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Karafiludis, Stephanos ; Kochovski, Z. ; Scoppola, E. ; Retzmann, Anika ; Hodoroaba, Vasile-Dan ; ten Elshof, J. E. ; Emmerling, Franziska ; Stawski, Tomasz Maciej
Here, we elucidate nonclassical multistep crystallization pathways of transition metal phosphates from aqueous solutions. We followed precipitation processes of M-struvites, NH4MPO4·6H2O, and M-phosphate octahydrates, M3(PO4)2·8H2O, where M = Ni, Co, or NixCo1–x, by using in situ scattering and spectroscopy-based techniques, supported by elemental mass spectrometry analyses and advanced electron microscopy. Ni and Co phosphates crystallize via intermediate colloidal amorphous nanophases, which change their complex structures while agglomerating, condensing, and densifying throughout the extended reaction times. We reconstructed the three-dimensional morphology of these precursors by employing cryo-electron tomography (cryo-ET). We found that the complex interplay between metastable amorphous colloids and protocrystalline units determines the reaction pathways. Ultimately, the same crystalline structure, such as struvite, is formed. However, the multistep process stages vary in complexity and can last from a few minutes to several hours depending on the selected transition metal(s), their concentration, and the Ni/Co ratio.
Surface functionalized silica nanoparticles (SiO2-NP) gained great interest in the life and material sciences, as they can be used e.g. as drug carriers, fluorescent sensors, and multimodal labels in bioanalytical assays and imaging applications. They are highly stable, are easily produced and modified on a large scale at low cost and can be labeled or stained with a multitude of sensor dyes. These dye modified particle conjugates have several advantages as compared to conventional molecular probes like enhanced brightness, ease of designing ratiometric systems by combining analyte-sensitive and inert reference dyes, and increased photostability. Moreover, stained nanoparticles can enable the use of hydrophobic dyes in aqueous environments.
Here we present our work on multicolored sensors for the measurement of pH, oxygen and saccharides utilizing amorphous SiO2 NPs.
In recent years, chromium (III) complexes have received a lot of attention as novel near-infrared (NIR) emitters triggered by the report on the first molecular ruby Cr(ddpd)2(BF4)3 with a high photoluminescence quantum yield of 13.7% of its near infrared (NIR) emission band and a long luminescence lifetime of 1.122 ms at room temperature.[1] However, in an oxygen-containing environment, the photoluminescence quantum yields and luminescence lifetimes of these chromium(III) complexes show only very small values. This hampers their application as NIR luminescence labels. This application, that cannot be tackled by conventional deoxygenating approaches, requires suitable strategies to protect the luminescence of the chromium(III) complexes from oxygen quenching. An elegant approach to reduce the undesired luminescence quenching by triplet oxygen explored by us presents the incorporation of these chromium(III) complexes into different types of amorphous, non-porous silica nanoparticles, that can be simply surface functionalized, e.g., with targeting ligands and/or other sensor molecules. In this work, as first proof-of-concept experiments, a set of chromium (III) complexes constituting of different ligands and counter anions, were embedded into the core of silica nanoparticles. Subsequently, the optical properties of the resulting luminescent silica nanoparticles were spectroscopically assessed by steady state and time-resolved luminescence spectroscopy. First results of time-resolved luminescence measurements confirm our design concept of nanoscale NIR emissive Cr(III) complex-based reporters
Engineered nanoparticles (NPs) with various chemical compositions and surface functionalities are routinely fabricated for industrial applications such as medical diagnostics, drug delivery, sensing, catalysis, energy conversion and storage, opto-electronics, and information storage which improve the quality of life and European prosperity. NP function, performance, interaction with biological species, and environmental fate are largely determined by their surface functionalities. Standardized repeatable surface characterization methods are therefore vital for quality control of NPs, and to meet increasing concerns regarding their safety. Therefore, industry, regulatory agencies, and policymakers need validated traceable measurement methods and reference materials. This calls for fit-for-purpose, validated, and standardized methods, and reference data and materials on the surface chemistry of engineered NPs. Here, we present a concept for the development of such standardized measurement protocols utilizing method cross-validation and interlaboratory comparisons (ILCs) with emphasis on both advanced measurement methods such as quantitative Nuclear Magnetic Resonance (qNMR), X-ray photoelectron spectroscopy (XPS) and secondary ion mass spectrometry (SIMS) and cost-efficient, non-surface specific methods like optical assays and electrochemical titration methods.
