Identical Samples Research Articles

The role of spin diffusion in endogenous metal ions DNP.

The sensitivity of solid state nuclear magnetic resonance spectroscopy can be enhanced via dynamic nuclear polarization (DNP) using unpaired electrons as polarizing agents. In metal ions based (MI)-DNP, paramagnetic metal ions are introduced as dopants into inorganic materials serving as endogenous polarizing agents. Having polarizing agents as part of the structure enables signal enhancements within the bulk of the material. Nuclear spins can be hyperpolarized either directly through their coupling to the polarizing agent or via homonuclear spin diffusion. In this work, we addressed what are the factors determining the relative sizes of the spin pools polarized by each of these two mechanisms and how changing their contribution to the polarization process affects the experimental outcome. Experimentally, we adjusted the spin diffusion rate through modifying the isotope ratio 6Li/7Li in otherwise identical samples, Li4Ti5O12 doped with paramagnetic Fe(III). DNP experiments on samples with typical content of polarizing agents for MI-DNP, corroborated by simulations, evidenced that while the efficiency of spin diffusion has large effects on the polarization buildup times, the enhancements remain largely unaffected.

Results of the 2023 Rapid DNA Multi-Laboratory Study – I-Chip

Identical sensitivity and mixture sample sets were provided to six external laboratories to evaluate the recent enhancements to the Rapid DNA technology for crime scene-type samples. Data were returned to the Federal Bureau of Investigation and the National Institute of Standards and Technology for review and analysis. Manual interpretation (modified Rapid DNA analysis) was conducted on all samples to assess the success of genotyping for both full profiles and the 20 Combined DNA Index System (CODIS) core STR loci using 18 total cellular DNA input amounts ranging from 102.4ng to 0.025ng. Two-person and three-person mixtures at varying ratios for two total DNA inputs were analyzed and observed allele percentages were compared to expected allele percentages.

Results of the 2023 Rapid DNA Multi-Laboratory Study – RapidINTEL Plus Sample Cartridge

Identical sensitivity and mixture sample sets were provided to six external laboratories to evaluate the recent enhancements to the Rapid DNA technology for crime scene-type samples. Data were returned to the Federal Bureau of Investigation and the National Institute of Standards and Technology for review and analysis. Manual interpretation (modified Rapid DNA analysis) was conducted on all samples to assess the success of genotyping for both full profiles and the 20 Combined DNA Index System (CODIS) core STR loci using 18 total cellular DNA input amounts ranging from 102.4ng to 0.025ng. Two-person and three-person mixtures at varying ratios for two total DNA inputs were analyzed and observed allele percentages were compared to expected allele percentages.

Chimeric mitochondrial RNA transcripts predict mitochondrial genome deletion mutations in mitochondrial genetic diseases and aging.

While it is well understood that mitochondrial DNA (mtDNA) deletion mutations cause incurable diseases and contribute to aging, little is known about the transcriptional products that arise from these DNA structural variants. We hypothesized that mitochondrial genomes containing deletion mutations express chimeric mitochondrial RNAs. To test this, we analyzed human and rat RNA sequencing data to identify, quantitate, and characterize chimeric mitochondrial RNAs. We observed increased chimeric mitochondrial RNA frequency in samples from patients with mitochondrial genetic diseases and in samples from aged humans. The spectrum of chimeric mitochondrial transcripts reflected the known pattern of mtDNA deletion mutations. To test the hypothesis that mtDNA deletions induce chimeric RNA transcripts, we treated 18 mo and 34 mo rats with guanidinopropionic acid to induce high levels of skeletal muscle mtDNA deletion mutations. With mtDNA deletion induction, we demonstrate that the chimeric mitochondrial transcript frequency also increased and correlated strongly with an orthogonal DNA-based mutation assay performed on identical samples. Further, we show that the frequency of chimeric mitochondrial transcripts predicts expression of both nuclear and mitochondrial genes central to mitochondrial function, demonstrating the utility of these events as metrics of age-induced metabolic change. Mapping and quantitation of chimeric mitochondrial RNAs provides an accessible, orthogonal approach to DNA-based mutation assays, offers a potential method for identifying mitochondrial pathology in widely accessible datasets, and opens a new area of study in mitochondrial genetics and transcriptomics.

