Accurate Quantitative Analysis Research Articles

Automatic reorientation to generate short-axis myocardial PET images

BackgroundAccurately redirecting reconstructed Positron emission tomography (PET) images into short-axis (SA) images shows great significance for subsequent clinical diagnosis. We developed a system for automatic redirection and quantitative analysis of myocardial PET images.MethodsA total of 128 patients were enrolled for 18 F-FDG PET/CT myocardial metabolic images (MMIs), including 3 image classifications: without defects, with defects, and excess uptake. The automatic reorientation system includes five modules: regional division, myocardial segmentation, ellipsoid fitting, image rotation and quantitative analysis. First, the left ventricular geometry-based canny edge detection (LVG-CED) was developed and compared with the other 5 common region segmentation algorithms, the optimized partitioning was determined based on partition success rate. Then, 9 myocardial segmentation methods and 4 ellipsoid fitting methods were combined to derive 36 cross combinations for diagnostic performance in terms of Pearson correlation coefficient (PCC), Kendall correlation coefficient (KCC), Spearman correlation coefficient (SCC), and determination coefficient. Finally, the deflection angles were computed by ellipsoid fitting and the SA images were derived by affine transformation. Furthermore, the polar maps were used for quantitative analysis of SA images, and the redirection effects of 3 different image classifications were analyzed using correlation coefficients.ResultsOn the dataset, LVG-CED outperformed other methods in the regional division module with a 100% success rate. In 36 cross combinations, PSO-FCM and LLS-SVD performed the best in terms of correlation coefficient. The linear results indicate that our algorithm (LVG-CED, PSO-FCM, and LLS-SVD) has good consistency with the reference manual method. In quantitative analysis, the similarities between our method and the reference manual method were higher than 96% at 17 segments. Moreover, our method demonstrated excellent performance in all 3 image classifications.ConclusionOur algorithm system could realize accurate automatic reorientation and quantitative analysis of PET MMIs, which is also effective for images suffering from interference.

Research on batch multielement rapid quantitative analysis based on the standard curve-assisted calibration-free laser-induced breakdown spectroscopy method

This study proposes a batch rapid quantitative analysis method for multiple elements by combining the advantages of standard curve (SC) and calibration-free laser-induced breakdown spectroscopy (CF-LIBS) technology to achieve synchronous, rapid, and accurate measurement of elements in a large number of samples, namely, SC-assisted CF-LIBS. Al alloy standard samples, divided into calibration and test samples, were applied to validate the proposed method. SC was built based on the characteristic line of Pb and Cr in the calibration sample, and the contents of Pb and Cr in the test sample were calculated with relative errors of 6% and 4%, respectively. SC built using Cr with multiple characteristic lines yielded better calculation results. The relative contents of ten elements in the test sample were calculated using CF-LIBS. Subsequently, the SC-assisted CF-LIBS was executed, with the majority of the calculation relative errors falling within the range of 2%–5%. Finally, the Al and Na contents of the Al alloy were predicted. The results demonstrate that it effectively enables the rapid and accurate quantitative analysis of multiple elements after a single-element SC analysis of the tested samples. Furthermore, this quantitative analysis method was successfully applied to soil and Astragalus samples, realizing an accurate calculation of the contents of multiple elements. Thus, it is important to advance the LIBS quantitative analysis and its related applications.

Prior image-based generative adversarial learning for multi-material decomposition in photon counting computed tomography

