Spatial Resolution Properties Research Articles

Thermal excitation-induced micro/nano fabrication and material modification of superconducting Nb films

We report the thermal excitation-induced material modification and micro/nano fabrication based on the interactions between nano laser direct writing (NLDW) and superconducting films experimentally and by simulation. The niobium (Nb) films with a critical temperature of 9 K were deposited on silicon substrate via sputtering with the thickness of around 50 nm. The boundary between material modification and micro/nano fabrication was verified by changing the interaction time and laser power continuously. Specifically, as the laser power was fixed at 250 mW and the interaction time below 440 ns, the interaction is material modification. With the increasing interaction time further, the Nb films were etched away. As the interaction time was fixed at 500 ns and the laser power below 200 mW, the interaction is material modification too. With the increasing of laser power further, the Nb films were etched away. In the experiment, the oxygen content and current–voltage characteristic (IVCs) before and after laser irradiation were displayed to verify the material modification, which is in line with the simulation results. Considering the 50 nm resolution of NLDW, in the material modification region, one could trim trilayer junctions, tune shunt resistors, or adjust critical currents, etc. In the micro/nano fabrication region, one could fabricate various devices and exploit the properties of high spatial resolution, high flexibility, and fast processing.

Image quality evaluation in deep-learning-based CT noise reduction using virtual imaging trial methods: Contrast-dependent spatial resolution.

Deep-learning-based image reconstruction and noise reduction methods (DLIR) have been increasingly deployed in clinical CT. Accurate image quality assessment of these methods is challenging as the performance measured using physical phantoms may not represent the true performance of DLIR in patients since DLIR is trained mostly on patient images. In this work, we aim to develop a patient-data-based virtual imaging trial framework and, as a first application, use it to measure the spatial resolution properties of a DLIR method. The patient-data-based virtual imaging trial framework consists of five steps: (1) insertion of lesions into projection domain data using the acquisition geometry of the patient exam to simulate different lesion characteristics; (2) insertion of noise into projection domain data using a realistic photon statistical model of the CT system to simulate different dose levels; (3) creation of DLIR-processed images from projection or image data; (4) creation of ensembles of DLIR-processed patient images from a large number of noise and lesion realizations; and (5) evaluation of image quality using ensemble DLIR images. This framework was applied to measure the spatial resolution of a ResNet based deep convolutional neural network (DCNN) trained on patient images. Lesions in a cylindrical shape and different contrast levels (-500, -100, -50, -20, -10 HU) were inserted to the lower right lobe of the liver in a patient case. Multiple dose levels were simulated (50%, 25%, 12.5%). Each lesion and dose condition had 600 noise realizations. Multiple reconstruction and denoising methods were used on all the noise realizations, including the original filtered-backprojection (FBP), iterative reconstruction (IR), and the DCNN method with three different strength setting (DCNN-weak, DCNN-medium, and DCNN-strong). Mean lesion signal was calculated by performing ensemble averaging of all the noise realizations for each lesion and dose condition and then subtracting the lesion-present images from the lesion absent images. Modulation transfer functions (MTFs) both in-plane and along the z-axis were calculated based on the mean lesion signals. The standard deviations of MTFs at each condition were estimated with bootstrapping: randomly sampling (with replacement) all the DLIR/FBP/IR images from the ensemble data (600 samples) at each condition. The impact of varying lesion contrast, dose levels, and denoising strengths were evaluated. Statistical analysis with paired t-test was used to compare the z-axis and in-plane spatial resolution of five algorithms for five different contrasts and three dose levels. The in-plane and z-axis spatial resolution degradation of DCNN becomes more severe as the contrast or radiation dose decreased, or DCNN denoising strength increased. In comparison with FBP, a 59.5% and 4.1% reduction of in-plane and z-axis MTF (in terms of spatial frequencies at 50% MTF), respectively, was observed at low contrast (-10 HU) for DCNN with the highest denoising strength at 25% routine dose level. When the dose level reduces from 50% to 12.5% of routine dose, the in-plane and z-axis MTFs reduces from 92.1% to 76.3%, and from 98.9% to 95.5%, respectively, at contrast of -100 HU, using FBP as the reference. For most conditions of contrasts and dose levels, significant differences were found among the five algorithms, with the following relationship in both in-plane and cross-plane spatial resolution: FBP>DCNN-Weak>IR>DCNN-Medium>DCNN-Strong. The spatial resolution difference among algorithms decreases at higher contrast or dose levels. A patient-data-based virtual imaging trial framework was developed and applied to measuring the spatial resolution properties of a DCNN noise reduction method at different contrast and dose levels using real patient data. As with other non-linear image reconstruction and post-processing techniques, the evaluated DCNN method degraded the in-plane and z-axis spatial resolution at lower contrast levels, lower radiation dose, and higher denoising strength.