Comprehensive Study on the Role of Proton in the Insertion Chemistry of Aqueous Zn-Ion Batteries

Li-ion rechargeable batteries played a major role in the present revolution of portable devices since Goodenough's group switched from sulfide to oxide cathode materials in the 1980s. However, the safety of explosive organic electrolytes, rising demand-driven costs, and even geopolitical concerns over lithium minerals have prompted scientists to seek alternate systems. Due to their enormous capacity, Zn-ion rechargeable batteries with a non-explosive aqueous electrolyte have garnered significant attention among the candidates. Especially the newly demonstrated capacity over 300 mAh/g in the V2O5 cathode illustrated the promise of the aqueous Zn-ion system. Although aqueous electrolytes reduce safety concerns, how protons function in insertion chemistry is still unclear. In contrast to Li-ion's single insertion behavior in organic electrolytes, charge carriers compete with one another, particularly in low-pH regions with a lot of proton content. Therefore, a thorough examination of insertion behavior over a broad pH range is necessary to comprehend the involvement of protons.In this work, we systematically investigated the insertion behavior in the V2O5 cathode in two distinct cell types in three pH conditions. To guarantee reliable results, we integrated multi-scale analytical methods and selected compact sampling points from discharge curves. Our first cell type, the beaker cell, provided sufficient protons comparable to metal cations. The insertion behavior in this environment was dependent on the proton concentration. EDS Elemental maps from samples with greater pH levels reveal Zn signals are found even in the early stages of discharge, despite the fact that protons are inserted first in low-pH samples (Fig. 1a). This implies that Zn-ion plays a major role as a charge carrier while influence of protons on discharge voltage or capacity becomes less significant under mildly acidic conditions. Additionally, the vanadium’s valence state changed as the discharge proceed, more reduced when Zn-ion is inserted instead of proton in the high-pH region, according to the XPS spectrum from identical samples with EDS maps. We employed X-ray absorption spectroscopy techniques to overcome the shortcomings of the surface sensitivity of XPS and local region observation in STEM. The XAS results provided insights about the overall oxidation state changes of vanadium as a result of carrier-ion insertion, where the XANES spectrum shows a modest reduction with proton insertion and a notable reduction upon the Zn-ion insertion. The bonding nature was further explained by the simultaneously obtained EXAFS spectrum, which showed the characteristic peak of the Zn insertion site and the peak splitting of V-V bonding under high pH circumstances. In addition to Zn-ion electrolytes, we also revealed similar pH-dependent insertion behavior in monovalent Na-, divalent Mg-, and trivalent Al-ion electrolytes to generalize our hypothesis.Coin cell cases show the opposite of the beaker cell test, where the proton has a negligible effect on the insertion chemistry. Regardless of proton concentration, we discovered that Zn-ions are always inserted from the first step of discharge, as evidenced by elemental maps in Fig. 1b. The oxidation state variation of vanadium in the XPS spectrum and even redox peak position in CV curves for every pH was identical, which can be somewhat inferred from similar discharge curve shapes irrespective of pH. We concluded that the proton contribution to discharge capacity is only meaningful when a substantial amount of highly acidic electrolyte is supplied. Thus, it is essential to consider the relative mass ratio between the electrolyte volume and cathode material mass to ascertain how protons affect the voltage and capacity of an aqueous battery system. Figure 1

(Invited) Replicating the Corrosion Behavior of Steel and Aluminum Alloys at a Coastal Exposure Site: Using an Accelerated Combined-Effects Chamber Simulation (ACES) for Improved Laboratory Corrosion Testing