BackgroundPhoton counting detector computed tomography (PCD-CT) is a novel promising technique providing higher spatial resolution, lower radiation dose and greater energy spectrum differentiation, which create more possibilities to improve image quality. Multi-material decomposition is an attractive application for PCD-CT to identify complicated materials and provide accurate quantitative analysis. However, limited by the finite photon counting rate in each energy window of photon counting detector, the noise problem hinders the decomposition of high-quality basis material images. MethodsTo address this issue, an end-to-end multi-material decomposition network based on prior images is proposed in this paper. First, the reconstructed images corresponding to the full spectrum with less noise are introduced as prior information to improve the overall signal-to-noise ratio of the data. Then, a generative adversarial network is designed to mine the relationship between reconstructed images and basis material images based on the information interaction of material decomposition. Furthermore, a weighted edge loss is introduced to adapt to the structural differences of different basis material images. ResultsTo verify the performance of the proposed method, simulation and real studies are carried out. In simulation study of structured fibro-glandular tissue model, the results show that the proposed method decreased the root mean square error by 67 % and 26 % on adipose, 66 % and 28 % on fibroglandular, 52 % and 8 % on calcification, compared to butterfly network and dual interactive Wasserstein generative adversarial network. ConclusionExperimentally, the proposed method shows certain advantages over other methods on noise suppression effect, detail retention ability and decomposition accuracy.

The stray light induced redshift of absorption peaks of inorganic anions in the far ultraviolet region-an "artifact".

In the analytical process of spectrophotometry, the prerequisite for accurate qualitative and quantitative analysis is obtaining the intrinsic spectra of the analyte. However, the intrinsic properties of spectra can sometimes be masked by easily overlooked non-intrinsic factors, such as those from measuring instruments, leading to erroneous spectral identification. In this study, we documented an unusual redshift phenomenon in the far ultraviolet spectral region. With a spectrophotometer under the nitrogen atmosphere, we selected 14 representative inorganic anions and investigated their absorption spectral behaviors at different optical pathlengths and concentrations. It was intriguing to observe that the absorption peaks with maximum absorption wavelengths below a watershed wavelength of 200 nm underwent a redshift as pathlength and concentration increased, while those above 200 nm did not exhibit a significant redshift phenomenon. In-depth formula simulations and experimental verifications demonstrated that this peculiar spectral behavior was caused by unavoidable stray light in the spectrophotometer. Some methodological and instrumental recommendations are given in the paper. Our study results may serve as a reminder to carefully identify non-intrinsic phenomena when studying absorption spectra in the far ultraviolet region, and provide guidance on spectral corrections in scientific research and practical applications.

Application of S-transform-based nonlinear processing for accurate LIBS quantitative analysis of iron ore slurry.

Real-time Fe content monitoring in iron ore slurry is crucial for evaluating concentrate quality and enhancing mineral processing efficiency. Laser-induced breakdown spectroscopy (LIBS) is a promising technique for the online monitoring of elemental content at industrial sites. However, LIBS measurements are hampered by the matrix effect and the self-absorption effect, limiting the precision of linear analytical processes. To overcome this, we propose to introduce a nonlinear processing unit based on the S-transform to incorporate nonlinearity into the data analysis process. This approach integrates a feature selection unit based on the spectral distance variable selection method (SDVS), a nonlinear processing unit based on the S-transform (ST), and a partial least squares regression model (PLS). To demonstrate the improvement in accuracy achieved through nonlinear processing, a comparative analysis involving five models, Raw-PLS, SDVS-PLS, ST-PLS, SDVS-ANN, and SDVS-ST-PLS, is conducted. The results reveal a significant improvement in the performance of the SDVS-ST-PLS model, effectively facilitating the successful application of the LIBSlurry analyzer to the mineral flotation process.

Data efficient contrastive learning in histopathology using active sampling

Deep learning (DL) based diagnostics systems can provide accurate and robust quantitative analysis in digital pathology. These algorithms require large amounts of annotated training data which is impractical in pathology due to the high resolution of histopathological images. Hence, self-supervised methods have been proposed to learn features using ad-hoc pretext tasks. The self-supervised training process uses a large unlabeled dataset which makes the learning process time consuming. In this work, we propose a new method for actively sampling informative members from the training set using a small proxy network, decreasing sample requirement by 93% and training time by 62% while maintaining the same performance of the traditional self-supervised learning method. The code is available on github.

