Powerful Method Research Articles

Weighted reverse counting process (WRCP): A novel approach to quantify the overall treatment effect with multiple time-to-event outcomes by adaptive weighting.

In a longitudinal randomized study where multiple time-to-event outcomes are collected, the overall treatment effect may be quantified by a composite endpoint defined as the time to the first occurrence of any of the selected events including death. The reverse counting process (RCP) was recently proposed to extend the restricted mean survival time (RMST) approach with an advantage of utilizing observations of events beyond the "first-occurrence" endpoint. However, the interpretation may be questionable because RCP treats all events equally without considering their different associations with the overall survival. In this work, we propose a novel approach, the weighted reverse counting process (WRCP), to construct a weighted composite endpoint to evaluate the overall treatment effect. A multi-state transition model is used to model the association between events, and an adaptive weighting algorithm is developed to determine the weight for individual endpoints based on the association between the nonfatal endpoints and death using the trial data. Simulation studies are presented to compare the performance of WRCP with RCP, log-rank test and RMST approach. The results show that WRCP is a powerful and robust method to detect the overall treatment effect while controlling the clinically false positive rate well across different simulation scenarios.

Comparative analysis of single-machine equivalent methods for heterogeneous PMSG-based wind farm with the VSC-HVDC system

This paper presents a comparative study of four single-machine equivalent methods used to simplify the analysis of small-signal stability (SSS) in grid-connected wind farms composed of multiple wind turbine generators (WTGs), particularly focusing on heterogeneous permanent magnet synchronous generator (PMSG)-based wind farms with a VSC-HVDC system. The four methods compared are (1) the dynamic aggregation method, (2) the average power or average control parameter method, (3) the weighted average method, and (4) the covering theorem method. The comparative analysis highlights the connections and differences among those four methods that help to gain a better understanding of their strengths and limitations as follows: (1) When the dynamic characteristics of individual PMSGs are different, the dynamic aggregation method may result in errors in assessing system SSS. (2) The average power and weighted average methods are accurate in assessing the system's SSS when a linear relationship exists between the system's oscillation modes and the PMSG output or control parameters. However, both methods may give erroneous SSS assessments when this linearity does not hold. (3) The weighted average method can accurately assess SSS when wind speeds differ across PMSGs, while the average wind speed method produces errors. (4) The covering theorem method is found to be conservative in SSS assessment. Finally, the methods are validated through simulations on two PMSG-based wind farm topologies connected to the grid with a VSC-HVDC system.

The background sodium leak channel NALCN is a major controlling factor in pituitary cell excitability

AbstractThe pituitary gland produces and secretes a variety of hormones that are essential to life, such as for the regulation of growth and development, metabolism, reproduction, and the stress response. This is achieved through an intricate signalling interplay between the brain and peripheral feedback signals that shape pituitary cell excitability by regulating the ion channel properties of these cells. In addition, endocrine anterior pituitary cells spontaneously fire action potentials to regulate the intracellular calcium ([Ca2+]i) level, an essential signalling conduit for hormonal secretion. To this end, pituitary cells must regulate their resting membrane potential (RMP) close to the firing threshold, but the molecular identity of the ionic mechanisms responsible for this remains largely unknown. Here, we revealed that the sodium leak channel NALCN, known to modulate neuronal excitability elsewhere in the brain, regulates excitability in the mouse anterior endocrine pituitary cells. Using viral transduction combined with powerful electrophysiology methods and calcium imaging, we show that NALCN forms the major Na+ leak conductance in these cells, appropriately tuning cellular RMP for sustaining spontaneous firing activity. Genetic depletion of NALCN channel activity drastically hyperpolarised these cells, suppressing their firing and [Ca2+]i oscillations. Remarkably, despite this profound function of NALCN conductance in controlling pituitary cell excitability, it represents a very small fraction of the total cell conductance. Because NALCN responds to hypothalamic hormones, our results also provide a plausible mechanism through which hormonal feedback signals from the brain and body could powerfully affect pituitary activity to influence hormonal function. imageKey points Pituitary hormones are essential to life as they regulate important physiological processes, such as growth and development, metabolism, reproduction and the stress response. Pituitary hormonal secretion relies on the spontaneous electrical activity of pituitary cells and co‐ordinated inputs from the brain and periphery. This appropriately regulates intracellular calcium signals in pituitary cells to trigger hormonal release. Using viral transduction in combination with electrophysiology and calcium imaging, we show that the activity of the background leak channel NALCN is a major controlling factor in eliciting spontaneous electrical activity and intracellular calcium signalling in pituitary cells. Remarkably, our results revealed that a minute change in NALCN activity could have a major influence on pituitary cell excitability. Our study provides a plausible mechanism through which the brain and body could intricately control pituitary activity to influence hormonal function.

