Experimental Configuration Research Articles

Virtual X-ray critical dimension metrology via Monte Carlo simulation.

X-ray critical dimension (XCD) metrology is a highly promising technique for achieving sub-nanometer precision in critical dimension measurements at advanced nodes of integrated circuit manufacturing. Compared to XCD experiments utilizing synchrotron radiation sources, those employing compact X-ray sources encounter challenges like extended testing time and increased uncertainty. To evaluate the influence of experimental conditions on measurement results, we developed an ab initio virtual X-ray critical dimension metrology via a Monte Carlo simulation (MC-VXCD). Through calibrating the system parameters of the MC-VXCD to a home-built compact XCD instrument, we achieved excellent consistency between virtual and actual measurement results. The virtual instrument effectively estimated measurement errors stemming from the reduced exposure time, which significantly influences the measurement accuracy and throughput. Furthermore, through the MC-VXCD, we establish the connection between the application scenarios of the XCD metrology and the geometry of XCD instruments, offering a versatile platform for the system design, experimental configuration optimization, data analysis, etc., in XCD metrology.

Importance of feature selection stability in the classifier evaluation on high-dimensional genetic data

Classifiers trained on high-dimensional data, such as genetic datasets, often encounter situations where the number of features exceeds the number of objects. In these cases, classifiers typically rely on a small subset of features. For a robust algorithm, this subset should remain relatively stable with minor changes in the training data, such as the replacement of a few samples. While the stability of feature selection is a common focus in studies of feature selection algorithms, it is less emphasized in classifier evaluation, where only metrics such as accuracy are commonly used. We investigate the importance of feature selection stability through an empirical study of four classifiers (logistic regression, support vector machine, convex and piecewise Linear, and Random Forest) on seven high dimensional, publicly available, gene datasets. We measure the stability of feature selection using Lustgarten, Nogueira and Jaccard Index measures. We employed our own cross-validation procedure that guarantees a difference of exactly p objects between any two training sets which allows us to control the level of disturbance in the data. Our results show the existence of a drop in feature selection stability when we increase disturbance in the data in all 28 experiment configurations (seven datasets and four classifiers). The relationship is not linear, but resembles more of a hyperbolic pattern. In the evaluation of the tested classifiers, logistic regression demonstrated the highest stability. It was followed in order by support vector machine, convex and piecewise linear, with Random Forest exhibiting the lowest stability among them. This work provides evidence that all tested classifiers are very sensitive to even small changes in learning data in terms of features used by the model, while showing almost no sensitivity in terms of accuracy. The data and codes to reproduce the results in the article are available publicly on GitHub: https://github.com/tlukaszuk/feature-selection-stability-in-classifier-evaluation.

A simulation framework for preclinical proton irradiation workflow

Objective.The integration of proton beamlines with x-ray imaging/irradiation platforms has opened up possibilities for image-guided Bragg peak irradiations in small animals. Such irradiations allow selective targeting of normal tissue substructures and tumours. However, their small size and location pose challenges in designing experiments. This work presents a simulation framework useful for optimizing beamlines, imaging protocols, and design of animal experiments. The usage of the framework is demonstrated, mainly focusing on the imaging part.Approach.The fastCAT toolkit was modified with Monte Carlo (MC)-calculated primary and scatter data of a small animal imager for the simulation of micro-CT scans. The simulated CT of a mini-calibration phantom from fastCAT was validated against a full MC TOPAS CT simulation. A realistic beam model of a preclinical proton facility was obtained from beam transport simulations to create irradiation plans in matRad. Simulated CT images of a digital mouse phantom were generated using single-energy CT (SECT) and dual-energy CT (DECT) protocols and their accuracy in proton stopping power ratio (SPR) estimation and their impact on calculated proton dose distributions in a mouse were evaluated.Main results.The CT numbers from fastCAT agree within 11 HU with TOPAS except for materials at the centre of the phantom. Discrepancies for central inserts are caused by beam hardening issues. The root mean square deviation in the SPR for the best SECT (90 kV/Cu) and DECT (50 kV/Al-90 kV/Al) protocols are 3.7% and 1.0%, respectively. Dose distributions calculated for SECT and DECT datasets revealed range shifts <0.1 mm, gamma pass rates (3%/0.1 mm) greater than 99%, and no substantial dosimetric differences for all structures. The outcomes suggest that SECT is sufficient for proton treatment planning in animals.Significance.The framework is a useful tool for the development of an optimized experimental configuration without using animals and beam time.