Dynamic spectral extraction method with approximately the same optical path length of non-invasive quantitative analysis of human blood components

The non-invasive quantitative analysis of blood components using spectroscopic methods based on Beer-Lambert law, and dynamic spectroscopic theory needs to meet the four applicable conditions of this law. However, in practical applications, due to the scattering characteristics of human tissue, it is difficult to meet the two conditions (parallel monochromatic light and non-scattering homogeneous system of absorbing substances), which will reduce the accuracy of quantitative analysis. To reduce the principle error caused by scattering, this paper proposes an extraction method for dynamic spectra with approximately the same optical path length. This method extracts approximately the same absorbance values from photoplethysmographic (PPG) spectral signals. A smaller absorbance value corresponds to a thinner blood layer, in which light can be approximated as parallel light, thus maximizing the satisfaction of the conditions of Beer-Lambert law. In the experimental, compared with the traditional single-edge extraction method, the proposed method has higher prediction accuracy. The correlation coefficients of the total cholesterol and triglycerides increased by 31.22% and 38.05%, and the root mean square errors decreased by 29.63% and 37.14%. The results show that the methods can significantly enhance the robustness of non-invasive quantitative analysis of blood components based on dynamic spectral theory.

Quantitative determination of oxides in cement raw meal based on near-infrared spectroscopy and hybrid feature selection strategy

Determining the content of components in cement raw meal is vital in Portland cement manufacturing. Near-infrared spectroscopy (NIRS)enables rapid, safe, and accurate quantitative analysis of the primary constituents of cement raw meal (CaO, SiO2, Al2O3, and Fe2O3), thereby assisting the cement industry in reducing the time delay in raw meal proportioning control and enhancing the quality of the raw meal. This study employed a hybrid feature selection strategy to identify effective feature points in the NIRS of raw meal samples. Effective feature points from the raw meal were integrated with partial least squares (PLS) to develop a regression model. Initially, NIRS data from raw meal samples were obtained using a Fourier NIR spectrometer. Subsequently, sample set partitioning based on joint X-Y distance (SPXY) was utilized to divide the samples into a calibration set (70 samples) and a validation set (22 samples). Next, A backward interval partial least squares (BiPLS) approach, along with various preprocessing algorithms, was then employed to initially screen the optimal waveband combinations of the NIRS. Within these combinations, effective feature points were further identified using competitive adaptive reweighted sampling (CARS), variable combined population analysis (VCPA) and weighted fusion variable space shrinkage approach (WFVSSA). Finally, we developed and compared PLS models based on the full spectrum and three hybrid feature selection methods. The results demonstrated that.WFVSSA offers a comprehensive assessment of wavelength importance, effectively preserving feature points while eliminating redundant information. BiPLS-WFVSSA-PLS outperformed BiPLS-CARS-PLS and BiPLS-VCPAPLS on the validation set, with R2, RMSEP, and RPD values of 0.8676, 0.1431, and 2.8129 for CaO; 0.9212, 0.1216, and 3.6461 for SiO2; 0.9198, 0.0607, and 3.5618 for Al2O3; and 0.8717, 0.0166, and 2.8614 for Fe2O3, respectively. These findings underscore the superior accuracy and stability of BiPLS-WFVSSA-PLS in determining cement raw meal component contents.

Determination and study of correlations of relative sensitivity factors in metal and metal oxide samples by glow discharge mass spectrometry

