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It is shown that preconditioning of experimental X‐ray computed tomography (XCT) data is critical to achieve high‐precision segmentation scores. The challenging experimental XCT datasets and deep convolutional neural networks (DCNNs) are used that are trained with low‐resemblance synthetic XCT data. The material used is a 6‐phase Al–Si metal matrix composite‐reinforced with ceramic fibers and particles. To achieve generalization, in our past studies, specific data augmentation techniques were proposed for the synthetic XCT training data. In addition, two toolsets are devised: (1) special 3D DCNN architecture (3D Triple_UNet), slicing the experimental XCT data from multiple views (MultiView Forwarding), the i.S.Sy.Da.T.A. iterative segmentation algorithm, and (2) nonlocal means (NLM) conditioning (filtering) for the experimental XCT data. This results in good segmentation Dice scores across all phases compared to more standard approaches (i.e., standard UNet architecture, single view slicing, standard single training, and NLM conditioning). Herein, the NLM filter is replaced with the deep conditioning framework BAM SynthCOND introduced in a previous publication, which can be trained with synthetic XCT data. This leads to a significant segmentation precision increase for all phases. The proposed methods are potentially applicable to other materials and imaging techniques.
The applications of composite overwrapped pressure vessels (COPVs) in extreme conditions, such as storing hydrogen gases at very high pressure, impose new requirements related to the system's integrity and safety. The development of a structural health monitoring (SHM) system that allows for continuous monitoring of the COPVs provides rich information about the structural integrity of the component. Furthermore, the collected data can be used for different purposes such as increasing the periodic inspection intervals, providing a remaining lifetime prognosis, and also ensuring optimal operating conditions. Ultimately this information can be complementary to the development of the envisioned digital twin of the monitored COPVs. Guided waves (GWs) are preferred to be used in continuous SHM given their ability to travel in complex structures for long distances. However, obtained GW signals are complex and require advanced processing techniques. Machine learning (ML) is increasingly utilized as the main part of the processing pipeline to automatically detect anomalies in the system's integrity. Hence, in this study, we are scrutinizing the potential of using ML to provide continuous monitoring of COPVs based on ultrasonic GW data. Data is collected from a network of sensors consisting of fifteen Piezoelectric (PZT) wafers that were surface mounted on the COPV. Two ML algorithms are used in the automated evaluation procedure (i) a long short-term memory (LSTM) autoencoder for anomaly detection (defects/impact), and (ii) a convolutional neural network (CNN) model for feature extraction and classification of the artificial damage sizes and locations. Additional data augmentation steps are introduced such as modification and addition of random noise to original signals to enhance the model's robustness to uncertainties. Overall, it was shown that the ML algorithms used were able to detect and classify the simulated damage with high accuracy.
Lanthanide Nanocrystals as Authentication and Security Tags and Reporters for Optical Sensors
(2023)
Counterfeiting of goods has serious economic implications worldwide and can present a considerable threat to human health and national security, rendering product authentication more and more important. This led to an increasing interest in simple tags for anticounterfeiting and authentication applications for the integration into different materials and matrices that are robust enough to survive the respective production processes and can be read out in-situ with simple instrumentation.
Here we present a platform of chemically inert, multi-color luminescent lanthanide nanocrystals (LnNC) consisting of a NaYF4 matrix doped with Yb, Er, Tm, or Ho as promising candidates for such applications.
LnNC with characteristic multi-color emission patterns, composed of narrow bands of varying intensity in the ultraviolet, visible, near-infrared, and short-wave were synthesized via a thermal decomposition approach under inert conditions[1]. The tailor-made design of different emission pattern was achieved by tuning particle size, material composition, and particle surface chemistry in upscaleable syntheses.[2] Particle size and morphology were determined by dynamic light scattering and transmission electron microscopy and the optical properties were characterized by absorption and fluorescence spectroscopy as well as fluorescence lifetime measurements.
The particles were then incorporated into security inks and consumer products. In addition, microenvironment-specific changes in the emission properties of the LnNC tag were utilized to study and sense changes of physico-chemical parameters in different matrices.
Counterfeiting of goods has serious economic implications worldwide and can be a threat to human health and safety. Upconversion nanoparticles (UCNPs)are ideal candidates for optical encoding of various materials. Particles with tailor-made emission patterns can be used as anti-counterfeiting markers and luminophores in security inks and to track material flows. Microenvironment-specific changes in the emission properties of the UCNP tags can be used to study, signal, and sense changes of physico-chemical parameters in different environments, e.g., building
materials.