Standardized Daily High‐Resolution Large‐Eddy Simulations of the Arctic Boundary Layer and Clouds During the Complete MOSAiC Drift

AbstractThis study utilizes the wealth of observational data collected during the recent Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) drift experiment to constrain and evaluate close to two‐hundred daily Large‐Eddy Simulations (LES) of Arctic boundary layers and clouds at high resolutions. A standardized approach is adopted to tightly integrate field measurements into the experimental configuration. Covering the full drift represents a step forward from single‐case LES studies, and allows for a robust assessment of model performance against independent data under a range of atmospheric conditions. A homogeneously forced domain is simulated in a Lagrangian frame of reference, initialized with radiosonde and value‐added cloud profiles. Prescribed boundary conditions include various measured surface characteristics. Time‐constant composite forcing is applied, primarily consisting of subsidence rates sampled from reanalysis data. The simulations run for 3 hours, allowing turbulence and clouds to spin up while still facilitating direct comparison to MOSAiC data. Key aspects such as the vertical thermodynamic structure, cloud properties, and surface energy fluxes are well reproduced and maintained. The model captures the bimodal distribution of atmospheric states that is typical of Arctic climate. Selected days are investigated more closely to assess the model's skill in maintaining the observed boundary layer structure. The sensitivity to various aspects of the experimental configuration and model physics is tested. The model input and output are available to the scientific community, supplementing the MOSAiC data archive. The close agreement with observed meteorology justifies the use of LES for gaining further insight into Arctic boundary layer processes and their role in Arctic climate change.

Beam emission spectroscopy diagnostic design and capabilities for two-dimensional turbulence measurements on Wendelstein 7-X

A beam emission spectroscopy (BES) diagnostic is designed for studying two-dimensional turbulent dynamics by measuring the Doppler-shifted Balmer-Alpha emission (n = 3 → 2) from neutral heating beams on Wendelstein 7-X (W7-X) stellarator. The BES viewing geometry has been determined in the conceptual design previously. However, the small Doppler shifts and small optical throughput compared to a typical BES diagnostic demand dedicated efforts on the optical assemblies and the detector module for the BES system. We present the detailed opto-mechanical design and specifications of BES, including a customized neutral beam viewing optical system, a semi-telecentric optical assembly, and a detector module for electronic amplification. The point spread function is calculated using the pyFIDASIM code with experimental parameters and W7-X magnetic configurations to estimate the BES spatial resolution and beam intensity. The as-manufactured interference filter is applied for the spectral isolated beam radiance calculation. Result shows that the BES system is capable of measuring the ion-scale turbulence for k ⊥ ρ i ≤ 0.4 at r/a = 0.75 with reasonable spatial and wavenumber resolutions. An integrated detector module is fabricated where two 8×4 avalanche photodiode detectors (APD) are embedded into the custom-designed pre-amplifier circuit to gain signals to the desired level. The detector noise measurement is performed and the signal-to-noise ratio (SNR) is evaluated. A detectable fluctuation level can be achieved as low as ñ e /n e ≈ 0.5% at frequency f ≤ 400 kHz with a bandwidth of 1 MHz.

Comparison between shear-driven and pressure-driven oscillatory flows over ripples

Direct numerical simulations of oscillatory flow over a bed made of ripples have been performed. Two oscillatory flow forcing mechanisms have been compared: (i) a sinusoidal external pressure gradient (pressure-driven flow); and (ii) a sinusoidal velocity boundary conditions on the rippled bed (shear-driven flow). In the second case, the oscillations of the bed are such that when observed from a reference frame fixed with the bed, the free stream follows the same harmonic oscillation as in the pressure-driven case. While the outer layers have the same dynamics in the two cases, close to the bed differences are observed during the cycle, mostly because the large form drag across the ripples cannot be reproduced in the shear-driven case. A comparison against experimental data from an oscillating tray apparatus provides a relatively good agreement for the phase-averaged flow when the same forcing is considered (i.e. a shear-driven flow). The pressure-driven case has a comparable error to the shear-driven numerical results over the crest of the ripples, whereas the discrepancy is larger at the troughs. The discrepancies between the two cases are more limited for time-averaged flow quantities, such as the mean flow pattern and the time-averaged Reynolds stress distribution. This suggests that numerical or experimental shear-driven configurations may capture well the net effects of coastal transport processes (which occur in pressure-driven oscillatory flow), but care should be exercised in interpreting phase-dependent dynamics near the troughs. More work is needed to fully assess the sensitivity to the forcing mechanisms in different flow regimes.