Monitoring Spatial–Temporal Variations in River Width in the Aral Sea Basin with Sentinel-2 Imagery

Rivers in arid regions serve as crucial freshwater resources for local communities and play an essential role in global hydrological and biogeochemical cycles. The Aral Sea Basin (ASB) in Central Asia is characterized by an arid climate and river dynamics that are sensitive to climate change and human activities. Monitoring the spatiotemporal variations in river water extent in the ASB is essential to maintain an ecological balance and ensure water security. In this study, we extracted data regarding monthly river water bodies in the ASB from 2017 to 2022 by synthesizing monthly Sentinel-2 images. The water extents on the Sentinel images were automatically mapped using the Otsu method, and the river widths for all river channels were calculated using the RivWidth algorithm. We investigated the relationships between the river dynamics and the geomorphology, climatic change, human activities, and the annual and interannual variations in the river width in different reaches of the basin. The results show a seasonal variability in the river width, with most rivers reaching the largest width in the warm season and a few rivers in the middle and lower areas reaching the valley value in the warm season. Compared to their tributaries, the mainstem in the middle/lower regions showed less seasonal variability. According to interannual analysis, most of the rivers in the ASB significantly narrowed between 2017 and 2022, a phenomenon which is generally impacted by temperature and evapotranspiration variations. Comparisons show that our results provide improved information about the narrow river reaches and denser river networks compared to the previous global dataset, demonstrating the advantageous properties of high spatial resolution in Sentinel-2 imagery.

Validating computer applications for calculating spatial resolution and noise property in CT using simulated images with known properties.

The purpose of this study was to evaluate, using simulated images with known property values, how accurately some computer applications for calculating modulation transfer function (MTF), task transfer function (TTF), or noise power spectrum (NPS) in computed tomography (CT) based on widely known techniques produce their results. Specifically, they were three applications applicable to the wire method for MTF calculation, two applications corresponding to the circular edge (CE) and linear edge (LE) methods for TTF, and one application using a two-dimensional Fourier transform for NPS, which are collectively integrated with the software 'CTmeasure' provided by the Japanese Society of CT Technology. Images for the calculation with radial symmetry were generated based on a roll-off type filter function. The accuracy of each application was evaluated by comparing the calculated property with the true one. The calculated MTFs for the wire method accurately matched the true ones with percentage errors of smaller than 1.0%. In contrast, the CE and LE methods presented relatively large errors of up to 50% at high frequencies, whereas the NPS's errors were up to 30%. A closer investigation revealed, however, that these errors were attributable not to the applications but to the insufficiencies in the measurement techniques commonly employed. By improving the measurement conditions to minimize the effects of the insufficiencies, the errors notably decreased, whichvalidated the calculation techniques in the applications we used.