Although there are numerous test methods for exposing bare and coated metals in a controlled, corrosive environment to determine relative corrosion resistance and coating behavior, the prediction and correlation of corrosion performance of “accelerated exposure tests” in environmental chambers like salt spray (B117) to field environments are not always straightforward. Existing test methods do not address the simultaneous exposure of various atmospheric and environmental conditions that can affect corrosion performance of a bare or coated metal or correlate results of exposure chamber tests with exposure to outdoor atmospheres and with end-user performance. Many investigations have been performed in recent years to clarify the role of environmental and climatic factors in the atmospheric corrosion of commonly used structural metals and coatings as well as simulate their observed corrosion behavior in the laboratory.The first step toward developing better accelerated test methods is to analyze and accurately reproduce these environments in a laboratory setting. To achieve this goal, a new state-of-the-art accelerated combined effects simulation (ACES) exposure chamber has been developed and has undergone testing by the U.S. Air Force Research Laboratory (AFRL) in collaboration with the U.S. Naval Research Laboratory (NRL) to replicate field conditions and corrosion behavior of various alloys, coating systems, and corrosion/environmental sensors. This chamber was built to include more environmental effects than any previous exposure chamber to accurately mimic a field environment that includes temperature and humidity control, ultraviolet (UV), mixed gasses, and actuators to simulate mechanical stresses.The long term objective of this effort was to develop a better method to categorize the environmental severity of field exposure sites in terms of corrosion attack, which will aid in the design of accelerated laboratory test protocols and lifetime prediction for bare metal substrates in coastal environments. Measurements included site specific weather parameters, mass loss of 1018 steel, AA2024-T3 and AA7075-T6, the corrosion characteristics of uncoated aluminum alloys AA2024-T3 and AA7075-T6, and the resulting environmental deposition of salts onto the aluminum alloy exposure coupons.In the present work, the ACES was used to simulate a 90 day exposure environment of bare metal coupons at the NRL-Key West (NRL-KW) outdoor exposure facility (Key West, FL) as well as an “accelerated” version of the environmental exposure period. The resulting corrosion behavior of bare steel and aluminum alloy coupons in the chamber were compared to identical samples exposed at the NRL-KW facility. It was determined that the exposed steel and aluminum alloy coupons in the ACES chamber exhibited statistically similar values in terms of mass loss (Figure 1), and other corrosion parameters such as thickness of material lost, maximum pit depth, pit coverage and pit density to that determined from identical coupons exposed in the field. In addition, scanning electron microscopy and energy dispersive spectroscopy (SEM-EDS) of elemental surface contamination through deposition from salt spray deposits on the field exposed samples were somewhat similar to the ACES chamber exposed samples.The ability to accurately replicate an exposure environment for materials corrosion testing would provide a significant research development and testing platform for bare and coated materials exposed to environments that are variable in nature. The intent of the test protocol is to be specific to a particular exposure environment or dynamically “tunable” to match the particular environment in which the coating or alloy substrate is intended to be used in service. Any test protocol developed using this system could allow a reasonable prediction of performance lifetime based upon an accelerated test in a relatively short timeframe. Figure 1

Parallel Analyses by Mass Spectrometry (MS) and Reverse Phase Protein Array (RPPA) Reveal Complementary Proteomic Profiles in Triple-Negative Breast Cancer (TNBC) Patient Tissues and Cell Cultures.

High-plex proteomic technologies have made substantial contributions to mechanism studies and biomarker discovery in complex diseases, particularly cancer. Despite technological advancements, inherent limitations in individual proteomic approaches persist, impeding the achievement of comprehensive quantitative insights into the proteome. In this study, we employed two widely used proteomic technologies, mass spectrometry (MS) and reverse phase protein array (RPPA) to analyze identical samples, aiming to systematically assess the outcomes and performance of the different technologies. Additionally, we sought to establish an integrated workflow by combining these two proteomic approaches to augment the coverage of protein targets for discovery purposes. We used 14 fresh frozen tissue samples from triple-negative breast cancer (TNBC: seven tumors versus seven adjacent non-cancerous tissues) and cell line samples to evaluate both technologies and implement this dual-proteomic strategy. Using a single-step protein denaturation and extraction protocol, protein samples were subjected to reverse-phase liquid chromatography (LC) followed by electrospray ionization (ESI)-mediated MS/MS for proteomic profiling. Concurrently, identical sample aliquots were analyzed by RPPA for profiling of over 300 proteins and phosphoproteins that are in key signaling pathways or druggable targets in cancer. Both proteomic methods demonstrated the expected ability to differentiate samples by groups, revealing distinct proteomic patterns under various experimental conditions, albeit with minimal overlap in identified targets. Mechanism-based analysis uncovered divergent biological processes identified with the two proteomic technologies, capitalizing on their complementary exploratory potential.