A scaled derivative-based DMDc method for modelling multiple-input multiple-output mechanical systems

Dynamic mode decomposition with control (DMDc) is a powerful data-driven method for modelling dynamical systems with inputs and outputs. However, the inability to identify oscillations and the high sensitivity to noise limit the application of standard DMDc in structural dynamics. To address this challenge, this paper proposes a novel method called scaled derivative-based DMDc (sd-DMDc) to construct a discrete state-space model for mechanical systems using noisy data. This method requires only the displacement and excitation data, computes the scaled derivatives of displacement to extend the system states, and integrates the extended forward-backward strategy. Two crucial parameters are defined and optimized by minimizing the response error. The sd-DMDc algorithm is first verified through numerical simulations of a multiple-input multiple-output (MIMO) cantilever plate. The results indicate that sd-DMDc exhibits superior modelling accuracy across various noise levels and measurement points compared to the delay-DMDc method. It has also been proved that sd-DMDc can accurately identify dynamical systems from partial observations. Subsequently, vibration experiments are conducted on a MIMO cantilever plate. The sd-DMDc method is further verified using experimental data, and the conclusions align with those drawn from simulation studies. The sd-DMDc algorithm demonstrates great modelling accuracy, surpassing that of delay-DMDc, especially for partial observations. In addition, the model given by sd-DMDc can extract the modal parameters of the plate, which are consistent with the modal test results.

Continuous fiber printing of a modular heater for ion mobility spectrometry

Ion mobility spectrometry (IMS) is a powerful analytical method for separating and detecting gaseous substances like volatile organic compounds (VOCs). It is highly versatile, used both as a standalone technique and in combination with chromatographic methods for pre-separation or mass spectrometry for complex matrix analysis. This study explores the use of an advanced 3D printing technology to print a heater for an IMS-device.A continuous fiber printer, commonly used to enhance the mechanical properties of printed objects, was used to create a customized heating element by embedding a copper wire within the polymer matrix during the printing process. Cyclic olefin copolymer (COC) was chosen as the matrix material due to its analytical properties, such as low off-gassing and higher thermal stability, compared to the more commonly used polylactic acid (PLA).The 3D printed heater was incorporated into a drift tube ion mobility spectrometer, and its performance was evaluated by analyzing the separation of various ketones. The results show that the 3D printed heater effectively controlled the temperature within the drift tube, improving the resolving power of overlapping peaks.This study shows the potential of 3D printing technology to improve fabrication and functionality of IMS devices. The flexibility in design and material selection afforded by 3D printing not only enhances the analytical performance of IMS but also allows for customization and rapid prototyping of other analytical instruments or laboratory devices.

Homozygous Microdeletion Involving Exon 1 of ERCC8 and NDUFAF2 With Uniparental Isodisomy of Chromosome 5