A hybrid experimental configuration for emissivity measurement at cryogenic temperatures

Abstract In cryogenic and high vacuum systems, radiation mode of heat transfer is the primary source of energy interaction owing to the wide temperature differentials and very low temperatures and pressures. Measurement of radiative properties such as emissivity is crucial in the design of cryogenic systems. Emissivity measurement at cryogenic temperatures requires careful consideration of the sample preparation and measurement conditions to ensure accuracy and reliability. Most experimental techniques developed so far are complex in their design and equipment, and they work only on a single measuring method. To address these challenges, this research has proposed a simpler and hybrid experimental setup that could employ both calorimetric and heat flux methods to directly measure the total hemispherical emissivity in the range 90 K–180 K. Experiments were conducted with five types of materials (black paint, stainless steel, aluminum foil, aluminized mylar foil, and copper sheet) by using both methods, and the results were validated with the literature data. The data obtained by calorimetric and heat flux methods for each sample showed a high level of consistency with each other, with a variation of only ±1.5%. Moreover, the discrepancy between the measured and literature data was within the acceptable range of 0.3%–10%. The total hemispherical emissivity of mechanically treated copper was higher than that of chemically polished copper by 11%. This simplified setup has incorporated all possible means to minimize heat leakages into and from the cryostat while maintaining accuracy. The obtained data can be used to accurately estimate the radiation heat loads in cryopumps and other cryogenic systems that use similar components.

Optimization Methodology of Polar Direct-Drive Illumination for the National Ignition Facility.

Improved laser illumination uniformity drives shocks and implosions to create more extreme high energy density environments. Predominantly, the geometry of experiments that can be performed is dictated by the layout of beams at laser facilities, limiting interfacility and multiscale investigations. This Letter presents the first automated, algorithmic approach for generating illumination configurations for high energy density experiments. The method is demonstrated in comparison to a polar direct-drive solid target experiment at the National Ignition Facility. The new illumination configuration is simulated to create greater than ×3 higher peak pressure and almost ×2 higher density by maintaining better shock uniformity. The optimization process is performed with reduced computational expense and isotropic plasma profiles while accounting for the impact of cross-beam energy transfer.

Optimization of Electrodialysis for Ammonium Removal From NH4Cl-Doped Groundwater Samples Using the Response Surface Method.

This study aims to optimize ammonium removal from NH4Cl-enriched groundwater at different concentrations using an electrodialysis (ED) process. A customized design (CD) based on response surface methodology (RSM) was employed to develop predictive models and improve the performance of the demineralization system. Ion removal efficiency was evaluated in 32 unique experimental configurations, taking into account variations in three input parameters: voltage (A), initial ammonium concentration (B) and demineralization rate (C). These parameters were selected for their impact on two response variables: electric conductivity (Y1) and final ammonium concentration (Y2). An in-depth analysis of variance (ANOVA) was performed to examine the variables and their interactions. The results indicated that Y1 was significantly influenced by C, while Y2 was influenced by B. In addition, the predictive models demonstrated strong correlations, with a coefficient of determination (R2) greater than 0.88 for both response variables. The RSM approach applied to optimize the parameters studied identified the following optimum values: 14.17 V for A, 1 mg/L for B and 70 % for C, giving Y1 of 215.377 μS/cm and Y2 of 0.279 mg/L.

The 4-Octahedra model

This work comprises a generalization of a simple geometric model originally developed to describe coupled rotations of corner-sharing octahedra in the surface layer of Sr2RuO4 under uniaxial compression. The main objective of the model is to establish a link between the experimental global strain configuration and the possible microscopic mechanisms and compatible geometries by which the octahedra accommodate the applied strain. In achieving this, a useful and intuitive parametrization of four-site two-dimensional systems of corner-sharing octahedra has been established, which can be readily extended to three dimensions and N&gt;4 inequivalent sites or directly employed to analyze the octahedral configuration of many perovskites, transition metal oxides (TMOs), and layered compounds.