Quality of coffee dried on suspended terrace with different layers

Seeking to improve the final quality of coffee, producers began to improve and innovate in all stages of this segment to meet the demanding market. Hence, this study evaluated the physicochemical and sensory quality of dried unripe, cherry and over-ripe coffee cherries on suspended terraces in different layers. The experiment involved drying coffee in all layers of the suspended terraces, consisting of seven treatments and four replications as follows: T1– concrete terrace; T2 – conventional “single-layer” suspended terrace; T3 – first layer “suspended terrace with two layers”; T4 – second layer “suspended terrace with two layers”; T5 – first layer “suspended terrace with three layers”; T6 – second layer “suspended terrace with three layers”; and T7 – third layer “suspended terrace with three layers.” All treatments used identical samples composed of a mixture of coffee cherries in the three different physiological maturation stages (unripe, cherry and over-ripe). Percentage of each stage was evaluated in a one-liter container with coffee. Physicochemical and sensory quality of unripe, cherry and overripe coffees is influenced by the terrace structure. Treatments T5 and T6, represented by the first layer “suspended terrace with three layers” and second layer “suspended terrace with three layers,” presented the best results for the beneficial fermentation caused by the terrace structure, providing a more shaded environment and resulting in desirable sensory attributes.

Validation of point-of-care tests used at in-home assessments among older adults in primary care

Background Diagnosing acute disease in older adults is challenged by vague and atypical symptoms. Point-of-care tests (POCTs) at home may improve diagnostics and clinical decision-making. We compared various POCT devices to routine testing in acutely ill older adults to assess their clinical reliability. Methods We enrolled participants aged 65+ years requiring acute in-home assessment with signs of acute conditions. Venous and capillary blood samples were collected and analysed on-site using POCT, while identical samples were transported and analysed in a routine laboratory. Agreement between POCT and laboratory testing was assessed using scatter plots with linear regression, Pearson’s correlation coefficient (PCC), limits of agreement, and Bland-Altman plots. Misclassification rates were calculated based on clinically meaningful cut-offs to assess POCT’s clinical reliability. Results We included 100 participants with a mean age of 81.6 (±8.4 SD) years. Strong correlation was found between POCT and routine measurements (PCC: 0.76–0.94 for capillary samples and 0.85–0.98 for venous samples). Venous samples showed higher PCC than capillary, except for neutrophils (0.93 for capillary, 0.89 for venous). Misclassification occurred in capillary samples for haemoglobin (10/62) and total WBC (6/50), while in venous samples, misclassification was observed for haemoglobin (4/54), total WBC (4/50), K+ (5/47), urea (5/47), and creatinine (3/42). No misclassification was observed for Na+. Conclusion POCT provides acceptable, clinically reliable measurements in acutely ill older adults, potentially enhancing diagnostics and treatments during in-home assessment. Venous blood testing is preferable due to a lower misclassification rate, but capillary blood remains a pragmatic alternative, despite higher variation and inaccuracy.

Application of electrical resistivity and induced polarization for identification and erosion assessment in fine-grained soils – A case study

Electrical Resistivity Imaging (ERI) and Induced Polarization (IP) are the non-destructive and rapid methods used for mapping the subsurface. This paper presents an application of these methods for erodibility characterization. These tests and Erosion Function Apparatus (EFA) tests were conducted on identical samples with plasticity indices ranging from low to high to establish a correlation of ERI, IP and erosion rates. Erosion rate and erosion category were determined through the analysis of soil Electrical Resistivity (ER) and IP values. Then, the ERI and IP surveys were performed at a site that had CPT data to analyze the subsurface soil layers. In order to verify the developed correlation, three holes were hand augured and Shelby tube samples were extracted from the site to conduct EFA testing. The results show that ER and IP methods can be used to predict soil erosion characterization in the preliminary stages of bridge site developments with concerns about erosions around bridge piers.