ABSTRACTBackgroundUniparental isodisomy (UPiD) refers to a condition, in which both homologous chromosomes are inherited from only one parental homolog, which can result in either imprinting disorders or autosomal recessive conditions.MethodsWe performed chromosomal microarray analysis, exome sequencing (ES), and RNA sequencing (RNA‐seq) using the patient's urine‐derived cells on a patient with growth retardation and multiple congenital anomalies.ResultsWe identified a homozygous ~0.53 kb microdeletion at 5q12.1, which was transmitted from the father with paternal UPiD(5). The deletion encompassed the first exon of both the ERCC8 and NDUFAF2 genes, which are responsible for Cockayne syndrome (CS) and mitochondrial complex I deficiency, respectively. Furthermore, RNA‐seq confirmed the reduced expression of both genes. Indeed, in addition to clinical features common to both syndromes, such as growth retardation, developmental delay, and feeding difficulties, the patient exhibited blended phenotypes: the characteristic features of CS, including arthrogryposis, microcephaly, and facial dysmorphisms, and those of mitochondrial complex I deficiency, including high serum lactate levels and lethal apnea resulting in a severe clinical course.ConclusionThe results imply that ES in combination with RNA‐seq could be a powerful method for the detection of underlying factors responsible for rare genetic conditions, such as UPD.

A meshless Runge-Kutta-based Physics-Informed Neural Network framework for structural vibration analysis

In recent years, Physics-Informed Neural Networks (PINN) have emerged as powerful meshless numerical methods for solving partial differential equations (PDEs) in engineering and science, including the field of structural vibration. However, PINN struggles due to the spectral bias when the target PDEs exhibit high-frequency features. In this work, a meshless Runge-Kutta-based PINN (R-KPINN) framework for structural vibration modelling is proposed for the first time. In the framework, the meshless features of traditional PINN are retained while applying Runge-Kutta (R-K) time integration to discretise the temporal domain, thereby reducing computational demands and enhancing temporal flexibility. Besides, the proposed R-KPINN allows for segmental training of PINN, reducing the dimensionality of the structural vibration problems and offering accurate solutions to relatively high-frequency functions. Through numerical examples including free, damped, and forced vibration of Euler-Bernoulli beams, the proposed framework provides effective and precise solutions for predicting the responses of structures. In summary, the proposed R-KPINN framework provides a flexible, efficient, and interpretable meshless method for solving structural vibration problems with state-of-the-art PINN techniques.

Autoregressive HMM resolves biomolecular transitions from passive optical tweezer force measurements

Optical tweezer (OT) single molecule force spectroscopy is a powerful method to map out the energy landscape of biological complexes and has found increasing applications in academic and pharmaceutical research. The dominant method to extract molecular conformation transitions from the thermal diffusion-broadened trajectories of the microscopic OT probes attached to the single molecule of interest is through hidden Markov models (HMM). In standard applications, the HMMs assume a white noise spectrum of the probes superimposed onto the molecular signal.Here, we demonstrate, through theoretical derivation, computer modeling and experimental measurements that this standard white noise HMM (wnHMM) misses key features of real OT data. The deviation is most pronounced at higher frequencies because the white noise model does not account for the over-damped nature of particle diffusion in an OT harmonic potential in aqueous environments. To address this, we derive how to incorporate autoregression (ar) between consecutive data points into an HMM, and demonstrate through modeling and experiment that such an arHMM captures real OT data behavior across all frequency ranges. Through analysis of real OT data we recorded on a single DNA hairpin undergoing folding and unfolding transitions, we show that the wnHMM extracts lifetimes that are at least a factor of 2 faster and less consistent than the arHMM results which match expectations and prior measurements. Overall, our work suggests that arHMM should be the default model choice for analysis OT single molecule transitions and that its use will improve the fidelity and accuracy of single molecule force spectroscopy measurements.

Efficient small fragment sequencing of human, cattle, and bison miRNA, small RNA, or csRNA-seq libraries using AVITI