The determination of relative sensitivity factor (RSF) plays a crucial role in achieving accurate quantitative analysis in glow discharge mass spectrometry (GD-MS). In this study, the RSF of typical elements in 14 metal and metal oxide matrices were determined, and their correlation with discharge parameters, matrix effects, the first ionization energies and sample shapes were investigated. It was found that discharge gas flow exerted a minor influence on RSF values in a nickel (Ni) metal sample, whereas discharge current significantly affected RSF, with variations up to 57.00 % for cobalt (59Co). The results revealed that matrix effects were more pronounced in metal oxides, with the relative standard deviation (RSD) of RSF for typical elements ranging from 25.17 % to 101.04 %. RSF values for the same element across different metal oxides showed a correlation with lattice binding energy (U0). Particularly, RSF values for 12 elements including 23Na, 27Al, 28Si, 45Sc, 52Cr, 56Fe, 59Co, 88Sr, 130Te, 138Ba, 139La and 140Ce within 7 metal oxides showed a significant correlation with U0 of the metal oxides. In terms of sample shape, pin samples generally yielded higher RSF values than flat samples in both metals and oxides. RSF accuracy was confirmed with standard samples of zirconium (Zr 73 M R10002 and Zr IARM 705–18) and iron oxides (Fe2O3-1 and Fe2O3-2) with relative errors within 17.50 % except for 12C. Subsequently, these RSF values were effectively utilized for the quantitative analysis of samples collected from the China Space Station (CSS). This comprehensive analysis elucidates the multifaceted influences on RSF values, enhancing the precision of trace element quantification in GD-MS.

Establishment of collection methods and laboratory testing methods for active pharmaceutical ingredient in the workplace air

Objective: To establish collection methods and laboratory testing methods for qualitative and quantitative analysis of 9 typical active pharmaceutical ingredient in the workplace air. Methods: In December 2021, a mixed solution of nine analytes was prepared and then dispersed in aerosol state to simulate sampling. Glass fiber filter membrane was selected as air collector and collected active pharmaceutical ingredient in the air at a rate of 2.0 L/min for 15 minutes. Then, the obtained filter membrane samples were eluted with 25%ACN/75%MeOH. Finally, the eluent was qualitatively and quantitatively analyzed with liquid chromatography-triple quadrupole mass spectrometer. Results: This method could effectively collect active pharmaceutical ingredient in the air, with an average sampling efficiency of more than 98.5%. The linear correlation coefficient r was greater than 0.9990. The lower limit of quantification for each analyte ranged from 0.6~500.0 ng/ml, and the average recovery rate ranged from 97.6%~102.5%. Conclusion: This method could simultaneously collect 9 active pharmaceutical ingredient in the workplace air, and could provide accurate qualitative and quantitative analysis in subsequent laboratory tests.

Separation, identification, and fingerprinting of antioxidant components in persimmon (Diospyros kaki) leaves by offline two-dimensional liquid chromatography with electrochemical detection and tandem mass spectrometry.

In this work, the antioxidant components in persimmon (Diospyros kaki) leaves were separated by offline two-dimensional liquid chromatography-electrochemical detection (LC×LC-ECD) and identified by LC-tandem mass spectrometry (LC-MS/MS). A total of 33 antioxidants, mainly proanthocyanidins, and glycosides of kaempferol and quercetin, were identified. The antioxidant assays demonstrated that the fractions collected from the first-dimension LC (1D-LC) possessed considerable radical scavenging capabilities, with correlation coefficients of peak area versus radical scavenging capability of 1,1-diphenyl-2-picrylhydrazyl and 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) being 0.9335 and 0.9116, respectively. The fingerprinting showed that 37 peaks were present in all samples. The major antioxidant components of persimmon leaves were the glycosides of kaempferol and quercetin. Finally, fourteen antioxidants were quantitatively assessed. Offline LC×LC provided high peak capacity and separation; ECD enabled specific screening and detection of antioxidant components; and MS/MS provided excellent identification capability. In this study, the combination of the three approaches was utilized to screen for antioxidant components in persimmon leaves, with satisfactory findings. In conclusion, this technique is an effective means for rapid analysis of antioxidant components and quality control of medicinal plants, achieving rapid separation of congeners and facilitating more accurate qualitative and quantitative analyses.

Construction of a mitochondria-targeted probe to monitor cysteine levels in cancer cells and zebrafish.

