Ultra-small Sample Research Articles

Application of Neural Network Models with Ultra-Small Samples to Optimize the Ultrasonic Consolidation Parameters for 'PEI Adherend/Prepreg (CF-PEI Fabric)/PEI Adherend' Lap Joints.

The aim of this study was to optimize the ultrasonic consolidation (USC) parameters for 'PEI adherend/Prepreg (CF-PEI fabric)/PEI adherend' lap joints. For this purpose, artificial neural network (ANN) simulation was carried out. Two ANNs were trained using an ultra-small data sample, which did not provide acceptable predictive accuracy for the applied simulation methods. To solve this issue, it was proposed to artificially increase the learning sample by including additional data synthesized according to the knowledge and experience of experts. As a result, a relationship between the USC parameters and the functional characteristics of the lap joints was determined. The results of ANN simulation were successfully verified; the developed USC procedures were able to form a laminate with an even regular structure characterized by a minimum number of discontinuities and minimal damage to the consolidated components.

THE PROGRESS OF 14C-AMS ANALYSIS FOR ULTRA-SMALL SAMPLES AT XI’AN AMS CENTER

ABSTRACT As there exists a growing demand for chronological research and tracer applications using radiocarbon (14C) analyses of samples smaller than 100 μg C, a compact micro-specific hydrogen graphitization method has been developed at the Xi’an Accelerator Mass Spectrometry (AMS) Center. This article describes the performance of the system and the mass of carbon background produced during ultra-small sample preparation. Furthermore, we discuss the results of contamination corrections and perform 14C analyses on small samples with known age or reference values. The results reveal that our 14C analysis of ultra-small samples of 10–100 μg C can obtain accurate and reliable results, and the micro-scale 14C-AMS analysis technique meets our research objectives for dating and tracer applications.

Intracavity dynamics-based gain-assisted sensing with microtubule Raman microlaser

Microcavity lasers show excellent performance as a miniaturized microsensor in various applications. However, their relatively weak power may be easily submerged in system noises and disturbed by environmental fluctuations, rendering them ineffective at detecting small signals for precise sensing. To solve this problem, the laser differential frequency-shift feedback technique is demonstrated in a microtubule Raman laser to achieve the optical gain assistance. When the microlaser is frequency-shift-modulated and returns back to the resonator, the measurement signal can resonate with the laser relaxation oscillation and be significantly enhanced. The intracavity dynamics-based enhancement makes it effective for increasing intensity changes caused by analytes. Small signals that would otherwise be buried in system noises and go undetected can be more easily resolved. In addition, the microsensor reduces the spectral measurement range and offers a way to observe the fast dynamic response. Based on that, a measurement resolution of 50 nm nanoparticle detection limit and a refractive index noise-limited resolution of 8.18 × 10−7 refractive index unit (RIU) are demonstrated. The dynamic phase transition of thermosensitive hydrogel is further investigated as a validation of its rapid detection capability. Integrated with an inherent microfluidic channel, the proposed microsensor provides a direct interaction between analytes and probe light with ultrasmall sample consumption down to 50 pl. It is expected to boost the detection of weak signals in microlasers and enlighten the development of optofluidic microsensors in exploring diverse biochemical processes.

Improving Every Image: HexAuFoil® Ultra-small Hole Sample Supports for CryoEM Reconstructions.

Improving Every Image: HexAuFoil® Ultra-small Hole Sample Supports for CryoEM Reconstructions.

Label-free and specific detection of active Botulinum neurotoxin in 0.5 μL drops with the meta-nano-channel field-effect biosensor

Botulinum neurotoxins (BoNT) are the most poisonous and lethal substances known to humans, and as such there is a great need for fast, easy-to-use, and early BoNT detection technologies which are not available today. A biosensor based on field-effect transistor (FET) is a highly pursued sensing platform due to the inherent low-noise and amplification, low-cost, and the ultimate potential for the sensing of multiple analytes from an ultra-small sample volume. In the current study we employ the novel Meta-Nano-Channel (MNC) FET biosensor for specific and label-free sensing of BoNT. The MNC biosensor is a multi-channel device which provides the means to optimize the coupling between the biological interactions and the readout sensing current, by applying a tight control over both the electrodynamics of the sensing current and the electrostatics of the biological interactions. Importantly, the MNC FET biosensor presents excellent stability, repeatability and robustness in wet conditions. Finally, the multi-channel approach of the MNC biosensor allows for tunable sensing performance in terms of limit-of-detection, sensitivity and dynamic range.

Diagnosis of Parkinson's Disease via the Metabolic Fingerprint in Saliva by Deep Learning.