BackgroundNext-Generation Sequencing (NGS) catalyzed breakthroughs across various scientific domains. Illumina’s sequencing by synthesis method has long been central to NGS, but new sequencing methods like Element Biosciences’ AVITI technology are emerging. AVITI is reported to offer improved signal-to-noise ratios and cost reductions. However, its reliance on rolling circle amplification, which can be affected by polymer size, raises questions about its effectiveness in sequencing small RNAs (sRNAs) such as microRNAs (miRNAs), small nucleolar RNAs (snoRNAs), and many others. These sRNAs are crucial regulators of gene expression and involved in various biological processes. Additionally, capturing capped small RNAs (csRNA-seq) is a powerful method for mapping active or “nascent” RNA polymerase II transcription initiation in tissues and clinical samples.ResultsHere, we report a new protocol for seamlessly sequencing short fragments on the AVITI and demonstrate that AVITI and Illumina sequencing technologies equivalently capture human, cattle (Bos taurus), and bison (Bison bison) sRNA or csRNA sequencing libraries, increasing confidence in both sequencing approaches. Additionally, analysis of generated nascent transcription start site (TSS) data for cattle and bison revealed inaccuracies in their current genome annotations, underscoring the potential and necessity to translate small and nascent RNA sequencing methodologies to livestock.ConclusionsOur accelerated and optimized protocol bridges the advantages of AVITI sequencing with critical methods that rely on sequencing short fragments. This advance bolsters the utility of AVITI technology alongside traditional Illumina platforms, offering new opportunities for NGS applications.

Ultrafast adaptive optics for imaging the living human eye

Adaptive optics (AO) is a powerful method for correcting dynamic aberrations in numerous applications. When applied to the eye, it enables cellular-resolution retinal imaging and enhanced visual performance and stimulation. Most ophthalmic AO systems correct dynamic aberrations up to 1−2 Hz, the commonly-known cutoff frequency for correcting ocular aberrations. However, this frequency may be grossly underestimated for more clinically relevant scenarios where the medical impact of AO will be greatest. Unfortunately, little is known about the aberration dynamics in these scenarios. A major bottleneck has been the lack of sufficiently fast AO systems to measure and correct them. We develop an ultrafast ophthalmic AO system that increases AO bandwidth by ~30× and improves aberration power rejection magnitude by 500×. We demonstrate that this much faster ophthalmic AO is possible without sacrificing other system performances. We find that the discontinuous-exposure AO-control scheme runs 32% slower yet achieves 53% larger AO bandwidth than the commonly used continuous-exposure scheme. Using the ultrafast system, we characterize ocular aberration dynamics in six clinically-relevant scenarios and find their power spectra to be 10−100× larger than normal. We show that ultrafast AO substantially improves aberration correction and retinal imaging performance in these scenarios compared with conventional AO.

A Caching-based Framework for Scalable Temporal Graph Neural Network Training

Representation learning over dynamic graphs is critical for many real-world applications such as social network services and recommender systems. Temporal graph neural networks (T-GNNs) are powerful representation learning methods and have demonstrated remarkable effectiveness on continuous-time dynamic graphs. However, T-GNNs still suffer from high time complexity, which increases linearly with the number of timestamps and grows exponentially with the model depth, making them not scalable to large dynamic graphs. To address the limitations, we propose Orca , a novel framework that accelerates T-GNN training by caching and reusing intermediate embeddings. We design an optimal caching policy, named MRD , for the uniform cache replacement problem, where embeddings at different intermediate layers have identical dimensions and recomputation costs. MRD not only improves the efficiency of training T-GNNs by maximizing the number of cache hits but also reduces the approximation errors by avoiding keeping and reusing extremely stale embeddings. For the general cache replacement problem, where embeddings at different intermediate layers can have different dimensions and recomputation costs, we solve this NP-hard problem by presenting a novel two-stage framework with approximation guarantees on the achieved benefit of caching. Furthermore, we have developed profound theoretical analyses of the approximation errors introduced by reusing intermediate embeddings, providing a thorough understanding of the impact of our caching and reuse schemes on model outputs. We also offer rigorous convergence guarantees for model training, adding to the reliability and validity of our Orca framework. Extensive experiments have validated that Orca can obtain two orders of magnitude speedup over state-of-the-art T-GNNs while achieving higher precision on various dynamic graphs.