Engineered and tailored nanomaterials (NM) are of great interest in the life and material sciences, as they can be used, e.g., as drug carriers, barcodes, fluorescent sensors, and multimodal labels in bioanalytical assays and imaging applications. Their performance and safety depend not only on their particle size, size distribution, and morphology, but also on their surface chemistry, i.e., the total number of surface functional groups (FG) and the number of FG accessible for subsequent functionalization with ligands or biomolecules, which in turn determines surface charge, colloidal stability, biocompatibility, and toxicity. It also underlines the importance of validated analytical methods that provide accurate information on these application-relevant physicochemical properties with a known uncertainty. In the case of FG quantification, this calls for robust, fast, inexpensive, and reliable methods which allow for the characterization of a broad variety of NM differing in size, chemical composition, and optical properties.
Methods
Aiming at the development of simple, versatile, and multimodal tools for the quantification of bioanalytically relevant FG such as amine, carboxy, thiol, and aldehyde functionalities, we investigated and compared various analytical methods commonly used for FG quantification. This includes electrochemical titration methods, dye-based optical assays, and other instrumental analytical techniques such as nuclear magnetic resonance (NMR), mass spectrometry (MS), and thermal analysis methods.
Results
Here, we will present examples for different types of NMs and FGs including results from a currently running interlaboratory comparison (ILC) with the National Research Council of Canada (NRC) to pave the road for method standardization.
Innovative aspects
• Surface analysis
• Performance and safety of nanomaterials
• Standardization
Engineered nanomaterials (NM) with their large surface-to-volume ratios and their for some materials observed size-dependent functional properties are of increasing relevance for current and future developments in various fields such as medical and pharmaceutical industry, computing and electronics or food and consumer products. The performance and safety of NM are determined by the sum of their intrinsic physicochemical properties. Especially, the particle surface chemistry, which is largely controlled by the chemical nature and density of functional groups (FG) and ligands, is an important key driver for NM performance, stability, and processibility as well as the interaction of NM with the environment. Thus, methods for FG quantification can foster the sustainable development of functional and safe(r) NM.
Aiming at the development of simple, versatile, and multimodal tools for the quantification of many bioanalytically relevant FG and ligands, we investigated and compared various analytical methods commonly used for FG quantification. This includes electrochemical titration methods, dye-based optical assays, and other instrumental analytical techniques such as nuclear magnetic resonance and thermal analysis methods.
The potential of our multimodal approach for FG quantification was demonstrated for commercial and custom-made polymeric and silica particles of varying FG, used as optical pH sensors. In the future, our strategy can contribute to establish multi-method characterization strategies to provide a more detailed picture of the structure-properties relationship.
In recent years, the use of functionalized micro- and nanomaterials has increased rapidly for a wide range of applications in the life and material sciences, due to their unique properties in combination with their high surface-to-volume ratio and stability. For instance, functionalized micro- and nanomaterials, that are labeled or stained with a multitude of sensor dyes can be used for monitoring, and quantification of neutral and ionic analytes. These materials have several advantages as compared to conventional molecular probes like enhanced brightness, ease of designing ratiometric systems by combining analyte-sensitive and inert reference dyes, and increased photostability. Moreover, stained nanoparticles can enable the use of hydrophobic dyes in aqueous environments.
Versatile templates and carriers for the fabrication of nanosensors by staining and/or labeling with different fluorophores and sensor molecules are biocompatible silica and polymeric particles, because they can be synthesized in large scales at low costs with different surface chemistries.
Here we present our work on multicolored sensors for the measurement of pH, oxygen and saccharides utilizing commercially available or in-house synthesized silica and polymeric particles.
Nowadays amorphous silica nanoparticles (SiO2-NP) are one of the most abundant engineered nanomaterials, that are highly stable and can be easily produced on a large scale at low cost. Surface functionalized SiO2-NP are of great interest in the life and material sciences, as they can be used e.g. as
drug carriers, fluorescent sensors, and multimodal labels in bioanalytical assays and imaging applications. Their performance in such applications depends not only on particle size, size distribution, and morphology, but also on surface chemistry, i.e. the total number of surface functional groups (FG)
and the number of FG accessible for subsequent functionalization with ligands or biomolecules, which in turn determines surface charge, colloidal stability, biocompatibility, and toxicity. Aiming at the development of simple, versatile, and multimodal tools for the quantification of many bioanalytically relevant FG and ligands, we investigated and compared various analytical methods commonly used for FG quantification. This includes electrochemical titration methods, dye-based optical assays, and other instrumental analytical techniques such as nuclear magnetic resonance and thermal analysis methods.
