizing radiation must be accounted for to accurately analyze laser-induced breakdown spectroscopy (LIBS) data retrieved from high-radiation environments. We evaluate this effect on two examples that mimic the diagnostics placed within novel nuclear reactor designs. The analysis is performed on LIBS data measured with 1% Xe gas in an ambient He environment and 1% Eu in a molten LiCl-KCl matrix, along with the measured optical absorption from the gamma- and neutron-irradiated low-OH fused silica and sapphire glasses. Significant changes in the number of laser shots required to reach a 3σ detection level are observed for the Eu data, increasing by two orders of magnitude after exposure to a 1.7 × 10¹⁷ n/cm² neutron fluence. For all cases examined, the spectral dependence of absorption results in the introduction of systematic errors. Moreover, if lines from different spectral regions are used to create Boltzmann plots, this attenuation leads to statistically significant changes in the temperatures calculated from the Xe II lines and Eu II lines, lowering them from 8000 ± 610 K to 6900 ± 810 K and from 15,800 ± 400 K to 7200 ± 800 K, respectively, for exposure to the 1.7 × 10¹⁷ n/cm² fluence. The temperature range required for a 95% confidence interval for the calculated temperature is also broadened. In the case of measuring the Xe spectrum, these effects may be mitigated using only the longer-wavelength spectral region, where radiation attenuation is relatively small, or through analysis using the iterative Saha-Boltzmann method.

Monitoring Xenon Capture in a Metal Organic Framework Using Laser-Induced Breakdown Spectroscopy

Molten salt reactor operation will necessitate circulation of a cover gas to remove certain evolved fission products and maintain an inert atmosphere. The cover gas leaving the reactor core is expected to contain both noble and non-noble gases, aerosols, volatile species, tritium, and radionuclides and their daughters. To remove these radioactive gases, it is necessary to develop a robust off-gas system, along with novel sensors to monitor the gas stream and the treatment system performance. In this study, a metal organic framework (MOF) was engineered for the capture of Xe, a major contributor to the off-gas source term. The engineered MOF column was tested with a laser-induced breakdown spectroscopy (LIBS) sensor for noble gas monitoring. The LIBS sensor was used to monitor breakthrough tests with various Xe, Kr, and Ar mixtures to determine the Xe selectivity of the MOF column. This study offers an initial demonstration of the feasibility of monitoring off-gas treatment systems using a LIBS sensor to aid in the development of new capture systems for molten salt reactors.

Qualitative Analysis of Glass Microfragments Using the Combination of Laser-Induced Breakdown Spectroscopy and Refractive Index Data

We have successfully demonstrated that although there are significant analytical challenges involved in the qualitative discrimination analysis of sub-mm sized (microfragment) glass samples, the task can be solved with very good accuracy and reliability with the multivariate chemometric evaluation of laser-induced breakdown spectroscopy (LIBS) data or in combination with pre-screening based on refractive index (RI) data. In total, 127 glass samples of four types (fused silica, flint, borosilicate and soda–lime) were involved in the tests. Four multivariate chemometric data evaluation methods (linear discrimination analysis, quadratic discrimination analysis, classification tree and random forest) for LIBS data were evaluated with and without data compression (principal component analysis). Classification tree and random forest methods were found to give the most consistent and most accurate results, with classifications/identifications correct in 92 to 99% of the cases for soda–lime glasses. The developed methods can be used in forensic analysis.

Analysis of constituents present in smokeless tobacco (Nicotiana tabacum)using spectroscopic techniques

Laser-Induced Breakdown Spectroscopy (LIBS) is an analytical technique used to identify and quantify the elements present in any type of material present in any phase (solid, liquid, gas, and aerosol). In the present work, our objective is to find the presence of toxic and other elements in chewing tobacco (Nicotiana tabacum) using LIBS. Spectral signatures of elements like C, Fe, Si, Mg, Mn, Ca, Ti, Na, H, N, K, O, along with some toxic elements Al, Sr, Li, Cu, Sb, and Cr are observed in the LIBS spectra of these tobacco samples. The spectral intensity ratio is measured for quantitative analysis of elements present in the samples. Further, Atomic Absorption Spectroscopy is used for determining absolute concentration in these samples. A relation between the AAS result and the relative intensity of spectral lines measured in the LIBS is obtained using regression analysis. The multivariate technique, Principal Component Analysis (PCA), discriminates all the samples based on their toxicity and other constituents. Molecular study (Photoacoustic spectroscopy (PAS), UV-Visible (UV-vis), and FT-IR) of tobacco samples were performed to analyze the molecules present in the tobacco samples.