Elucidating Protein Structures in the Gas Phase: Traversing Configuration Space with Biasing Methods.

Achieving accurate characterization of protein structures in the gas phase continues to be a formidable challenge. To tackle this issue, the present study employs Molecular Dynamics (MD) simulations in tandem with enhanced sampling techniques (methods designed to efficiently explore protein conformations). The objective is to identify suitable structures of proteins by contrasting their calculated Collision Cross-Section (CCS) with those observed experimentally. Significant discrepancies were observed between the initial MD-simulated and experimentally measured CCS values through Ion Mobility-Mass Spectrometry (IMS-MS). To bridge this gap, we employed two distinct enhanced sampling methods, Harmonic Biasing Potential and Adaptive Biasing Force, which help the proteins overcome energy barriers to adopt more compact configurations. These techniques leverage the radius of gyration as a reaction coordinate (guiding parameter), guiding the system toward compressed states that potentially match experimental configurations more closely. The guiding forces are only employed to overcome existing barriers and are removed to allow the protein to naturally arrive at a potential gas phase configuration. The results demonstrated close alignment (within ∼4%) between simulated and experimental CCS values despite using different strengths and/or methods, validating their efficacy. This work lays the groundwork for future studies aimed at optimizing biasing methods and expanding the collective variables used for more accurate gas-phase structural predictions.

Darwinian Evolution of Self-Replicating DNA in a Synthetic Protocell

Replication, heredity, and evolution are characteristic of Life. We and others have postulated that the reconstruction of a synthetic living system in the laboratory will be contingent on the development of a genetic self-replicator capable of undergoing Darwinian evolution. Although DNA-based life dominates, the in vitro reconstitution of an evolving DNA self-replicator has remained challenging. We hereby emulate in liposome compartments the principles according to which life propagates information and evolves. Using two different experimental configurations supporting intermittent or semi-continuous evolution (i.e., with or without DNA extraction, PCR, and re-encapsulation), we demonstrate sustainable replication of a linear DNA template – encoding the DNA polymerase and terminal protein from the Phi29 bacteriophage – expressed in the ‘protein synthesis using recombinant elements’ (PURE) system. The self-replicator can survive across multiple rounds of replication-coupled transcription-translation reactions in liposomes and, within only ten evolution rounds, accumulates mutations conferring a selection advantage. Combined data from next-generation sequencing with reverse engineering of some of the enriched mutations reveal nontrivial and context-dependent effects of the introduced mutations. The present results are foundational to build up genetic complexity in an evolving synthetic cell, as well as to study evolutionary processes in a minimal cell-free system.

Artificial Neural Networks for Mineral Production Forecasting in the In Situ Leaching Process: Uranium Case Study

This study was conducted to assess the applicability of artificial neural networks (ANN) for forecasting the dynamics of uranium extraction over exploitation time during the process of In Situ Leaching (ISL). Currently, ISL process simulation involves multiple steps, starting with geostatistical interpolation, followed by computational fluid dynamics (CFD) and reactive transport simulation. While extensive research exists detailing each of these steps, machine learning techniques may offer the potential to directly obtain extraction curves (i.e., the concentration of the mineral produced over the exploitation time of the deposit), thereby bypassing these computationally expensive steps. As a basis, both an empirical experimental configuration and reactive transport simulations were used to generate training data for the neural network model. An ANN was constructed, trained, and tested on several test cases with different initial parameters, then the expected outcomes were compared to those derived from conventional modeling techniques. The results indicate that for the employed experimental configuration and a limited number of features, artificial intelligence technologies, specifically regression-based neural networks can model the recovery rate (or extraction degree) of the ISL process for mineral production, achieving a high degree of accuracy compared to traditional CFD and mass transport models.

Surface-Enhanced, Low-Frequency Raman Spectroscopy: A Sensitive Screening Tool for Structural Characterization of Pharmaceuticals.