Image quality improvement of a one-step spectral CT reconstruction on a prototype photon-counting scanner

Objective. X-ray spectral computed tomography (CT) allows for material decomposition (MD). This study compared a one-step material decomposition MD algorithm with a two-step reconstruction MD algorithm using acquisitions of a prototype CT scanner with a photon-counting detector (PCD). Approach. MD and CT reconstruction may be done in two successive steps, i.e. decompose the data in material sinograms which are then reconstructed in material CT images, or jointly in a one-step algorithm. The one-step algorithm reconstructed material CT images by maximizing their Poisson log-likelihood in the projection domain with a spatial regularization in the image domain. The two-step algorithm maximized first the Poisson log-likelihood without regularization to decompose the data in material sinograms. These sinograms were then reconstructed into material CT images by least squares minimization, with the same spatial regularization as the one step algorithm. A phantom simulating the CT angiography clinical task was scanned and the data used to measure noise and spatial resolution properties. Low dose carotid CT angiographies of 4 patients were also reconstructed with both algorithms and analyzed by a radiologist. The image quality and diagnostic clinical task were evaluated with a clinical score. Main results. The phantom data processing demonstrated that the one-step algorithm had a better spatial resolution at the same noise level or a decreased noise value at matching spatial resolution. Regularization parameters leading to a fair comparison were selected for the patient data reconstruction. On the patient images, the one-step images received higher scores compared to the two-step algorithm for image quality and diagnostic. Significance. Both phantom and patient data demonstrated how a one-step algorithm improves spectral CT image quality over the implemented two-step algorithm but requires a longer computation time. At a low radiation dose, the one-step algorithm presented good to excellent clinical scores for all the spectral CT images.

Clinical Applications of Photon-counting CT: A Review of Pioneer Studies and a Glimpse into the Future.

CT systems equipped with photon-counting detectors (PCDs), referred to as photon-counting CT (PCCT), are beginning to change imaging in several subspecialties, such as cardiac, vascular, thoracic, and musculoskeletal radiology. Evidence has been building in the literature underpinning the many advantages of PCCT for different clinical applications. These benefits derive from the distinct features of PCDs, which are made of semiconductor materials capable of converting photons directly into electric signal. PCCT advancements include, among the most important, improved spatial resolution, noise reduction, and spectral properties. PCCT spatial resolution on the order of 0.25 mm allows for the improved visualization of small structures (eg, small vessels, arterial walls, distal bronchi, and bone trabeculations) and their pathologies, as well as the identification of previously undetectable anomalies. In addition, blooming artifacts from calcifications, stents, and other dense structures are reduced. The benefits of the spectral capabilities of PCCT are broad and include reducing radiation and contrast material dose for patients. In addition, multiple types of information can be extracted from a single data set (ie, multiparametric imaging), including quantitative data often regarded as surrogates of functional information (eg, lung perfusion). PCCT also allows for a novel type of CT imaging, K-edge imaging. This technique, combined with new contrast materials specifically designed for this modality, opens the door to new applications for imaging in the future.

Comprehensive evaluation and new recommendations in the use of Gafchromic EBT3 film.

Gafchromic film's unique properties of tissue-equivalence, dose-rate independence, and high spatial resolution make it an attractive choice for many dosimetric applications. However, complicated calibration processes and film handling limits its routine use. We evaluated the performance of Gafchromic EBT3 film after irradiation under a variety of measurement conditions to identify aspects of film handling and analysis for simplified but robust film dosimetry. The short- (from 5min to 100h) and long-term (months) film response was evaluated for clinically relevant doses of up to 50Gy for accuracy in dose determination and relative dose distributions. The dependence of film response on film-read delay, film batch, scanner type, and beam energy was determined. Scanning the film within a 4-h window and using a standard 24-h calibration curve introduced a maximum error of 2% over a dose range of 1-40Gy, with lower doses showing higher uncertainty in dose determination. Relative dose measurements demonstrated <1mm difference in electron beam parameters such as depth of 50% of the maximum dose value (R50 ), independent of when the film was scanned after irradiation or the type of calibration curve used (batch-specific or time-specific calibration curve) if the same default scanner was used. Analysis of films exposed over a 5-year period showed that using the red channel led to the lowest variation in the measured net optical density values for different film batches, with doses >10Gy having the lowest coefficient of variation (<1.7%). Using scanners of similar design produced netOD values within 3% after exposure to doses of 1-40Gy. This is the first comprehensive evaluation of the temporal and batch dependence of Gafchromic EBT3 film evaluated on consolidated data over 8years. The relative dosimetric measurements were insensitive to the type of calibration applied (batch- or time-specific) and in-depth time-dependent dosimetric signal behaviors can be established for film scanned outside of the recommended 16-24h post-irradiation window. We generated guidelines based on our findings to simplify film handling and analysis and provide tabulated dose- and time-dependent correction factors to achieve this without reducing the accuracy of dose determination.