Aerospace Equipment Fault Diagnosis Method Based on Fuzzy Fault Tree Analysis and Interpretable Interval Belief Rule Base

The stable operation of aerospace equipment is important for space safety, and the fault diagnosis of aerospace equipment is of practical significance. A fault diagnosis system needs to establish clear causal relationships and provide interpretable determination results. Fuzzy fault tree analysis (FFTA) is a flexible and powerful fault diagnosis method, which can deeply understand causes and fault mechanisms. The interval belief rule base (IBRB) can describe uncertainty. In this paper, an interpretable fault diagnosis model (FFDI) for aerospace equipment based on FFTA and the IBRB is presented for the first time. Firstly, the initial FFDI is constructed with the assistance of FFTA. Second, a model inference is implemented based on an evidential reasoning (ER) parsing algorithm. Then, a projection covariance matrix adaptive evolutionary strategy algorithm with an interpretability constraints (IP-CMA-ES) optimization algorithm is used for optimization. Finally, the effectiveness of the FFDI is verified by a flywheel dataset. This method ensures the completeness of the rule base and the interpretability of the model, avoids the problem of exploding certain combinations of rules, and is suitable for the fault diagnosis of aerospace equipment.

Unsupervised Coherent Noise Removal From Seismological Distributed Acoustic Sensing Data

AbstractRecent advances in sensing technologies, particularly Distributed Acoustic Sensing (DAS), have significantly improved the collection and analysis of seismological data. DAS is a powerful method for detecting vibrations from various sources, including earthquakes. However, the vast amount of data produced by DAS requires sophisticated analytical methods to differentiate between signals of interest and noise, such as traffic signals. We introduce an innovative approach by extending the Noise2Self framework to effectively remove unwanted, structured coherent noise from DAS data. By creating masks based on the characteristics of traffic signals, we isolate and preserve earthquake signals while maintaining the denoising performance of the original Noise2Self approach, which reduces noise without requiring clean reference data. To evaluate our method, we used synthetic data generated from seismic recordings of closely spaced seismometers and then applied our approach to data from a DAS array crossing the Alpine Fault near Haast, New Zealand. Our results demonstrate that our model successfully removes traffic noise and other non‐coherent noise while preserving seismic signals, leading to improvements in both Signal‐to‐Noise Ratio (SNR) and waveform coherence. Evaluations on real‐world DAS data further confirm the robustness of our method, positioning it as a valuable tool for analyzing large‐scale DAS data sets in various geoscientific contexts. This approach, which we refer to as “CoherentNoise2Self” to emphasize the extension of Noise2Self to coherent noise, advances the capabilities for near real‐time monitoring and analysis of seismic DAS data.

In depth sequencing of a serially sampled household cohort reveals the within-host dynamics of Omicron SARS-CoV-2 and rare selection of novel spike variants.

SARS-CoV-2 has undergone repeated and rapid evolution to circumvent host immunity. However, outside of prolonged infections in immunocompromised hosts, within-host positive selection has rarely been detected. The low diversity within-hosts and strong genetic linkage among genomic sites make accurately detecting positive selection difficult. Longitudinal sampling is a powerful method for detecting selection that has seldom been used for SARS-CoV-2. Here we combine longitudinal sampling with replicate sequencing to increase the accuracy of and lower the threshold for variant calling. We sequenced 577 specimens from 105 individuals from a household cohort primarily during the BA.1/BA.2 variant period. There was extremely low diversity and a low rate of divergence. Specimens had 0-12 intrahost single nucleotide variants (iSNV) at >0.5% frequency, and the majority of the iSNV were at frequencies <2%. Within-host dynamics were dominated by genetic drift and purifying selection. Positive selection was rare but highly concentrated in spike. Two individuals with BA.1 infections had S:371F, a lineage defining substitution for BA.2. A Wright Fisher Approximate Bayesian Computational model identified positive selection at 14 loci with 7 in spike, including S:448 and S:339. We also detected significant genetic hitchhiking between synonymous changes and nonsynonymous iSNV under selection. The detectable immune-mediated selection may be caused by the relatively narrow antibody repertoire in individuals during the early Omicron phase of the SARS-CoV-2 pandemic. As both the virus and population immunity evolve, understanding the corresponding shifts in SARS-CoV-2 within-host dynamics will be important.