The potential of our multimodal approach for FG quantification was demonstrated for commercial and custom-made silica particles of varying FG, showing not only an influence of the synthesis methods on the number of FG but also on the performance. In the future, our strategy can contribute to establish multi-method characterization strategies to provide a more detailed picture of the structure-properties relationship.
In the focus of division Biophotonics are the design, preparation, analytical and spectroscopic characterization, and application of molecular and nanoscale
functional materials, particularly materials with a photoluminescence in the visible, near infrared (NIR) and short-wave infrared (SWIR). This includes optical reporters for bioimaging and sensing, security and authentication barcodes, and materials for solid state lighting, energy conversion, and photovoltaics. For the identification of optimum particle structures quantitative spectroscopic studies are performed under application-relevant conditions, focusing on the key performance parameter photoluminescence quantum yield. In addition, simple, cost-efficient, and standardizable strategies for quantifying functional groups on the surface of nano- and microparticles are developed, here with a focus on optical assays and electrochemical titration methods, cross-validated by more advanced methods such as quantitative NMR. In addition, reference materials and reference products are developed for optical methods, particularly luminescence techniques, and for analytical methods utilized for the characterization of nanomaterials.
For fluorescence microscopy, there is an increasing need for suitable calibration tools and reference materials for microscope calibration, the determination of performance parameters, and the regular control and validation of instrument performance. This is addressed in the BmWk-financed project FluMikal (WIPANO program) by two research groups from academia and two companies, that is coordinated by BAM. Here we present different approaches to liquid and solid fluorescence standards for the determination of the wavelength-dependent spectral sensitivity of fluorescence microscopes and ideas concerning the choice of suitable fluorescence lifetime standard for increasingly utilized fluorescence lifetime imaging (FLIM).
The digitalization of quality control processes and the underlying data infrastructures for safety relevant components, such as hydrogen pressure vessels, plays a significant role in the transition towards Industry 4.0. In the current safety regulations for hydrogen pressure vessels, there is no established concept for structural health monitoring. The development of a reliable structural health monitoring methodology for monitoring the structural integrity of pressure vessels enables a fast-forward transition from personnel- and costintensive recurring inspections, a.k.a. periodic maintenance, to predictive maintenance. In the work presented; we investigated the application of ultrasonic guided wave propagation to monitor and assess the condition of Type IV composite overwrapped pressure vessel (COPV). A sensor network of fifteen piezo-electric wafers is placed on the carbon fibre reinforced composite cylinder. Five different artificial damage configurations are created by gluing two different weight blocks on three different locations. The database containing measured guided wave data sets is enriched by two different boundary conditions. We utilized an open-source software, openBIS labnotebook, to store and analyse experimental datasets. The guided wave ultrasonic signals were investigated and analysed by using commonly used ultrasonic features (e.g., amplitude, frequency, time of flight) as well as non-traditional time-series features (kurtosis, skewness, variance). The features were used to calculate damage index and the detection performance for the results has been evaluated. The results suggest that both traditional and non-traditional features assume significant importance in artificial damage detection. The future works will additionally involve the impacts of operational conditions, such as periodic pressure variations temperature loadings as well as material degradations.
The application of composite overwrapped pressure vessels (COPV) to store hydrogen and other compressed gases, especially when operating at high pressures, imposed the need for an innovative and reliable approach to ensure the safe operation of the system. Continuous structural health monitoring (SHM) based on ultrasonic guided waves (GWs) is a promising approach due to the ability of the wave to propagate for long distances and go around complex structures, moreover the high sensitivity to various failure modes such as delamination, matrix cracking and debonding. In this study, we scrutinize the behavior of (GWs) within the COPV using a network of PZT elements that are used for excitation and sensing. A laser doppler vibrometer (LDV) was also used to scan the surface of the vessel in various directions. L(0,1) and L(0,2) were observed in the captured signal. The L(0, 2) appeared to be a dominant mode in the COPV and was capable of propagating along the entire length and maintaining a good signal-to-noise ratio. The L(0,2) mode maintained the same phase velocity when it is captured at various excitation angles (0, 45, and 90 degrees). The reduced effect of the orthotropy of the materials on the propagating waves is an important result as it will reduce the complexity in data processing when performing damage identification.
In the event of moisture deterioration, rapid detection and localization is particularly important to prevent further deterioration and costs. For building floors, the layered structure poses a challenging obstacle for most moisture measurement methods. But especially here, layer-specific information on the depth of the water is crucial for efficient and effective repairs. Ground Penetrating Radar (GPR) shows the potential to generate such depth information. Therefore, the present work investigates the suitability of GPR in combination with machine learning methods for the automated classification of the typical deterioration cases (i) dry, (ii) wet insulation, and (iii) wet screed.