Contrast- and noise-dependent spatial resolution measurement for deep convolutional neural network-based noise reduction in CT using patient data.

Deep convolutional neural network (DCNN)-based noise reduction methods have been increasingly deployed in clinical CT. Accurate assessment of their spatial resolution properties is required. Spatial resolution is typically measured on physical phantoms, which may not represent the true performance of DCNN in patients as it is typically trained and tested with patient images and the generalizability of DNN to physical phantoms is questionable. In this work, we proposed a patient-data-based framework to measure the spatial resolution of DCNN methods, which involves lesion- and noise-insertion in projection domain, lesion ensemble averaging, and modulation transfer function measurement using an oversampled edge spread function from the cylindrical lesion signal. The impact of varying lesion contrast, dose levels, and CNN denoising strengths were investigated for a ResNet-based DCNN model trained using patient images. The spatial resolution degradation of DCNN reconstructions becomes more severe as the contrast or radiation dose decreased, or DCNN denoising strength increased. The measured 50%/10% MTF spatial frequencies of DCNN with highest denoising strength were (-500 HU:0.36/0.72 mm-1; -100 HU:0.32/0.65 mm-1; -50 HU:0.27/0.53 mm-1; -20 HU:0.18/0.36 mm-1; -10 HU:0.15/0.30 mm-1), while the 50%/10% MTF values of FBP were almost kept constant of 0.38/0.76 mm-1.

Physical Properties of Small Focal Spot Imaging with Deep Learning Reconstruction in Chest-abdominal Plain CT

The aim of this study was to compare the physical properties of small focal spot imaging with deep learning reconstruction (DLR) and small or large focal spot imaging with hybrid iterative reconstruction (IR) in chest-abdominal plain computed tomography. In small focal spot imaging using DLR and hybrid IR, tube currents were set at 350 mA. For the large focal spot imaging using hybrid IR, the tube current was set at 360, 400, 450, and 500 mA. The spatial frequencies with 50% task transfer function (TTF) for delrin and acrylic were calculated to compare spatial resolution properties for lung and soft tissue in the chest. Additionally, the low-contrast object-specific contrast-to-noise ratio (CNRLO) was measured as noise property was measured for a 7-mm module with a CT value contrast of 10 HU in the abdomen. Spatial frequencies with 50% TTF for delrin and acrylic were found to be greater in small focal spot imaging using DLR compared to those in small and large focal spot imaging using hybrid IR. Moreover, the CNRLO obtained from small focal spot imaging with DLR was also nearly equivalent to that of large focal spot imaging with hybrid IR at tube currents of 450 and 500 mA. In chest-abdominal plain computed tomography, small focal spot imaging with DLR has been demonstrated to exhibit greater spatial resolution properties compared to small and large focal spot imaging with hybrid IR, with equivalent or better noise performance.

A confocal-controlled Raman-LIBS hybrid microscope with high stability and spatial resolution

A novel confocal controlled Raman-LIBS hybrid microscope, with a high spatial resolution and antidrift property, has been developed. The microscope provides valuable compositional and structural analysis and is a powerful tool to study unknown minerals.