Development of RT-qPCR assay for assessing the expression of ACTB and SDHA housekeeping genes in the cell cultures of mammalian hosts of zoonotic infections

Background. The molecular mechanisms behind the maintenance of zoonotic pathogens in nature can be better understood by examining the gene expression in host cells in response to the infection. Reverse transcription and quantitative polymerase chain reaction (RT-qPCR) is a powerful method to study gene expression, especially with the use of endogenous reference genes (RG) to normalize the data. Therefore, it is critical to develop the reliable qRT-PCR assay and validate that selected RGs are stably expressed in the studied organism or cell culture.The aim. In this work, we aimed to develop the real-time qRT-PCR method for detecting mRNA of two candidate RGs, succinate dehydrogenase subunit A (SDHA) and beta-actin (ACTB), in the cell lines of mammalian hosts of zoonotic infections.Materials and methods. The SPEV (porcine embryonic kidney cell line) and ApnK (Korean field mouse kidney cell line) cells were grown in 24-well culture plates. Total RNA/DNA was isolated from trypsin-detached cell monolayers. Genomic DNA in the samples was removed with RNase-free DNase I, and one-step RT-qPCR was performed using primers for SDHA and ACTB gene fragments and the corresponding TaqMan hydrolysis probes. The experiment was performed in 4 independent replicates.Results. In the Korean field mouse cells, the linearity, efficiency, repeatability, and reproducibility of the RT-qPCR for ACTB gene mRNA corresponded to the modern requirements. However, RT-qPCR for SDHA exhibited good linearity and efficiency of the reaction, but CV values for repeatability and reproducibility slightly exceeded the recommended standards. In porcine cells, both assays had acceptable parameters. Thus, to use the SDHA as RG for ApnK cells, a detailed study of the stability of its expression in this particular model is required.Conclusions. New qRT-PCR assay was developed to assess the expression of housekeeping genes ACTB and SDHA in the cells of the Korean field mouse and domestic pig. Further research is necessary to validate these genes as references for quantitative assessment of gene expression in the cells of mammalian hosts of zoonotic infections.

Distribution law analysis and calculating method for windage power in a geotechnical centrifuge

Distribution law analysis and calculating method for windage power in a geotechnical centrifuge

Mechanics‐Photophysics Correlation in Tough, Stretchable and Long‐Lived Room Temperature Phosphorescence Ionogels Deciphered by Dynamic Mechanical Analysis

The development of tough, stretchable and long‐lived room temperature phosphorescence (RTP) materials holds great significance for manufacturing and processing photoluminescent materials, but limited techniques are available to profile their mechanics‐photophysics correlation. Here we report glassy ionogels, and their mechanical properties and photophysical properties are fused by dynamic mechanical analysis (DMA), functioning like a human brain that perceives a material instantaneously by linking sensory perception and cognition. Depending on two special temperatures presented in DMA curves, Tloss (the peak of loss modulus (E”)) and Tg (glass transition temperature), the ionogels can vary from being either tough with persistent phosphorescence, extensible with effective phosphorescence or resilience with inefficient phosphorescence. Leveraging this method, we achieve stretchable and long‐lived RTP ionogels with tensile yield strength of 53 MPa, tensile strain of 497%, Young’s modulus of 782 MPa, toughness of 111.2 MJ/m3, and lifetime of 113.05 ms. Our work provides a simple yet powerful method to reveal the mechanics‐photophysics correlation of RTP ionogels, to predict their performance without laborious synthesis and characterization, opening new avenues for applications of RTP materials, including applications in harsh conditions (257 K or 347 K), shape memory and shape reconstruction.