First, a literature review was conducted to identify the most common methods for detecting moisture in building materials using GPR. Here, it especially became clear that all publications only investigated individual time-, amplitude- or frequency features separately, without combining them. This was seen as a potential aspect for innovation, as the multivariate application of several signal features can help to overcome individual weaknesses and limitations.
Preliminary investigations carried out on drying screed samples confirmed the profitable use of multivariate evaluations. In addition to the general suitability and dependencies of various features, first limitations due to possible interference between the direct wave and the reflection wave could be identified. This is particularly evident with thin or dry materials, for which the two-way travel times of the reflected radar signals become shorter.
An extensive laboratory experiment was carried out, for which a modular test specimen was designed to enable the variation of the material type and thickness of screed and insulation, as well as the simulation of moisture deteriorations. The data collected revealed clear differences between dry and deteriored structures within measured B-scans. These deviations were to be detected with the newly introduced B-scan features, which evaluate the statistical deviation of A-scan features within a survey line. In this way, deteriorations to unknown floor structures are recognized, regardless of the material parameters present. In a subsequent training and cross-validation process of different classifiers, accuracies of over 88 \% of the 504 recorded measurements (252 different experimental setups) were achieved. For that, the combination of amplitude and frequency features, which covered all relevant reflections of the radar signals, was particularly beneficial. Furthermore, the data set showed only small differences between dry floors and deteriored screeds for the B-scan features, which could be attributed to a homogeneous distribution of the added water in the screeds. The successfully separation of these similar feature distributions raised the suspicion of overfitting, which was examined in more detail by means of a validation with on-site data.
For this purpose, investigations were carried out at five different locations in Germany, using the identical measurement method like in the laboratory. By extracting drilling cores, it was possible to determine the deterioration case for each measurement point and thus generate a corresponding reference. However, numerous data had to be sorted out before classification, since disturbances due to underfloor heating, screed reinforcements, steel beams or missing insulation prevented comparability with the laboratory experiments. Validation of the remaining data (72 B-scans) achieved only low accuracy with 53 \% correctly classified deterioration cases. Here, the previously suspected overfitting of the small decision boundary between dry setups and deteriored screeds within the laboratory proved to be a problem. The generally larger deviations within (also dry) on-site B-scans were thus frequently misclassified as screed deterioration. In addition, there were sometimes strongly varying layer thicknesses or changing cases of deterioration within a survey line, which caused additional errors due to the local limitation of the drilling core reference. Nevertheless, individual on-site examples also showed the promising potential of the applied signal features and the GPR method in general, which partly allowed a profound interpretation of the measurements. However, this interpretation still requires the experience of trained personnel and could not be automated using machine learning with the available database. Nevertheless, such experience and knowledge can be enriched by the findings of this work, which provide the basis for further research.
Future work should aim at building an open GPR data base of on-site moisture measurements on floors to provide a meaningful basis for applying machine learning. Here, referencing is a crucial point, whose limitations with respect to the moisture present and its distribution can easily reduce the potential of such efforts. The combination of several reference methods might help to overcome such limitations. Similarly, a focus on monitoring approaches can also help to reduce numerous unknown variables in moisture measurements and increase confidence in the detection of different deterioration cases.
These data sets serve as models for calculating the specific surface area (BET method) using gas sorption in accordance with ISO 9277.
The present measurements were carried out with nitrogen at 77 Kelvin and argon at 87 Kelvin.
It is recommended to use the following requirements for the molecular cross-sectional area:
Nitrogen: 0.1620 nm²
Argon: 0.1420 nm²
Titanium dioxides certified with nitrogen sorption and additionally measured with argon for research purposes were used as sample material.
The resulting data sets are intended to serve as comparative data for own measurements and show the differences in sorption behaviour and evaluations between nitrogen and argon.
These data are stored in the universal AIF format (adsorption information file), which allows flexible use of the data.
We employ in-house generated synthetic Al-Si matrix composite XCT data for training deep convolutional neural networks for XCT data conditioning and automatic segmentation. We propose an in-house multilevel deep conditioning framework capable of rectifying noise and blur in corrupted XCT data sequentially. Furthermore, for automatic segmentation, we utilize a special in-house network coupled with a novel iterative segmentation algorithm capable of generalized learning from synthetic data. We report a consistent SSIM efficiency of 92%, 99%, and 95% for the combined denoising/deblurring, standalone denoising, and standalone deblurring, respectively. The overall segmentation precision was over 85% according to the Dice coefficient. We used experimental XCT data from various scans of Al-Si matrix composites reinforced with ceramic particles and fibers.
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.