Identification of Graves' ophthalmology by laser-induced breakdown spectroscopy combined with machine learning method

Diagnosis of the Graves' ophthalmology remains a significant challenge. We identified between Graves' ophthalmology tissues and healthy controls by using laser-induced breakdown spectroscopy (LIBS) combined with machine learning method. In this work, the paraffin-embedded samples of the Graves' ophthalmology were prepared for LIBS spectra acquisition. The metallic elements (Na, K, Al, Ca), non-metallic element (O) and molecular bands ((C-N), (C-O)) were selected for diagnosing Graves' ophthalmology. The selected spectral lines were inputted into the supervised classification methods including linear discriminant analysis (LDA), support vector machine (SVM), k-nearest neighbor (kNN), and generalized regression neural network (GRNN), respectively. The results showed that the predicted accuracy rates of LDA, SVM, kNN, GRNN were 76.33%, 96.28%, 96.56%, and 96.33%, respectively. The sensitivity of four models were 75.89%, 93.78%, 96.78%, and 96.67%, respectively. The specificity of four models were 76.78%, 98.78%, 96.33%, and 96.00%, respectively. This demonstrated that LIBS assisted with a nonlinear model can be used to identify Graves' ophthalmopathy with a higher rate of accuracy. The kNN had the best performance by comparing the three nonlinear models. Therefore, LIBS combined with machine learning method can be an effective way to discriminate Graves' ophthalmology.

Analysis of Rock Varnish from the Mojave Desert by Handheld Laser-Induced Breakdown Spectroscopy

Laser-induced breakdown spectroscopy (LIBS) is a form of optical emission spectroscopy that can be used for the rapid analysis of geological materials in the field under ambient environmental conditions. We describe here the innovative use of handheld LIBS for the in situ analysis of rock varnish. This thinly laminated and compositionally complex veneer forms slowly over time on rock surfaces in dryland regions and is particularly abundant across the Mojave Desert climatic region of east-central California (USA). Following the depth profiling examination of a varnished clast from colluvial gravel in Death Valley in the laboratory, our in situ analysis of rock varnish and visually similar coatings on rock surfaces was undertaken in the Owens and Deep Spring valleys in two contexts, element detection/identification and microchemical mapping. Emission peaks were recognized in the LIBS spectra for the nine elements most abundant in rock varnish-Mn, Fe, Si, Al, Na, Mg, K, Ca and Ba, as well as for H, Li, C, O, Ti, V, Sr and Rb. Focused follow-up laboratory and field studies will help understand rock varnish formation and its utility for weathering and chronological studies.

Sulfur Detection in Soil by Laser Induced Breakdown Spectroscopy Assisted by Multivariate Analysis

Laser-induced breakdown spectroscopy (LIBS) is used for the detection and determination of sulfur content in some organic soil samples. The most suitable sulfur spectral lines for such tasks were found to occur in the vacuum ultraviolet (VUV) spectral region and they were used for the construction of calibration curves. For the analysis, both univariate and multivariate statistical models were employed. The results obtained by the different analysis techniques are evaluated and compared. The present study demonstrates both the applicability and efficiency of LIBS for fast sulfur detection in soil matrices when aided by multivariate analysis methods improving the accuracy and extending the potential use of LIBS in such applications.

Comparison of Laser Pulse Duration for the Spatially Resolved Measurement of Coating Thickness with Laser-Induced Breakdown Spectroscopy

In this study, a method is presented to measure precisely the thickness of coated components based on laser-induced breakdown spectroscopy (LIBS). The thickness is determined by repetitively ablating the coating with ultrashort laser pulses, monitoring the spectrum of the generated plasma and calculating the coating thickness from the specific plasma signal in comparison to a reference measurement. We compare different pulse durations of the laser (290 fs, 10 ps, 6 ns) to extend the material analysis capabilities of LIBS to a real thickness measurement tool. The method is designed for production processes with known coating materials. Here, we show this for a nickel coating and a tungsten carbide coating on a copper sample with thicknesses from 5–30 µm.