Surface-enhanced, low-frequency Raman spectroscopy (SELFRS) was explored for its potential as a structural screening tool within pharmaceutical applications, including facile small-scale multicomponent analysis. Paracetamol was used as the model drug, and its crystallization behavior with or without the presence of a templating agent (benzoic acid) was investigated using commercial silver-based SERS substrates. The Raman imaging was carried out using two different LFR-enabled instruments employing 532 and 785 nm incident lasers, where each of the setups showed certain affinity for differentiating lattice vibrations of the polymorphic forms of interest: form I and form II. A comparison of SELFRS, SERS, and their combination using chemometrics showed the potential for the LFR spectral range to improve surface-enhanced measurements either individually or in combination with the typically-used fingerprint region without the need to alter the experimental configuration. Additionally, the use of crystallization additives that helped to drive the formation of metastable form II was shown using SELFRS to provide additional mechanistic understanding of the template-assisted crystallization processes.

On the axonal transport of lipid nanoparticles in primary hippocampal neurons

Axonal transport is a crucial process in healthy neurons as it supports the intra-cellular movement of nutrients, endogenous substances, and vesicles regulating a broad set of biological functions. Notably, this physiological mechanism is efficiently exploited by a variety of viruses to infect multiple cells within the central nervous system and, thus, it has been proposed as a strategy to enhance the brain penetrance of macromolecules and nanoparticles. In this work, the retrograde and anterograde transport of lipid nanoparticles (LNP) is systematically analyzed in primary hippocampal neurons cultured in compartmentalized microfluidic chips, where neurites are left to grow within 150 μm-long channels connecting the somal and synaptic compartments. After characterizing the physico-chemical properties, toxicological profile, and cell internalization efficiency, the axonal trafficking of fluorescently labeled LNP was monitored over time via live-cell microscopy. Both naïve LNP and apolipoprotein E-coated LNP (ApoE-LNP) were considered under two different experimental configurations, with the LNP being either added to the somal or the synaptic compartment for anterograde or retrograde transport analyses, respectively. ApoE-LNP only were very efficiently uptaken by neurons and rapidly relocated in a perinuclear position. Also, ApoE-LNP incubated in the somal compartment did not translocate along the neurites (null anterograde transport), whereas ApoE-LNP added to the synaptic compartment were detected near the soma already at 30 min post incubation demonstrating retrograde transport velocities up to ∼ 160 nm/s. This preliminary study suggests that ApoE-LNP could be efficiently used to rapidly transport a variety of therapeutic and imaging cargos from the synaptic cleft to the somal compartment.

High-dimensional quantum key distribution using orbital angular momentum of single photons from a colloidal quantum dot at room temperature

High-dimensional quantum key distribution (HDQKD) is a promising avenue to address the inherent limitations of basic quantum key distribution (QKD) protocols. However, experimental realizations of HDQKD to date have relied on indeterministic photon sources that limit the achievable key rate. In this paper, we demonstrate a full emulation of a HDQKD system using a single colloidal giant quantum dot (gQD) as a deterministic, compact, and room-temperature single-photon source (SPS). We demonstrate a practical protocol by encoding information in a high-dimensional space (d = 3) of the orbital angular momentum of the photons. Our experimental configuration incorporates two spatial light modulators for encoding and decoding the spatial information carried by individual photons. Our experimental demonstration establishes the feasibility of utilizing high radiative quantum yield gQDs as practical SPSs for HDQKD. We also experimentally demonstrate surpassing the traditional d = 2 QKD capacity with comparable error rates, indicating a significant improvement in performance while maintaining reliability.

Raman Microspectroscopy of Hair: Low-Frequency Markers of Protein Secondary Structure.

Low-frequency intervals that can be used to study the secondary structure of proteins are determined. Compared are Ramanspectra of keratins from unpigmented human hair, measured in two experimental configurations: with excitation radiation coaxial with the hair and perpendicular to it. Based on the polarization sensitivity, the bands peaked at 150 and 221 cm-1 are assigned to vibrations of α-helical structures. The comparison of Raman spectra of hair fragments with different contents of secondary structure elements shows that the vibrations of β-structure are manifested in a spectral interval of 270-340 cm-1. The results obtained for a particular object (hair keratin) can be used in the study of the secondary structure of proteins.