Cysteine (Cys) plays an indispensable role as an antioxidant in the maintenance of bioredox homeostasis. We have constructed an efficient fluorescent probe Mito-Cys based on the binding of indole and naphthol. The acrylic ester group serves as a recognition switch for specific detection of Cys, which undergoes Michael addition and intramolecular cyclization reactions, thereby ensuring the chemical kinetics priority of Cys compared to other biothiols. The probe has good water solubility, large Stokes shift (137nm), with a detection limit of 21.81nM. In addition, cell imaging experiments have shown that the probe has excellent mitochondrial targeting ability (R = 0.902). The probe can distinguish between Cys, homocysteine (Hcy) and glutathione (GSH), and can detect Cys specifically and quickly (100s) to ensure accurate quantitative analysis of Cys changes in cells. More importantly, the probe confirms that ferroptosis inducing factors trigger thiol starvation in mitochondria, which helps to gain a deeper understanding of the physiological and pathological functions related to Cys and ferroptosis.

Atomic-Scale Distribution and Evolution of Strain in Pt Nanoparticles Grown on MoS2 Nanosheet.

Interface strain significantly affects the band structure and electronic states of metal-nanocrystal-2D-semiconductor heterostructures, impacting system performance. While transmission electron microscopy (TEM) is a powerful tool for studying interface strain, its accuracy may be compromised by sample overlap in high-resolution images due to the unique nature of the metal-nanocrystals-2D-semiconductors heterostructure. Utilizing digital dark-field technology, the substrate influence on metal atomic column contrasts is eliminated, improving the accuracy of quantitative analysis in high-resolution TEM images. Applying this method to investigate Pt on MoS2 surfaces reveals that the heterostructure introduces a tensile strain of ≈3% in Pt nanocrystal. The x-directional linear strain in Pt nanocrystals has a periodic distribution that matches the semi-coherent interface between Pt nanocrystals and MoS2, while the remaining strain components localize mainly on edge atomic steps. These results demonstrate an accurate and efficient method for studying interface strain and provide a theoretical foundation for precise heterostructure fabrication.

Adipose Tissue Segmentation after Lung Slice Localization in Chest CT Images Based on ConvBiGRU and Multi-Module UNet.

The distribution of adipose tissue in the lungs is intricately linked to a variety of lung diseases, including asthma, chronic obstructive pulmonary disease (COPD), and lung cancer. Accurate detection and quantitative analysis of subcutaneous and visceral adipose tissue surrounding the lungs are essential for effectively diagnosing and managing these diseases. However, there remains a noticeable scarcity of studies focusing on adipose tissue within the lungs on a global scale. Thus, this paper introduces a ConvBiGRU model for localizing lung slices and a multi-module UNet-based model for segmenting subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT), contributing to the analysis of lung adipose tissue and the auxiliary diagnosis of lung diseases. In this study, we propose a bidirectional gated recurrent unit (BiGRU) structure for precise lung slice localization and a modified multi-module UNet model for accurate SAT and VAT segmentations, incorporating an additive weight penalty term for model refinement. For segmentation, we integrate attention, competition, and multi-resolution mechanisms within the UNet architecture to optimize performance and conduct a comparative analysis of its impact on SAT and VAT. The proposed model achieves satisfactory results across multiple performance metrics, including the Dice Score (92.0% for SAT and 82.7% for VAT), F1 Score (82.2% for SAT and 78.8% for VAT), Precision (96.7% for SAT and 78.9% for VAT), and Recall (75.8% for SAT and 79.1% for VAT). Overall, the proposed localization and segmentation framework exhibits high accuracy and reliability, validating its potential application in computer-aided diagnosis (CAD) for medical tasks in this domain.

Automatic segmentation of dura for quantitative analysis of lumbar stenosis: A deep learning study with 518 CT myelograms.