Estimation of the Fe and Cu Contents of the Surface Water in the Ebinur Lake Basin Based on LIBS and a Machine Learning Algorithm

Traditional technology for detecting heavy metals in water is time consuming and difficult and thus is not suitable for quantitative detection of large samples. Laser-induced breakdown spectroscopy (LIBS) can identify multi-state (such as solid, liquid, and gas) substances simultaneously, rapidly and remotely. In this study, water samples were collected from the Ebinur Lake Basin. The water samples were subjected to LIBS to extract the characteristic peaks of iron (Fe) and copper (Cu). Most of the quantitative analysis of LIBS rarely models and estimates the heavy metal contents in natural environments and cannot quickly determine the heavy metals in field water samples. This study creatively uses the Fe and Cu contents in water samples and the characteristics of their spectral curves in LIBS for regression modelling analysis and estimates their contents in an unknown water body by using LIBS technology and a machine learning algorithm, thus improving the detection rate. The results are as follows: (1) The Cu content of the Ebinur Lake Basin is generally higher than the Fe content, the highest Fe and Cu contents found within the basin are in the Ebinur Lake watershed, and the lowest are in the Jing River. (2) A number of peaks from each sample were found of the LIBS curve. The characteristic analysis lines of Fe and Cu were finally determined according to the intensities of the Fe and Cu characteristic lines, transition probabilities and high signal-to-background ratio (S/B). Their wavelengths were 396.3 and 324.7 nm, respectively. (3) The relative percent deviation (RPD) of the Fe content back-propagation (BP) network estimation model is 0.23, and the prediction ability is poor, so it is impossible to accurately predict the Fe content of samples. In the estimation model of BP network of Cu, the coefficient of determination (R²) is 0.8, the root mean squared error (RMSE) is 0.1, and the RPD is 1.79. This result indicates that the BP estimation model of Cu content has good accuracy and strong predictive ability and can accurately predict the Cu content in a sample. In summary, estimation based on LIBS improved the accuracy and efficiency of Fe and Cu content detection in water and provided new ideas and methods for the accurate estimation of Fe and Cu contents in water.

Metal Contamination Distribution Detection in High-Voltage Transmission Line Insulators by Laser-induced Breakdown Spectroscopy (LIBS)

The fast detection of classical contaminants and their distribution on high-voltage transmission line insulators is essential for ensuring the safe operation of the power grid. The analysis of existing insulator contamination has traditionally relied on taking samples during a power cut, taking the samples back to the lab and then testing them with elemental analysis equipment, especially for sugars, bird droppings, and heavy metal particulates, which cannot be analysed by the equivalent salt deposit density (ESDD) or non-soluble deposit density (NSDD) methods. In this study, a novel method called laser-induced breakdown spectroscopy (LIBS) offering the advantages of no sample preparation, being nearly nondestructive and having a fast speed was applied for the analysis of metal contamination. Several LIBS parameters (laser energy and delay time) were optimized to obtain better resolution of the spectral data. The limit of detection (LOD) of the observed elements was obtained using a calibration curve. Compared to calibration curves, multivariate analysis methods including principal component analysis (PCA), k-means and partial least squares regression (PLSR) showed their superiority in analyzing metal contamination in insulators. Then, the elemental distribution of natural pollution was predicted using LIBS to fully capture information about the bulk elements (Na, Ni, Cu, Mn, Ca, etc.) of entire areas with PLSR. The results showed that LIBS could be a promising method for accurate direct online quantification of metal contamination in insulators.

Laser-induced breakdown spectroscopy-Raman: An effective complementary approach to analyze renal-calculi

Presence of renal-calculi (kidney stones) in human urethra is being increasingly diagnosed over the last decade and is considered as one of the most painful urological disorders. Accurate analysis of such stones plays a vital role in the evaluation of urolithiasis patients and in turn helps the clinicians toward exact etiologies. Two highly complementary laser-based analytical techniques; laser-induced breakdown spectroscopy (LIBS) and micro-Raman spectroscopy have been used to identify the chemical composition of different types of renal-calculi. LIBS explores elemental characteristics while Raman spectroscopy provides molecular details of the sample. This complete information on the sample composition might help clinicians to identify the key aspects of the formation of kidney stones, hence assist in therapeutic management and to prevent recurrence. The complementarity of both techniques has been emphasized and discussed. LIBS spectra of different types of stones suggest the probable composition of it by virtue of the major, minor and trace elements detected from the sample. However, it failed to differentiate the crystalline form of different hydrates of calcium oxalate stone. This lacuna was overcome by the use of Raman spectroscopy and these results are compared with conventional chemical analysis.