Demonstrating competency and equivalence for the measurement capacity of contaminants and nutrients in primary foodstuffs is a priority of the OAWG 10-year strategy for Track A core comparisons. Such measurements have posed significant challenges for reference material producers and calibration service providers. This key comparison (KC), under the topic of “non- polar analyte in high carbohydrate food matrix: trans-Zearalenone (trans-ZEN) in maize powder” , was a sector of the model system selected to align with this class within the OAWG strategy. Evidence of successful participation in formal, relevant international comparisons is needed to demonstrate the Calibration and Measurement Capabilities (CMCs) of national metrology institutes (NMIs) and designated institutes (DIs).
17 NMIs and DIs participated in the Track A KC CCQM- 168 “non-polar analyte in high carbohydrate food matrix: trans-ZEN in maize powder” . Participants were requested to evaluate the mass fraction (μg/kg) of trans-ZEN in maize powder material. Methods like liquid-liquid extraction and SPE were applied in the pre-treatment, and HPLC-MS/MS and HPLC-FLD were used for detection by the participants. The mass fractions for trans-ZEN were in the range of (91.8 to 169) μg/kg with standard uncertainties of (1.5 to 24.7) μg/kg, and corresponding relative standard uncertainties from 1.5% to 14.6%. Two labs, INTI and BAM were excluded from the KCRV evaluation. INTI result was identified as an outlier and confirmed their method had insufficient specificity. For BAM the calibration approach they used does not meet the CIPM traceability requirements. The other 15 labs included in the calculation of the consensus KCRV all agreed within their standard uncertainties. Hierarchical Bayes was used as estimators in calculating KCRV and standard uncertainty.
Successful participation in CCQM-K168 demonstrates the measurement capabilities in determining mass fraction of organic compounds, with molecular mass of 100 g/mol to 500 g/mol, having low polarity pKow < -2, in mass fraction range from 1 μg/kg to 1000 μg/kg in a high carbohydrate food matrix.
Trace element (TE) analysis of pyrite via LA-ICP-MS can be used to reconstruct the conditions of pyrite formation in complex mineral systems. The Carpentaria province in northern Australia is host to some of the world’s highest value Zn-Pb (+Ag, Cu) deposits. The genesis of many of these deposits is controversial, with competing models of single-vs. multi-stage ore formation. In this study, LA-ICP-MS data of paragenetically constrained pyrite from the George Fisher Zn-Pb-Ag deposit has been analysed to investigate the chemistry of different stages of ore formation. Pyrite from correlative unmineralized host rocks has also been analysed to investigate evidence of distal hydrothermal anomalism. All LA-ICP-MS data have been statistically evaluated (principal component analysis) and interpreted together with whole rock lithogeochemical data of the same samples. Pre-ore diagenetic pyrite is compositionally similar to other Proterozoic diagenetic pyrite, with some evidence of minor hydrothermal anomalism that with further analysis could help define distal alteration. Pyrite from the different ore stages are compositionally distinct, consistent with a multi-stage system. Ore stage 1 pyrite exceeds background contents of Co, Cu, Zn, As, Ag, Sb, Tl, and Pb and has elevated Co/Ni ratios, whereas only Ni and Co are above background abundances in ore stage 2 and 3 pyrite, of which only ore stage 3 pyrite has high Co/Ni ratios. Ore stage 1 pyrite has a similar composition to hydrothermal pyrite in the undeformed northern Carpentaria CD-type deposits and was likely syn-diagenesis. Ore stage 2 was syn-deformation, and resulted in replacement and recrystallization of pre-existing pyrite, and the expulsion of incompatible TEs. Ore stage 3 formed via a later Cu-Zn-Pb mineralizing event that resulted in a new geochemically distinct generation of Co-rich pyrite. Overall, this study demonstrates the value of paragenetically-constrained pyrite TE data for refining genetic models in complex sediment hosted mineral systems.
AbstractThe minor and trace element composition of minerals provides critical insights into a variety of geological processes. Multi-element mapping by laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) is an important technique applied for this purpose and although the method is rapidly advancing, there remains a fundamental compromise between spatial resolution, detection limit, and experiment duration when using sequential mass analyzers. To address the limitation of limited analyte selection for high spatial resolution maps imposed by the sequential nature of typical quadrupole (Q)-ICP-MS, we tested the Aerosol Rapid Introduction System (ARIS) for repeat mapping of the same area. The ARIS is a high-speed transfer tubing system that reduces aerosol washout times, permitting resolution of individual pulses at 40–60 Hz. Here, the ARIS was tested not for pulse resolution but with novel operating conditions optimized to perform fast, high spatial resolution mapping of minor and trace element distribution in pyrite and marcasite. For this purpose, ablation was conducted with a 5 µm beam aperture, a repetition rate of 50 Hz, and a continuous stage scan speed of 40 µm s−1. For each LA-Q-ICP-MS map, data were acquired for six elements with an acquisition time of 20 ms per element. This deliberately reduced the individual pulse resolution of the ARIS but instead exploited the spatial resolution and sensitivity gains afforded by the high-laser repetition rate combined with efficient aerosol transfer. The new method successfully mapped trace elements at single to double-digit parts per million levels, and the maps reveal fine-scale zoning of trace elements with an effective x and y resolution of 5 µm, while white light interferometry showed that for each experiment, only ca. 1 µm of the sample was removed. Repeated mapping of the same area showed excellent correspondence not only between element concentrations in successive experiments but also in the shape, dimension, and location of regions of interest defined by concentration criteria. The very good repeatability of the elemental maps indicates that for studies requiring more analytes, successive mapping of additional elements is possible. By contrast with conventional very small spot (i.e., 5 µm) analysis, fast repetition rate and stage scan speed mapping avoids down-hole fractionation effects and minimizes accidental analysis of buried invisible inclusions. Compared to conventional LA-ICP-MS mapping, the method reduces the experiment time by 4–8 times.