Parkinson's disease (PD) is the second cause of the neurodegenerative disorder, affecting over 6 million people worldwide. The World Health Organization estimated that population aging will cause global PD prevalence to double in the coming 30 years. Optimal management of PD shall start at diagnosis and requires both a timely and accurate method. Conventional PD diagnosis needs observations and clinical signs assessment, which are time-consuming and low-throughput. A lack of body fluid diagnostic biomarkers for PD has been a significant challenge, although substantial progress has been made in genetic and imaging marker development. Herein, a platform that noninvasively collects saliva metabolic fingerprinting (SMF) by nanoparticle-enhanced laser desorption-ionization mass spectrometry with high-reproducibility and high-throughput, using ultra-small sample volume (down to 10 nL), is developed. Further, excellent diagnostic performance is achieved with an area-under-the-curve of 0.8496 (95% CI: 0.7393-0.8625) by constructing deep learning model from 312 participants. In conclusion, an alternative solution is provided for the molecular diagnostics of PD with SMF and metabolic biomarker screening for therapeutic intervention.

High-Throughput Nanoliter Sampling and Direct Analysis of Biological Fluids Using Droplet Imbibition Mass Spectrometry.

A high-throughput droplet imbibition mass spectrometry (MS) experiment is reported for the first time that allows direct analysis of ultra-small volumes of complex mixtures. In this experiment, an array of optimized tips of glass capillaries containing the analyte solution is sampled by rapidly moving charged microdroplets, which picks up (i.e., imbibes) the analyte and transfers it to a proximal mass spectrometer. The advantages associated with this droplet imbibition experiment include (1) ultra-small sample consumption (1.3 nL/min), which reduces the matrix effect in complex mixture analysis, and (2) high surface activity, which eliminates ion suppression effects caused by competition for the space charge on the droplet surface. Collectively, the enhanced surface effect and small flow rates dramatically increase the sensitivity of the droplet imbibition MS approach. This was experimentally shown by constructing calibration curves for cocaine analysis in human raw urine and whole blood, achieving 2 and 7 pg/mL limits of detection, respectively. The high-throughput feature was demonstrated by analyzing five structurally different compounds in 20 s intervals. With the measured flow rate of 1.3 nL/min on a 5 μm glass tip size, the results described in the current study showcase droplet imbibition MS to be a powerful and high-throughput alternative for conventional nano-electrospray ionization (flow rate <100 nL/min), which is the most efficient method for transferring small sample volumes to mass spectrometers.

Multiattention Joint Convolution Feature Representation With Lightweight Transformer for Hyperspectral Image Classification

Hyperspectral image (HSI) classification is currently a hot topic in the field of remote sensing. The goal is to utilize the spectral and spatial information from HSI to accurately identify land covers. Convolution neural network (CNN) is a powerful approach for HSI classification. However, CNN has limited ability to capture non-local information to represent complex features. Recently, vision transformers (ViTs) have gained attention due to their ability to process non-local information. Yet, under the HSI classification scenario with ultra-small sample rates, the spectral-spatial information given to ViTs for global modeling is insufficient, resulting in limited classification capability. Therefore, in this article, Multi-Attention Joint Convolution Feature Representation with Lightweight Transformer (MAR-LWFormer) is proposed, which effectively combines the spectral and spatial features of HSI to achieve efficient classification performance at ultra-small sample rates. Specifically, we use a three-branch network architecture to extract multi-scale convolved 3D-CNN, EMAP, and LBP features of HSI, respectively, by taking full exploitation of ultra-small training samples. Second, we design a series of multi-attention modules to enhance spectral-spatial representation for the three types of features and to improve the coupling and fusion of multiple features. Third, we propose an explicit feature attention tokenizer to transform the feature information, which maximizes the effective spectral-spatial information retained in the flat tokens. Finally, the generated tokens are input to the designed lightweight transformer for encoding and classification. Experimental results on three datasets validate that MAR-LWFormer has an excellent performance in HSI classification at ultra-small sample rates when compared to several state-of-the-art classifiers.

Applications of capillary electrophoresis in the fields of environmental, pharmaceutical, clinical, and food analysis (2019-2021).

So far, the potential of capillary electrophoresis in the application fields has been increasingly excavated due to the advantages of simple operation, short analysis time, high-resolution, less sample consumption, and low cost. This review examines the implementations and advancements of capillary electrophoresis in different application fields (environmental, pharmaceutical, clinical, and food analysis) covering the literature from 2019 to 2021. In addition, ultrasmall sample injection volume (nanoliter range) and short optical path lead to relatively low concentration sensitivity of the most frequently used ultraviolet-absorption spectrophotometric detection, so the pretreatment technology being developed has been gradually utilized to overcome this problem. Despite the review being focused on the development of capillary electrophoresis in the fields of environmental, pharmaceutical, clinical, and food analysis, the new sample pretreatment techniques of microextraction and enrichment fit excellently to capillary electrophoresis in recent three years are also described briefly.