Fast Detection of Copper Content in Rice by Laser-Induced Breakdown Spectroscopy with Uni- and Multivariate Analysis

Fast detection of heavy metals is very important for ensuring the quality and safety of crops. Laser-induced breakdown spectroscopy (LIBS), coupled with uni- and multivariate analysis, was applied for quantitative analysis of copper in three kinds of rice (Jiangsu rice, regular rice, and Simiao rice). For univariate analysis, three pre-processing methods were applied to reduce fluctuations, including background normalization, the internal standard method, and the standard normal variate (SNV). Linear regression models showed a strong correlation between spectral intensity and Cu content, with an R2 more than 0.97. The limit of detection (LOD) was around 5 ppm, lower than the tolerance limit of copper in foods. For multivariate analysis, partial least squares regression (PLSR) showed its advantage in extracting effective information for prediction, and its sensitivity reached 1.95 ppm, while support vector machine regression (SVMR) performed better in both calibration and prediction sets, where Rc2 and Rp2 reached 0.9979 and 0.9879, respectively. This study showed that LIBS could be considered as a constructive tool for the quantification of copper contamination in rice.

Laser-Induced Breakdown Spectroscopy for Rapid Discrimination of Heavy-Metal-Contaminated Seafood Tegillarca granosa

Tegillarca granosa samples contaminated artificially by three kinds of toxic heavy metals including zinc (Zn), cadmium (Cd), and lead (Pb) were attempted to be distinguished using laser-induced breakdown spectroscopy (LIBS) technology and pattern recognition methods in this study. The measured spectra were firstly processed by a wavelet transform algorithm (WTA), then the generated characteristic information was subsequently expressed by an information gain algorithm (IGA). As a result, 30 variables obtained were used as input variables for three classifiers: partial least square discriminant analysis (PLS-DA), support vector machine (SVM), and random forest (RF), among which the RF model exhibited the best performance, with 93.3% discrimination accuracy among those classifiers. Besides, the extracted characteristic information was used to reconstruct the original spectra by inverse WTA, and the corresponding attribution of the reconstructed spectra was then discussed. This work indicates that the healthy shellfish samples of Tegillarca granosa could be distinguished from the toxic heavy-metal-contaminated ones by pattern recognition analysis combined with LIBS technology, which only requires minimal pretreatments.

Direct Determination of Contaminants and Major and Minor Nutrients in Solid Fertilizers Using Laser-Induced Breakdown Spectroscopy (LIBS)

Contaminants (Cd, Cr, and Pb) as well as minor (B, Cu, Mn, Na, and Zn) and major (Ca and Mg) elements were directly determined in solid fertilizer samples using laser-induced breakdown spectroscopy (LIBS). Factorial designs were used to define the most appropriate LIBS parameters and pellet pressure on solid fertilizers. Emission lines for all of the analytes were collected and employed 12 signal normalization modes. The best results were obtained using a laser energy of 75 mJ, a spot size of 50 μm, a pressure of 10 t/in., and a delay of 2.0 μs. Good correlation was obtained between the calibration model's prediction using the proposed LIBS method and the reference values obtained with ICP-OES. The limits of detection (LOD) for the proposed method varied from 2 mg/kg (for Cd) to 1% (for Zn).

Time-dependent growth of crystalline Au(0)-nanoparticles in cyanobacteria as self-reproducing bioreactors: 2. Anabaena cylindrica

Microbial biosynthesis of metal nanoparticles as needed in catalysis has shown its theoretical ability as an extremely environmentally friendly production method in the last few years, even though the separation of the nanoparticles is challenging. Biosynthesis, summing up biosorption and bioreduction of diluted metal ions to zero valent metals, is especially ecofriendly, when the bioreactor itself is harmless and needs no further harmful reagents. The cyanobacterium Anabaena cylindrica (SAG 1403.2) is able to form crystalline Au(0)-nanoparticles from Au(3+) ions and does not release toxic anatoxin-a. X-ray powder diffraction (XRD), transmission electron microscopy (TEM) and laser-induced breakdown spectroscopy (LIBS) are applied to monitor the time-dependent development of gold nanoparticles for up to 40 hours. Some vegetative cells (VC) are filled with nanoparticles within minutes, while the extracellular polymeric substances (EPS) of vegetative cells and the heterocyst polysaccharide layer (HEP) are the regions, where the first nanoparticles are detected on most other cells. The uptake of gold starts immediately after incubation and within four hours the average size remains constant around 10 nm. Analyzing the TEM images with an image processing program reveals a wide distribution for the diameter of the nanoparticles at all times and in all regions of the cyanobacteria. Finally, the nanoparticle concentration in vegetative cells of Anabaena cylindrica is about 50% higher than in heterocysts (HC). These nanoparticles are found to be located along the thylakoid membranes.