The diagnosis of lumbar spinal stenosis (LSS) can be challenging because radicular pain is not often present in the culprit-level localization. Accurate segmentation and quantitative analysis of the lumbar dura on radiographic images are key to the accurate differential diagnosis of LSS. The aim of this study is to develop an automatic dura-contouring tool for radiographic quantification on computed tomography myelogram (CTM) for patients with LSS. A total of 518 CTM cases with or without lumbar stenosis were included in this study. A deep learning (DL) segmentation algorithm 3-dimensional (3D) U-Net was deployed. A total of 210 labeled cases were used to develop the dura-contouring tool, with the ratio of the training, independent testing, and external validation datasets being 150:30:30. The Dice score (DCS) was the primary measure to evaluate the segmentation performance of the 3D U-Net, which was subsequently developed as the dura-contouring tool to segment another unlabeled 308 CTM cases with LSS. Automatic masks of 446 slices on the stenotic levels were then meticulously reviewed and revised by human experts, and the cross-sectional area (CSA) of the dura was compared. The mean DCS of the 3D U-Net were 0.905±0.080, 0.933±0.018, and 0.928±0.034 in the five-fold cross-validation, the independent testing, and the external validation datasets, respectively. The segmentation performance of the dura-contouring tool was also comparable to that of the second observer (the human expert). With the dura-contouring tool, only 59.0% (263/446) of the automatic masks of the stenotic slices needed to be revised. In the revised cases, there were no significant differences in the dura CSA between automatic masks and corresponding revised masks (p=0.652). Additionally, a strong correlation of dura CSA was found between the automatic masks and corresponding revised masks (r=0.805). A dura-contouring tool was developed that could automatically segment the dural sac on CTM, and it demonstrated high accuracy and generalization ability. Additionally, the dura-contouring tool has the potential to be applied in patients with LSS because it facilitates the quantification of the dural CSA on stenotic slices.

A novel method for quantitative determination of multiple substances using Raman spectroscopy combined with CWT

The identification of mixed solutions is a challenging and important subject in chemical analysis. In this paper, we propose a novel workflow that enables rapid qualitative and quantitative detection of mixed solutions. We use a methanol-ethanol mixed solution as an example to demonstrate the superiority of this workflow. The workflow includes the following steps: (1) converting Raman spectra into Raman images through CWT; (2) using MobileNetV3 as the backbone network, improved multi-label and multi-channel synchronization enables simultaneous prediction of multiple mixture concentrations; and (3) using transfer learning and multi-stage training strategies for training to achieve accurate quantitative analysis. We compare six traditional machine learning algorithms and two deep learning models to evaluate the performance of our new method. The experimental results show that our model has achieved good prediction results when predicting the concentration of methanol and ethanol, and the coefficient of determination R2 is greater than 0.999. At different concentrations, both MAPE and RSD outperform other models, which demonstrates that our workflow has outstanding analytical capabilities. Importantly, we have solved the problem that current quantitative analysis algorithms for Raman spectroscopy are almost unable to accurately predict the concentration of multiple substances simultaneously. In conclusion, it is foreseeable that this non-destructive, automated, and highly accurate workflow can further advance Raman spectroscopy.

Program VUE: analysing distributions of cryo-EM projections using uniform spherical grids

Three-dimensional cryo electron microscopy reconstructions are obtained by extracting information from a large number of projections of the object. These projections correspond to different `views' or `orientations', i.e. directions in which these projections show the reconstructed object. Uneven distribution of these views and the presence of dominating preferred orientations may distort the reconstructed spatial images. This work describes the program VUE (views on uniform grids for cryo electron microscopy), designed to study such distributions. Its algorithms, based on uniform virtual grids on a sphere, allow an easy calculation and accurate quantitative analysis of the frequency distribution of the views. The key computational element is the Lambert azimuthal equal-area projection of a spherical uniform grid onto a disc. This projection keeps the surface area constant and represents the frequency distribution with no visual bias. Since it has multiple tunable parameters, the program is easily adaptable to individual needs, and to the features of a particular project or of the figure to be produced. It can help identify problems related to an uneven distribution of views. Optionally, it can modify the list of projections, distributing the views more uniformly. The program can also be used as a teaching tool.