Non-resonant background removal in broadband CARS microscopy using deep-learning algorithms

Broadband Coherent anti-Stokes Raman (BCARS) microscopy is an imaging technique that can acquire full Raman spectra (400–3200 cm−1) of biological samples within a few milliseconds. However, the CARS signal suffers from an undesired non-resonant background (NRB), deriving from four-wave-mixing processes, which distorts the peak line shapes and reduces the chemical contrast. Traditionally, the NRB is removed using numerical algorithms that require expert users and knowledge of the NRB spectral profile. Recently, deep-learning models proved to be powerful tools for unsupervised automation and acceleration of NRB removal. Here, we thoroughly review the existing NRB removal deep-learning models (SpecNet, VECTOR, LSTM, Bi-LSTM) and present two novel architectures. The first one combines convolutional layers with Gated Recurrent Units (CNN + GRU); the second one is a Generative Adversarial Network (GAN) that trains an encoder-decoder network and an adversarial convolutional neural network. We also introduce an improved training dataset, generalized on different BCARS experimental configurations. We compare the performances of all these networks on test and experimental data, using them in the pipeline for spectral unmixing of BCARS images. Our analyses show that CNN + GRU and VECTOR are the networks giving the highest accuracy, GAN is the one that predicts the highest number of true positive peaks in experimental data, whereas GAN and VECTOR are the most suitable ones for real-time processing of BCARS images.

A Combined Experimental and Computational Study on the Effect of the Reactor Configuration and Operational Procedures on the Formation, Growth and Dissociation of Carbon Dioxide Hydrate

Clathrate hydrate-based technologies are considered promising and sustainable alternatives for the effective management of the climate change risks related to emissions of carbon dioxide produced by human activities. This work presents a combined experimental and computational investigation of the effects of the operational procedures and characteristics of the experimental configuration, on the phase diagrams of CO2-H2O systems and CO2 hydrates’ formation, growth and dissociation conditions. The operational modes involved (i) the incremental (step-wise) temperature cycling and (ii) the continuous temperature cycling processes, in the framework of an isochoric pressure search method. Also, two different high-pressure PVT configurations were used, of which one encompassed a stirred tank reactor and the other incorporated an autoclave of constant volume with magnetic agitation. The experimental results implied a dependence of the subcooling degree, (P, T) conditions for hydrate formation and dissociation, and thermal stability of the hydrate phase on the applied temperature cycling mode and the technical features of the utilized PVT configuration. The experimental findings were complemented by a thermodynamic simulation model and other calculation approaches, with the aim to resolve the phase diagrams including the CO2 dissolution over the entire range of the applied (P, T) conditions.

Assessing the value of combined use of distributed acoustic sensing (DAS) and multicomponent vertical seismic profiling (VSP) data with network-based full waveform inversion and uncertainty quantification: A case study in Alberta, Canada

Distributed acoustic sensing (DAS) technology, deployed in a vertical seismic profiling (VSP) experimental configuration, has emerged as a candidate for non-disruptive and low-cost seismic monitoring of CO2 geostorage and plume evolution. Full waveform inversion (FWI) has likewise received significant attention because it uses relatively complete physical models of wave propagation and because of its sample-by-sample incorporation of data information. Recent artificial neural network-based FWI algorithms (built with, for instance, recursive neural networks, or RNNs) have added to FWI a range of flexible and efficient tools for gradient computation and options for uncertainty assessment and initial model proxies. An important current research area for the use of DAS data is to better understand how they change our confidence levels in models selected through FWI. In particular, we seek to understand whether either DAS data or conventional geophone data alone are optimal for FWI model selection in the CO2 problem, and if not, to what degree they complement each other. The Snowflake 4D VSP dataset, which includes multi-offset and multi-azimuth broadband sources illuminating both fiberoptic cable and densely sampled accelerometer phones in the borehole, was acquired by our group to directly address these questions. In this study, we quantify uncertainty (UQ) by sampling the posterior model covariance matrix from the inverse Hessian matrix at the end of RNN-FWI runs on the Snowflake baseline data, invoking a velocity-density parameterization, and involving mixtures of accelerometer and DAS data. In this UQ context, the complementary effect of combining accelerometer and DAS data is evident in the Vp and Vs models. Our confidence in the approach is bolstered by its independent prediction of a region of known high uncertainty. In the overall pursuit of reliable and low-cost monitoring tools, this supports continued consideration of a multicomponent sensors supported by DAS approach.