Deep-Learning-Based Segmentation of Cells and Analysis (DL-SCAN).

With the recent surge in the development of highly selective probes, fluorescence microscopy has become one of the most widely used approaches to studying cellular properties and signaling in living cells and tissues. Traditionally, microscopy image analysis heavily relies on manufacturer-supplied software, which often demands extensive training and lacks automation capabilities for handling diverse datasets. A critical challenge arises if the fluorophores employed exhibit low brightness and a low signal-to-noise ratio (SNR). Consequently, manual intervention may become a necessity, introducing variability in the analysis outcomes even for identical samples when analyzed by different users. This leads to the incorporation of blinded analysis, which ensures that the outcome is free from user bias to a certain extent but is extremely time-consuming. To overcome these issues, we developed a tool called DL-SCAN that automatically segments and analyzes fluorophore-stained regions of interest such as cell bodies in fluorescence microscopy images using deep learning. We demonstrate the program's ability to automate cell identification and study cellular ion dynamics using synthetic image stacks with varying SNR. This is followed by its application to experimental Na+ and Ca2+ imaging data from neurons and astrocytes in mouse brain tissue slices exposed to transient chemical ischemia. The results from DL-SCAN are consistent, reproducible, and free from user bias, allowing efficient and rapid analysis of experimental data in an objective manner. The open-source nature of the tool also provides room for modification and extension to analyze other forms of microscopy images specific to the dynamics of different ions in other cell types.

Characterization of identical lithium-ion battery electrodes before and after charge/discharge cycles via in-plane large-area polishing

As an effective method to fabricate a large-area cross-sectional sample for lithium-ion battery electrodes, we perform in-plane polishing of LiNi0.8Co0.15Al0.05O2(NCA) cathode samples and obtain a large cross-sectional area with a diameter of 1.5 mm. The polished cross-sections of NCA cathode particles are sufficiently flat to perform the atomic force microscopy (AFM) measurements on each cathode particle. Following AFM-based Kelvin probe force microscopy and scanning spreading resistance microscopy measurements, an identical in-plane polished NCA sample is assembled into a coin cell for the charge and discharge processes. After 90 charge/discharge cycles, the in-plane-polished sample is successfully disassembled from the coin cell without causing critical damage. In addition, a microcrack structure, which is a typical degradation feature of the cycles of NCA particles, is observed for the identical in-plane polished NCA sample. This indicates that the in-plane polishing method is effective for investigating identical NCA electrode samples before and after the charge/discharge process. Furthermore, the in-plane polishing method can be successfully applied to the large-area polishing of a Si-based anode which is a mixture of Si carbon complexes and graphite particles. This study presents a novel methodology for analyzing the degradation of lithium-ion battery electrode materials.

Identifying Process Differences with ToF-SIMS: An MVA Decomposition Strategy.

In time-of-flight secondary ion mass spectrometry (ToF-SIMS), multivariate analysis (MVA) methods such as principal component analysis (PCA) are routinely employed to differentiate spectra. However, additional insights can often be gained by comparing processes, where each process is characterized by its own start and end spectra, such as when identical samples undergo slightly different treatments or when slightly different samples receive the same treatment. This study proposes a strategy to compare such processes by decomposing the loading vectors associated with them, which highlights differences in the relative behavior of the peaks. This strategy identifies key information beyond what is captured by the loading vectors or the end spectra alone. While PCA is widely used, partial least-squares discriminant analysis (PLS-DA) serves as a supervised alternative and is the preferred method for deriving process-related loading vectors when classes are narrowly separated. The effectiveness of the decomposition strategy is demonstrated using artificial spectra and applied to a ToF-SIMS materials science case study on the photodegradation of N719 dye, a common dye in photovoltaics, on a mesoporous TiO2 anode. The study revealed that the photodegradation process varies over time, and the resulting fragments have been identified accordingly. The proposed methodology, applicable to both labeled (supervised) and unlabeled (unsupervised) spectral data, can be seamlessly integrated into most modern mass spectrometry data analysis workflows to automatically generate a list of peaks whose relative behavior varies between two processes, and is particularly effective in identifying subtle differences between highly similar physicochemical processes.