Single-molecule optofluidic microsensor with interface whispering gallery modes.

Label-free sensors are highly desirable for biological analysis and early-stage disease diagnosis. Optical evanescent sensors have shown extraordinary ability in label-free detection, but their potentials have not been fully exploited because of the weak evanescent field tails at the sensing surfaces. Here, we report an ultrasensitive optofluidic biosensor with interface whispering gallery modes in a microbubble cavity. The interface modes feature both the peak of electromagnetic-field intensity at the sensing surface and high-Q factors even in a small-sized cavity, enabling a detection limit as low as 0.3 pg/cm2 The sample consumption can be pushed down to 10 pL due to the intrinsically integrated microfluidic channel. Furthermore, detection of single DNA with 8 kDa molecular weight is realized by the plasmonic-enhanced interface mode.

Development of Ultranarrow‐Bore Open Tubular High Efficiency Liquid Chromatography

Comprehensive SummaryChromatography theory shows that when the inner diameter of the chromatography column is very narrow (e.g., 1—2 μm), the maximum efficiency will be produced in liquid chromatography. In addition, the increasing demand for ultrasmall volume sample analysis has produced the trend of narrowing the diameter of the liquid chromatography column. Experiments have verified that ultranarrow‐bore open tubular liquid chromatography (i.d. ≤ 2 μm) has the advantages of small sample requirement and high separation efficiency. However, there also exist huge challenges along with such obvious advantages. This review summarizes the efforts made by our research group and other research groups to develop this field. We hope that in the near future, ultranarrow bore open‐tube liquid chromatography can be successfully and maturely applied to the application of analytes with ultrasmall size and volume such as single‐cell and even subcellular organelles omics research. What is the most favorite and original chemistry developed in your research group?Our research work originated from nanocapillary chromatographic analysis, and then expanded to micro/nanoscale bio‐separation analysis, micro/nanofluidic analysis, and the development of new micro/nano analytical instruments. Currently, our research focuses on single‐cell analysis with temporal and spatial resolution.What is the most important personality for scientific research?Having an enquiring mind; Be diligent, curious, and persistent; Pursuing perfection.What's your hobbies? What's your favorite book(s)?I enjoy cooking and playing Ping‐Pong. My favorite book is The Art of War.Who influences you mostly in your life?My parents.What is your favorite journal(s)?Journal of the American Chemical Society, which is the top international academic journal in the field of chemistry and values novelty.Could you please give us some advices on improving Chinese Journal of Chemistry?Pursue high quality, highlight the characteristics, strive for excellence, enable fast review and publication.

Single‐Cell Metabolomics by Mass Spectrometry: Opportunities and Challenges

AbstractCellular heterogeneity is an inherent property of cell populations with a wide spectrum of biological manifestations, ranging from barely observable variations that enhance organismal adaptation, to life‐threatening differences. Single‐cell metabolomics can reveal molecular information and variations in metabolite concentrations between cells that are masked in cell‐population studies. These differences are quantitatively captured by the abundance distributions for the population and their statistical analysis can reveal the presence of latent subpopulations. In recent years, mass spectrometry (MS), combined with novel sampling and ionization techniques, has become an important tool for single‐cell metabolomics. These new techniques must contend with significant challenges in the form of small cell sizes and volumes, ultratrace metabolite amounts per cell, and potentially interfering high turnover rates and rapid diffusion. Owing to the ultrasmall sample volume, low abundance of some metabolites, and poor ionization efficiencies, metabolite detection, identification, and accurate quantitation remain a major challenge. The ability of some techniques to analyze tissue‐embedded cells opens the door for spatial metabolomics, potentially revealing cellular synergism in organ level metabolism. In this Concept article, we present a bird's eye view of these major themes in single cell metabolomics and some of the relevant MS‐based methods.