Sculpting Mechanical Properties of Hydrogels by Patterning Seamlessly Interlocked Stiff Skeleton

AbstractFunctional soft materials, especially hydrogels have been widely developed to achieve various soft structures and machines. However, synthetic hydrogels commonly show formula‐dependent mechanical properties to fulfill the requirements of mechanical elasticity, stiffness, toughness, and tearing‐resistance for adapting to complex application scenario. Inspired by heterostructures and materials found in nature such as leaves and insect wings, a sequential photopolymerization process combined with site‐selective patterning exposure is reported to prepare programmable hydrogels with locally heterogeneous reinforcement skeletons, i.e., interpenetrating double networks. The heterogeneous interface between soft matrices and stiff skeletons is seamlessly interlocked through strong multiple hydrogen bonds induced by phase transition. By harnessing the size, shape, and distribution of the patterned stiff skeletons, a wide range of mechanical properties of hydrogels including modulus (0.32–5.92 MPa), toughness (0.15–18 kJ m−2), dissipated energy (1–100 kJ m−3), impact resistance, and mechanical anisotropy can be readily sculpted within one material system without needing design and optimization of the complex and elusive material formulation on demand. It is believed that this simple yet powerful method relying on heterogenous patterning would guide the development of functional hydrogel materials with programmable mechanical properties toward potential engineering applications, such as damping and flexible circuits.

Mechanics-Photophysics Correlation in Tough, Stretchable and Long-Lived Room Temperature Phosphorescence Ionogels Deciphered by Dynamic Mechanical Analysis.

The development of tough, stretchable and long-lived room temperature phosphorescence (RTP) materials holds great significance for manufacturing and processing photoluminescent materials, but limited techniques are available to profile their mechanics-photophysics correlation. Here we report glassy ionogels, and their mechanical properties and photophysical properties are fused by dynamic mechanical analysis (DMA), functioning like a human brain that perceives a material instantaneously by linking sensory perception and cognition. Depending on two special temperatures presented in DMA curves, Tloss (the peak of loss modulus (E")) and Tg (glass transition temperature), the ionogels can vary from being either tough with persistent phosphorescence, extensible with effective phosphorescence or resilience with inefficient phosphorescence. Leveraging this method, we achieve stretchable and long-lived RTP ionogels with tensile yield strength of 53 MPa, tensile strain of 497%, Young's modulus of 782 MPa, toughness of 111.2 MJ/m3, and lifetime of 113.05 ms. Our work provides a simple yet powerful method to reveal the mechanics-photophysics correlation of RTP ionogels, to predict their performance without laborious synthesis and characterization, opening new avenues for applications of RTP materials, including applications in harsh conditions (257 K or 347 K), shape memory and shape reconstruction.

Lifetime, size and emission of laser-induced plasmas for in-situ laser-induced breakdown spectroscopy on Earth, Mars and Moon

The spectroscopic technique of laser-induced breakdown spectroscopy (LIBS) is a powerful method to perform rapid chemical analysis of geologic samples with short measurement times and no need for sample preparation. After the ChemCam instrument aboard NASA’s MSL rover proved its suitability for space missions that explore planetary surfaces in 2012, the interest in LIBS instruments as payloads has grown and several subsequent missions have successfully used this technique since. The characteristics of a LIBS plasma depend on experimental and environmental parameters as well as on sample properties, including atmospheric conditions, laser irradiance and sample lithology. Consequently, LIBS instruments need to be designed and optimized specifically for each use case to maximize their science output. To aid in the development of new LIBS instruments for space exploration, we investigate the influence of atmospheric conditions, laser irradiance and sample lithology on the lifetime, size and emission of laser-induced plasmas. In our measurements, we use a plasma imaging setup with high temporal resolution of down to 2ns to investigate the evolution of the plasma from its ignition to its decay. We present a comparable data set recorded at terrestrial, Martian and airless atmospheric conditions, covering irradiances between 0.79GW/mmˆ2 and 1.43GW/mmˆ2 and samples with diverse properties, namely basalt and soapstone, as well as the lunar regolith simulants LHS-1 and LMS-1. Our measurements show the strong influence of atmospheric conditions on the plasma size and emission, while the lithologies and laser irradiances covered in this work play a minor role. This shows that instruments designed to work at certain atmospheric conditions can be used for a range of laser parameters and sample properties. Furthermore, we demonstrate that the decay of the plasma emission and the expansion of the plasma plume parallel to the sample surface can be described well by a power law and a drag model, respectively.