Molten sulfur is found in various subaerial volcanoes. However, limited records of the pools and flows of molten sulfur have been reported: therefore, questions remain regarding the physicochemical processes behind this phenomenon. A suite of new sulfur flows, some of which active, was identified at the Lastarria volcano (northern Chile) and studied using satellite imagery, in situ probing, and temperature and video recording. This finding provides a unique opportunity to better understand the emplacement mechanisms and mineral and chemical compositions of molten sulfur, in addition to gaining insight into its origin. Molten sulfur presented temperatures of 124–158°C, with the most prolonged sulfur flow reaching 12 m from the source. Photogrammetric tools permitted the identification of levees and channel structures, with an estimated average flow speed of 0.069 m/s. Field measurements yielded a total volume of 1.45 ± 0.29 m3 of sulfur (equivalent to ∼2.07 tons) mobilized during the January 2019 event for at least 408 min. Solidified sulfur was composed of native sulfur with minor galena and arsenic- and iodine-bearing minerals. Trace element analysis indicated substantial enrichment of Bi, Sb, Sn, Cd, as well as a very high concentration of As (&gt;40.000 ppm). The January 2019 molten sulfur manifestations in Lastarria appear to be more enriched in As compared to the worldwide known volcanoes with molten sulfur records, such as the Shiretoko-Iozan and Poás volcanoes. Furthermore, their rheological properties suggest that the “time of activity” in events such as this could be underestimated as flows in Lastarria have moved significantly slower than previously thought. The origin of molten sulfur is ascribed to the favorable S-rich chemistry of fumarolic gases and changes in host rock permeability (fracture opening). Molten sulfur in Lastarria correlates with a peak in activity characterized by high emissions of SO2 and other acid species, such as HF and HCl, in addition to ground deformation. Consequently, molten sulfur was framed within a period of volcanic unrest in Lastarria, triggered by changes in the magmatic-hydrothermal system. The appearance of molten sulfur is related to physicochemical perturbations inside the volcanic system and is perhaps a precursor of eruptive activity, as observed in the Poás and Turrialba volcanoes.
Use of the solvent-free mechanochemical method for a sustainable preparation of pharmaceuticals
(2023)
With the growing interest in environmental issues on the part of governments and institutions, pharmaceutical industries are asked to reduce their environmental footprint. Given the major impact related to the use of solvents, the development of methodologies less solvent demanding is nowadays even more urgent. In light of that, mechanochemistry would be a suitable solvent-free technology since it promotes the activation of the chemical reactions between (generally) solid materials via inputs of mechanical energy. Since such reactions may occur outside the kinetic and thermodynamic rules of conventional solution chemistry, the main limit of mechanochemistry is the poor mechanistic understanding of the solid-state transformations involved, which is still hindering a widespread use of the method, as well a scale-up to the industrial level. However, the development of methods for real-time monitoring of the mechanochemical reactions enables obtaining (in)accessible information on reaction intermediates, new products, or reaction time, thus getting closer to a better understanding of the mechanistic behaviour. With the rules of this chemistry becoming increasingly clear, the new reaction pathways of mechanochemistry wouldn’t represent a limit anymore, but an asset, that may lead to lot of opportunities for the pharmaceutical industry.
Thermal Destruction of PFAS
(2023)
Thermal treatment processes are currently the only full-scale option for thedestruction of per- and polyfluoroalkyl substances (PFAS) in large waste streams. While all organic molecules including PFAS are susceptible to thermal destruction, their decomposition rates are controlled by process variables such as temperature, reaction atmosphere, and residence time. Concerns exist about the formation of products of incomplete destruction and their emission from hazardous waste incinerators. This talk will summarize the current stateof-the-art of thermal PFAS destruction, identify research needs, and showcase future research designed to address critical knowledge gaps.