Functional partially linear single index models with measurement error in both functional and real covariates

In this paper, we investigate the functional partially linear single index models with contaminated functional and real covariates when the functional sample grid is dense. To eliminate the influence of measurement error on the estimations of the proposed models, a two-stage estimation procedure is introduced. In the first stage, we utilize a kernel presmoothing regression to denoise the corrupted functional curves. In the second stage, the fitted functional curves are applied to replace the original ones to obtain the predictor of unknown parameter in parametric component with the attenuation correction method and then we derive the predictor of unknown link function in nonparametric part with kernel method. Meanwhile, in order to improve computational efficiency, functional slice inverse regression method is employed to estimate the functional single index parameter. The asymptotic properties are also stated in this process at the situation of independent and identical distribution sample under some mild conditions. A simulation study and real data analysis are further explored to illustrate the good performance of our methodology.

Scalable data fusion via a scale-based hierarchical framework: Adapting to multi-source and multi-scale scenarios

Multi-source information fusion addresses challenges in integrating and transforming complementary data from diverse sources to facilitate unified information representation for centralized knowledge discovery. However, traditional methods face difficulties when applied to multi-scale data, where optimal scale selection can effectively resolve these issues but typically lack the advantage of identifying the optimal and simplest data from different data source relationships. Moreover, in multi-source, multi-scale environments, heterogeneous data (where identical samples have different features and scales in different sources) is prone to occur. To address these challenges, this study proposes a novel approach in two key stages: first, aggregating heterogeneous data sources and refining datasets using information gain; second, employing a customized Scale-based Tree (SbT) structure for each attribute to help extract specific scale information value, thereby achieving effective data fusion goals. Extensive experimental evaluations cover ten different datasets, reporting detailed performance across multiple metrics including Approximation Precision (AP), Approximation Quality (AQ) values, classification accuracy, and computational efficiency. The results highlight the robustness and effectiveness of our proposed algorithm in handling complex multi-source, multi-scale data environments, demonstrating its potential and practicality in addressing real-world data fusion challenges.

Crack suppression in directed energy deposition of molybdenum

Additive manufacturing (AM) is a valuable tool for the fabrication and repair of refractory parts such as molybdenum alloys. Cracking is a common defect encountered during AM processing of refractory parts, that is generally associated with the segregation of light elements to grain boundaries which affect grain boundary cohesion and, ultimately, affect the final performance of the part. Similarly, because of the high melting points of refractory metals, lack-of-fusion defects are also common. The effect of build substrate and small alloying additions on suppression of defects during multilayer builds was investigated using directed energy deposition (DED) printing. Identical sample matrices were printed on three different build substrates: molybdenum (Mo), commercially pure titanium (Cp-Ti), and 316 stainless steel (316). Twenty-six usable samples were produced. Samples were cross sectioned, polished, and were characterized for total cross-section defect area. Additionally, samples from each substrate material were analyzed for grain boundary oxygen content. The strongest defect suppression, producing crack free material, was observed in samples printed on a Cp-Ti build substrate with a ten atomic percent addition of titanium in the molybdenum powder feed. The part quality was enhanced due to three factors: 1) the moderation of thermal diffusivity through a change in build plate material, 2) the suppression of light element segregation via increased solubility through titanium addition, and 3) a lack of brittle phase formation due to metallurgical compatibility of the build material with the build substrate. Analysis of defect area versus dimensionless number, π1, shows that increasing π1 reduced defects throughout the part.

Bone metastasis scintigram generation using generative adversarial learning with multi-receptive field learning and two-stage training.