Accurate method based on data filtering for quantitative multi-element analysis of soils using CF-LIBS

To obtain more stable spectral data for accurate quantitative analysis of multi-element, especially for the large-area in-situ elements detection of soils, we propose a method for a multi-element quantitative analysis of soils using calibration-free laser-induced breakdown spectroscopy (CF-LIBS) based on data filtering. In this study, we analyze a standard soil sample doped with two heavy metal elements, Cu and Cd, with a specific focus on the line of Cu I 324.75 nm for filtering the experimental data of multiple sample sets. Pre- and post- data filtering, the relative standard deviation for Cu decreased from 30% to 10%, The limits of detection (LOD) values for Cu and Cd decreased by 5% and 4%, respectively. Through CF-LIBS, a quantitative analysis was conducted to determine the relative content of elements in soils. Using Cu as a reference, the concentration of Cd was accurately calculated. The results show that post- data filtering, the average relative error of the Cd decreases from 11% to 5%, indicating the effectiveness of data filtering in improving the accuracy of quantitative analysis. Moreover, the content of Si, Fe and other elements can be accurately calculated using this method. To further correct the calculation, the results for Cd was used to provide a more precise calculation. This approach is of great importance for the large-area in-situ heavy metals and trace elements detection in soil, as well as for rapid and accurate quantitative analysis.

Portable T-2 toxin biosensor based on target-responsive DNA hydrogel using water column height as readout

T-2 toxin, a hazardous mycotoxin often present in cereals and products based on cereals, poses a substantial risk to humans and animals due to its high toxicity. The development of uncomplicated, quick and highly sensitive methods for detecting T-2 toxin is imperative. In this work, a portable sensing system was constructed using water column height as a readout device in combination with a controlled release system, which allows for an accurate quantitative analysis of T-2 toxin without the need for expensive instrumentation or skilled technicians. Hyaluronic acid (HA) hydrogel was constructed by double cross-linked DNA/aptamer hybrids with polyethyleneimine (PEI) and embedded with platinum nanoparticles (Pt NPs). The aptamer specifically bound to T-2 toxin in its presence, resulting in the disruption of the hydrogel and subsequent release of the Pt NPs. These Pt NPs were later mixed with a solution of H2O2 in a confined reaction flask, leading to the decomposition of H2O2 into O2. A glass capillary tube containing a column of red water had been inserted into the cap of the reaction flask, and the low solubility of O2 led to an increase in pressure within the reaction unit, causing the red water column to rise. There is a good linear correlation between the height of the capillary liquid level and the T-2 toxin concentration in the range of 20 ng/mL to 6 μg/mL. The system has been successfully used to detect T-2 toxin in samples of barley tea and corn.

Shock compression experiments using the DiPOLE 100-X laser on the high energy density instrument at the European x-ray free electron laser: Quantitative structural analysis of liquid Sn

X-ray free electron laser (XFEL) sources coupled to high-power laser systems offer an avenue to study the structural dynamics of materials at extreme pressures and temperatures. The recent commissioning of the DiPOLE 100-X laser on the high energy density (HED) instrument at the European XFEL represents the state-of-the-art in combining x-ray diffraction with laser compression, allowing for compressed materials to be probed in unprecedented detail. Here, we report quantitative structural measurements of molten Sn compressed to 85(5) GPa and ∼3500 K. The capabilities of the HED instrument enable liquid density measurements with an uncertainty of ∼1% at conditions which are extremely challenging to reach via static compression methods. We discuss best practices for conducting liquid diffraction dynamic compression experiments and the necessary intensity corrections which allow for accurate quantitative analysis. We also provide a polyimide ablation pressure vs input laser energy for the DiPOLE 100-X drive laser which will serve future users of the HED instrument.