Tracking Brownian motion in three dimensions and characterization of individual nanoparticles using a fiber-based high-finesse microcavity

The dynamics of nanosystems in solution contain a wealth of information with relevance for diverse fields ranging from materials science to biology and biomedical applications. When nanosystems are marked with fluorophores or strong scatterers, it is possible to track their position and reveal internal motion with high spatial and temporal resolution. However, markers can be toxic, expensive, or change the object’s intrinsic properties. Here, we simultaneously measure dispersive frequency shifts of three transverse modes of a high-finesse microcavity to obtain the three-dimensional path of unlabeled SiO2 nanospheres with 300 μs temporal and down to 8 nm spatial resolution. This allows us to quantitatively determine properties such as the polarizability, hydrodynamic radius, and effective refractive index. The fiber-based cavity is integrated in a direct-laser-written microfluidic device that enables the precise control of the fluid with ultra-small sample volumes. Our approach enables quantitative nanomaterial characterization and the analysis of biomolecular motion at high bandwidth.

Nanoplasmonic-Nanofluidic Single-Molecule Biosensors for Ultrasmall Sample Volumes.

Detection of small amounts of biological compounds is of ever-increasing importance but also remains an experimental challenge. In this context, plasmonic nanoparticles have emerged as strong contenders enabling label-free optical sensing with single-molecule resolution. However, the performance of a plasmonic single-molecule biosensor is not only dependent on its ability to detect a molecule but equally importantly on its efficiency to transport it to the binding site. Here, we present a theoretical study of the impact of downscaling fluidic structures decorated with plasmonic nanoparticles from conventional microfluidics to nanofluidics. We find that for ultrasmall picolitre sample volumes, nanofluidics enables unprecedented binding characteristics inaccessible with conventional microfluidic devices, and that both detection times and number of detected binding events can be improved by several orders of magnitude. Therefore, we propose nanoplasmonic–nanofluidic biosensing platforms as an efficient tool that paves the way for label-free single-molecule detection from ultrasmall volumes, such as single cells.

Nanochannel chromatography and photothermal optical diffraction: Femtoliter sample separation and label-free zeptomole detection

Column miniaturization of liquid chromatography is a major trend in separation sciences with the advent of single-cell proteomics and metabolomics. Nanochannel chromatography is one of the promising tools for single-cell analyses because it provides ultra-small sample volume and high separation efficiency. However, non-fluorescent molecular detection in such small channels is still quite difficult due to fL–aL sample volume, which hinders further miniaturization of nanochannels. In this study, we overcame the size limitation of nanochannel chromatography by our label-free molecular detection method: photothermal optical diffraction (POD), which utilizes the photothermal effect of analytes and optical diffraction by nanochannel. The combination of the nanochannel chromatography and the POD enables 1.8 fL sample separation and label-free molecule detection in nanochannels with 800 nm width and 300 nm depth at the optimized experimental conditions. The limit of detection is 5.4 zmol (3300 molecules), approximately 50 times lower than the conventional label-free detection method. Furthermore, the theoretical plate number is calculated to be 105 plates/m, and the separation performance is discussed. Our label-free detection method will be widely used as a universal detector for nanochannel chromatography.

Controllable fabrication of pico/femtoliter pipette sampling probes and visual sample volume determination

We propose a hydraulically assisted eddy-current etching method for the controllable fabrication of pico/femtoliter sampling probes with equal inner diameters along the length of the probe. The relative standard deviations of the outer and inner diameters (O.D. and I.D., respectively) of several 1.07-μm-I.D. sampling probe tips fabricated in a single batch using this method were 2.2% and 2.8%, respectively, and the average O.D./I.D. ratio of these probes was 1.17. The probe fabrication method has high reproducibility, and sharp tips are produced, which is advantageous for the transfer of ultra-small sample volumes. Further, the narrow, equal diameter, cylindrical inner bore allows the online, visual determination of the pipetted sample volume by utilizing a microscopic imaging system to measure the liquid length. This value is converted linearly to the pipetted volume, whose measurement error is on a sub-pixel level. Image-based sampling using the fabricated probes was achieved by connecting the probe to an electroosmotic pump, which allowed the controlled pipetting of pico/femtoliter samples. Because the pipette allows samples of the same volume to be measured accurately, the pipette was applied for the semiquantitative mass spectrometry analysis of the metabolites in individual epidermal cells sampled from different parts of Portulaca oleracea plants. The results show that different types of cells have distinct metabolite profiles. Further, the experiments showed that, in dopamine-rich leaves, the content of dopamine in the petioles is higher than that in the foliage. The probe fabrication strategy opens new avenues for the controllable pipetting of ultra-small volume samples and the realization of the visual pipetting of pico/femtoliter samples.