Property valuation using machine learning algorithms on statistical areas in Greater Sydney, Australia

Property valuation plays a significant role in urban economics and is of great importance to various stakeholders who interact and shape the city, including property owners, buyers, banks, land developers, real estate agents, local councils and government planning authorities. In the literature, various predictive models have been proposed to automate the calculation of property value, most of which endeavour to factor in the combination of property characteristics, market factors and location-based attributes associated with individual properties use large citywide databases. At the same time, it has been widely acknowledged that regional sub-areas have impacts on property price prediction. Therefore, this paper aims to investigate the performance of various techniques on sub-areas using the Greater Sydney Region as the study area. The sub-area in this paper is defined as the statistical areas (SAs) as defined by the Australian Bureau of Statistics. In particular, two different SA geographies (SA4, SA3) along with the City Level are adopted to understand the spatial dependence which occurs at different levels. With real-world transaction records and data collected from a diverse range of sources, various methods including the traditional hedonic price model (HPM) and popular machine learning (ML) approaches are implemented and evaluated for property price prediction. Two different property markets for residential property are modelled, being for housing stock and apartment (unit) stock. Experimental results show that Random Forest and Gradient Boosting-based methods outperform other approaches in most scenarios and that the high spatial resolution property sub-area (SA3) improved the performance in terms of overall model accuracy. This research provides insights into how sub-area machine learning models can be employed in real estate to characterize property price, and helps understand the influential factors in different local geographical areas for policy-making.

The importance of the ANSI ADA Standard for digital intraoral radiographic systems—a pragmatic approach to quality assurance

The need for quality assurance (QA) for digital dental radiography has existed since the introduction of digital imaging; however, the methods and phantoms required to achieve it were not available. This resulted in a chaotic approach to address QA based largely upon subjective analysis of image quality. The American National Standards Institute (ANSI)/American Dental Association (ADA) Quality Assurance Standard 1094 for Digital Intraoral Radiographic Systems (DIRS) presents a paradigm shift to a scientific and objective method of QA rather than one based on subjective assessments. This standard takes into account the contributions of all components of the digital imaging chain that affect final image quality rather than assessing the various components in isolation. The optimal image is determined for each DIRS through objective analysis of the image quality properties of dynamic range, spatial resolution, and contrast perceptibility. Image optimization, a critical component of a quality assurance program, is the proper balance between diagnostic image quality and radiation dose to the patient. This publication counters disseminated myths and misconceptions with scientific evidence and will help dental practitioners appreciate and understand the benefits of the new ANSI/ADA Standard on QA for DIRS.

Multi-Resolution Collaborative Fusion of SAR, Multispectral and Hyperspectral Images for Coastal Wetlands Mapping

The hyperspectral, multispectral, and synthetic aperture radar (SAR) remote sensing images provide complementary advantages in high spectral resolution, high spatial resolution, and geometric and polarimetric properties, generally. How to effectively integrate cross-modal information to obtain a high spatial resolution hyperspectral image with the characteristics of the SAR is promising. However, due to divergent imaging mechanisms of modalities, existing SAR and optical image fusion techniques generally remain limited due to the spectral or spatial distortions, especially for complex surface features such as coastal wetlands. This paper provides, for the first time, an efficient multi-resolution collaborative fusion method for multispectral, hyperspectral, and SAR images. We improve generic multi-resolution analysis with spectral-spatial weighted modulation and spectral compensation to achieve minimal spectral loss. The backscattering gradients of SAR are guided to fuse, which is calculated from saliency gradients with edge preserving. The experiments were performed on ZiYuan-1 02D (ZY-1 02D) and GaoFen-5B (AHSI) hyperspectral, Sentinel-2 and GaoFen-5B (VIMI) multispectral, and Sentinel-1 SAR images in the challenging coastal wetlands. Specifically, the fusion results were comprehensively tested and verified on the qualitative, quantitative, and classification metrics. The experimental results show the competitive performance of the proposed method.