Deep learning is the primary method for conducting automated analysis of SPECT bone scintigrams. The lack of available large-scale data significantly hinders the development of well-performing deep learning models, as the performance of a deep learning model is positively correlated with the size of the dataset used. Therefore, there is an urgent demand for an automated data generation method to enlarge the dataset of SPECT bone scintigrams. We introduce a deep learning-based generation model that can generate realistic but not identical samples from the original SPECT bone scintigrams. Following the generative adversarial learning architecture, a bone metastasis scintigram generation model christened BMS-Gen is proposed. First, BMS-Gen takes multiple input conditions and employs multi-receptive field learning to ensure that the generated samples are as realistic as possible. Second, BMS-Gen adopts generative adversarial learning to retain the diversity of the generated samples. Last, BMS-Gen uses a two-stage training strategy to improve the quality of the generated samples. Experimental evaluation conducted on a set of clinical data of SPECT BM scintigrams has shown the performance of the proposed BMS-Gen, achieving the best overall scores of 1678.0, 69.33, and 19.51 for FID (Fréchet Inception Distance), MSE (Mean Square Error), and PSNR (Peak Signal-to-Noise Ratio) metrics. The introduction of samples generated by BMS-Gen contributes a maximum (minimum) increase of 3.01% (0.15%) on the F-1 score and a maximum (minimum) increase of 6.83% (2.21%) on the DSC score for the image classification and segmentation tasks, respectively. The proposed BMS-Gen model can be used as a promising tool for augmenting the data of bone scintigrams, greatly facilitating the development of deep learning-based automated analysis of SPECT bone scintigrams.

Future Parabolic Trough Collector Absorber Coating Development and Service Lifetime Estimation

This work presents a study on the optical and mechanical degradation of parabolic trough collector absorber coatings produced through the spray coating application technique of in-house developed paint. The main aim of this investigation is to prepare, cure, load, and analyze the absorber coating on the substrate under conditions that mimic the on-field thermal properties. This research incorporates predicted isothermal and cyclic loads for parabolic trough systems as stresses. Biweekly inspections of loaded, identical samples monitored the degradation process. We further used the cascade of data from optical, oxide-thickening, crack length, and pull-off force measurements in mathematical modelling to predict the service life of the parabolic trough collector. The results collected and used in modelling suggested that cyclic load in combination with iso-thermal load is responsible for coating fatigue, influencing the solar absorber optical values and resulting in lower energy transformation efficiency. Finally, easy-to-apply coatings made out of spinel-structured black pigment and durable binder could serve as a low-cost absorber coating replacement for a new generation of parabolic trough collectors, making it possible to harvest solar energy to provide medium-temperature heat to decarbonize future food, tobacco, and paint production industrial processes.

Measurements of Thermoelectric Properties of Identical Bi-Sb Sample in Magnetic Fields and Influence of Sample Geometry

Measurements of Thermoelectric Properties of Identical Bi-Sb Sample in Magnetic Fields and Influence of Sample Geometry

Assessing the influence of sleep and sampling time on metabolites in oral fluid: implications for metabolomics studies

IntroductionThe human salivary metabolome is a rich source of information for metabolomics studies. Among other influences, individual differences in sleep-wake history and time of day may affect the metabolome.ObjectivesWe aimed to characterize the influence of a single night of sleep deprivation compared to sufficient sleep on the metabolites present in oral fluid and to assess the implications of sampling time points for the design of metabolomics studies.MethodsOral fluid specimens of 13 healthy young males were obtained in Salivette® devices at regular intervals in both a control condition (repeated 8-hour sleep) and a sleep deprivation condition (total sleep deprivation of 8 h, recovery sleep of 8 h) and their metabolic contents compared in a semi-targeted metabolomics approach.ResultsAnalysis of variance results showed factor ‘time’ (i.e., sampling time point) representing the major influencer (median 9.24%, range 3.02–42.91%), surpassing the intervention of sleep deprivation (median 1.81%, range 0.19–12.46%). In addition, we found about 10% of all metabolic features to have significantly changed in at least one time point after a night of sleep deprivation when compared to 8 h of sleep.ConclusionThe majority of significant alterations in metabolites’ abundances were found when sampled in the morning hours, which can lead to subsequent misinterpretations of experimental effects in metabolomics studies. Beyond applying a within-subject design with identical sample collection times, we highly recommend monitoring participants’ sleep-wake schedules prior to and during experiments, even if the study focus is not sleep-related (e.g., via actigraphy).