Higher-order normal approximation approach for highly reliable system assessment

In this study, the issue of system reliability assessment (SRA) based on component failure data is considered. In industrial statistics, the delta method has become a popular approach for confidence interval approximation. However, for high reliability systems, usually the assessment is confronted with very limited component sample size, variant multi-parameter lifetime models, and complex system structure. Along with strict requirement on assessment accuracy and computational efficiency, existing approaches barely work under these circumstances. In this article, a normal approximation approach is proposed for determining the lower confidence limit of system reliability using components’ time-to-failure data. The polynomial adjustment method is adopted to construct higher-order approximate confidence limit. The main contribution of this work is constructing an integrated methodology for SRA. Specifically, a reliability-based Winterbottom-extended Cornish-Fisher (R-WCF) expansion method for log-location-scale family is elaborated. The proposed methodology exceeds the efficient limitation of Cramer Rao’s theory. Numerical studies are conducted to illustrate the effectiveness of the proposed approach, and results show that the R-WCF approach is more efficient than the delta method for highly reliable system assessment, especially with ultra-small sample size. Supplementary materials are available for this article. Go to the publisher’s online edition of IISE Transactions.

Sensitive and ultrasmall sample volume gas sensor based on a sealed slot waveguide.

Taking advantage of the near-infrared (IR) absorption characteristics of gases, a sensor with an ultrasmall sample volume composed of a sealed slot waveguide and based on evanescent field absorption is proposed in this paper. Compared with a traditional open-slot waveguide, it features small volume antiparticles depositing pollution over the long-term and is insensitive to surroundings. Working at 1645nm, a large evanescent field ratio of 0.27 is obtained by simulation and optimization; meanwhile, the propagation loss is around 1.6dB/cm. The needed sample volume of the designed sensor under the structure parameters of w_air=40 nm, h_air=400 nm, and waveguide length=3 cm is approximately 480 μm3, which helps the sensor demonstrate excellent performance for gas analysis with an ultrasmall sample volume.

Decoder calibration with ultra small current sample set for intracortical brain–machine interface

Objective. Intracortical brain–machine interfaces (iBMIs) aim to restore efficient communication and movement ability for paralyzed patients. However, frequent recalibration is required for consistency and reliability, and every recalibration will require relatively large most current sample set. The aim in this study is to develop an effective decoder calibration method that can achieve good performance while minimizing recalibration time. Approach. Two rhesus macaques implanted with intracortical microelectrode arrays were trained separately on movement and sensory paradigm. Neural signals were recorded to decode reaching positions or grasping postures. A novel principal component analysis-based domain adaptation (PDA) method was proposed to recalibrate the decoder with only ultra small current sample set by taking advantage of large historical data, and the decoding performance was compared with other three calibration methods for evaluation. Main results. The PDA method closed the gap between historical and current data effectively, and made it possible to take advantage of large historical data for decoder recalibration in current data decoding. Using only ultra small current sample set (five trials of each category), the decoder calibrated using the PDA method could achieve much better and more robust performance in all sessions than using other three calibration methods in both monkeys. Significance. (1) By this study, transfer learning theory was brought into iBMIs decoder calibration for the first time. (2) Different from most transfer learning studies, the target data in this study were ultra small sample set and were transferred to the source data. (3) By taking advantage of historical data, the PDA method was demonstrated to be effective in reducing recalibration time for both movement paradigm and sensory paradigm, indicating a viable generalization. By reducing the demand for large current training data, this new method may facilitate the application of intracortical brain–machine interfaces in clinical practice.

Subnanogram proteomics: Impact of LC column selection, MS instrumentation and data analysis strategy on proteome coverage for trace samples

One of the greatest challenges for mass spectrometry (MS)-based proteomics is the limited ability to analyze small samples. Here we investigate the relative contributions of liquid chromatography (LC), MS instrumentation and data analysis methods with the aim of improving proteome coverage for sample sizes ranging from 0.5ng to 50ng. We show that the LC separations utilizing 30-μm-i.d. columns increase signal intensity by >3-fold relative to those using 75-μm-i.d. columns, leading to 32% increase in peptide identifications. The Orbitrap Fusion Lumos MS significantly boosted both sensitivity and sequencing speed relative to earlier generation Orbitraps (e.g., LTQ-Orbitrap), leading to a ∼3-fold increase in peptide identifications and 1.7-fold increase in identified protein groups for 2ng tryptic digests of the bacterium S. oneidensis. The Match Between Runs algorithm of open-source MaxQuant software further increased proteome coverage by ∼95% for 0.5ng samples and by ∼42% for 2ng samples. Using the best combination of the above variables, we were able to identify >3000 proteins from 10ng tryptic digests from both HeLa and THP-1 mammalian cell lines. We also identified >950 proteins from subnanogram archaeal/bacterial cocultures. The present ultrasensitive LC–MS platform achieves a level of proteome coverage not previously realized for ultra-small sample loadings, and is expected to facilitate the analysis of subnanogram samples, including single mammalian cells.