Luminescence and Structural Characterization of Gd2O2S Scintillators Doped with Tb3+, Ce3+, Pr3+ and F for Imaging Applications

Radiodiagnostic technologies are powerful tools for preventing diseases and monitoring the condition of patients. Medicine and sectors such as industry and research all use this inspection methodology. This field demands innovative and more sophisticated systems and materials for improving resolution and sensitivity, leading to a faster, reliable, and safe diagnosis. In this study, a large characterization of gadolinium oxysulfide (Gd2O2S) scintillator screens for imaging applications has been carried out. Seven scintillator samples were doped with praseodymium (Pr3+), terbium (Tb3+) activators and co-doped with praseodymium, cerium, and fluorine (Gd2O2S:Pr,Ce,F). The sample screens were prepared in the laboratory in the form of high packing density screens, following the methodology used in screen sample preparation in infrared spectroscopy and luminescence. Parameters such as quantum detection efficiency (QDE), energy absorption efficiency (EAE), and absolute luminescence efficiency (ALE) were evaluated. In parallel, a structural characterization was performed, via XRD and SEM analysis, for quality control purposes as well as for correlation with optical properties. Spatial resolution properties were experimentally evaluated via the Modulation Transfer Function. Results were compared with published data about Gd2O2S:Pr,Ce,F screens produced with a standard method of a sedimentation technique. In particular, the ALE rose with the X-ray tube voltage up to 100 kVp, while among the different dopants, Gd2O2S:Pr exhibited the highest ALE value. When comparing screens with different thicknesses, a linear trend for the ALE value was not observed; the highest ALE value was measured for the 0.57 mm thick Gd2O2S:Pr,Ce,F sample, while the best MTF values were found in the thinner Gd2O2S:Pr,Ce,F screen with 0.38 mm thickness.

A Systematic Review Comparing Digital Subtraction Angiogram With Magnetic Resonance Angiogram Studies in Demonstrating the Angioarchitecture of Cerebral Arteriovenous Malformations.

In brain arteriovenous malformations (AVMs), there is mismatched communication between arteries and veins, causing a nidal bed between them. This systematic review explores whether a magnetic resonance angiogram (MRA) can be used as a diagnostic imaging tool instead of a digital subtraction angiogram (DSA).Utilizing PubMed, Cochrane, and Google Scholar, as well as the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for article selection, a literature search was conducted over the past five years.Eleven studies were included, with a majority of the articles suggesting a potential for consideration. Arterial spin labeling (ASL) versus time-of-flight (TOF) scans was a comparison study, in addition to the study on pseudo-continuous arterial spin labeling (pc-ASL), which proved its high sensitivity in comparison with DSA scans. Other studies included quantitative magnetic resonance angiogram (Q-MRA) measuring the blood flow and susceptibility weighted imaging (SWI) modality. Although promising, digital subtraction angiogram (DSA) scans have diagnostic superiority. In addition, articles discussed follow-up magnetic resonance angiogram (MRA) scans after surgery.Overall, digital subtraction angiogram remains the gold standard due to its superior spatial resolution and hemodynamic properties; these are the key limitations of magnetic resonance studies. MRA has demonstrated its ability to reproduce high-quality diagnostic images for arteriovenous malformation (AVM) angioarchitecture; however, coupled with their limitations, not many studies with large sample sizes over longer periods have been conducted, and we urge more research into it.

Dose reduction potential in dual-energy subtraction chest radiography based on the relationship between spatial-resolution property and segmentation accuracy of the tumor area

&lt;p class="Abstract"&gt;&lt;span lang="EN-US"&gt;We investigated the relationship between the spatial-resolution property of soft tissue images and the lesion detection ability using U-net. We aimed to explore the possibility of dose reduction during energy subtraction chest radiography. The correlation between the spatial-resolution property of each dose image and the segmentation accuracy of the tumor area in the four regions where the tumor was placed was evaluated using linear regression analysis. The spatial-resolution property was determined by task-based evaluation, and the task-based modulation transfer function (TTF) was computed as its index. TTFs of the reference dose image and the 75 % dose image showed almost the same frequency characteristics regardless of the location of the tumor, and the Dice coefficient also high. When the tumor was located in the right supraclavicular region and under 50 % dose, the frequency characteristics were significantly reduced, and the Dice coefficient was also low. Our results showed a close relationship between the spatial-resolution property and the segmentation accuracy of tumor area using deep learning in dual-energy subtraction chest radiography. In conclusion, a dose reduction of approximately 25 % compared to the conventional method can be achieved. The limitations are the shape of the simulated mass and the use of chest phantom.&lt;/span&gt;&lt;/p&gt;

Task-specific spatial resolution properties of iterative and deep learning-based reconstructions in computed tomography: Comparison using tasks assuming small and large enhanced vessels

PurposeThe present study aims to evaluate TTFs of deep-learning-based image reconstruction (DLIR) and iterative reconstruction (IR) in computed tomography (CT) using a conventional task with a rod object with a diameter of 30 mm and a newly-proposed task with a wire of 1 mm in diameter, simulating large and small enhanced vessels, respectively. MethodsThe rod or wire phantom made of a material equivalent to diluted iodine that exhibits about 270 Hounsfield unit (HU) was placed inside a 30-cm water phantom. In-plane and z-directional TTFs were measured for the rod using the circular edge (CE) and plane edge (PE) methods, respectively. By using the wire (iodine wire: IW), in-plane and z-directional TTFs were measured using Fourier transform (IW method). TTFs of filtered back projection (FBP), IR, and DLIR of a 256-row CT system and FBP and IR of a 64-row CT system were evaluated with CT dose indices of 10 and 5 mGy. ResultsFor DLIR and IR, TTFs measured using the IW method were notably lower than those using the CE (or PE) method; moreover, they were also lower than those of corresponding FBP, indicating that the small enhanced vessels with a diameter of about 1 mm would be blurred with both DLIR and IR. ConclusionsThe proposed IW method has turned out to be effective to evaluate TTFs for small enhanced vessels, which have not been properly evaluated by the CE or PE method conventionally recommended.

Evidence of synergistic electrocatalysis at a cobalt oxide–graphene interface through nanochemical mapping of scanning transmission X-ray microscopy

Free-standing graphene membranes are a promising candidate for use as in-situ environmental windows in X-ray (electron) microscopy. In this study, graphene membranes were used as the working electrode, and cobalt nanoparticles (Co-NPs) were grown directly on top of the graphene through electrochemical deposition for interfacial variation. The electronic structure and the chemical bonding states of the Co-NPs and the graphene materials were examined by using the high spatial resolution and element-specific properties of scanning transmission X-ray microscopy. X-ray absorption spectra of C, O, and Co revealed that the Co-NP size increased in accordance with the oxidation state (Co0/2+/3+), depending on the configuration of carbon bonding (C–C/C–OH/HO–C=O/O–C(O)–O/C=O–like state). We conducted a spectral comparison of the dipped graphene and the electrodeposited Co–graphene sample, which revealed an increase in C–OH formation before Co-NPs growth. In addition to electron transfer and electrochemical reduction, the oxidation evolution from C–OH to HO–C=O (or a defect) and the O–C(O)–O (or C=O) state paralleled the increase in Co-NPs size. We curve-fitted the results to demonstrate the reduction in chemical structure from mixing Co2+/3+ to Co3+/2+/0, and to explain the interfacial modulation and the unique metal Co0 layer on the surface of the Co-NPs. Our results provide information for the design of a reliable graphene window and offer an example for the interpretation of experimental X-ray (electron) microscopy.

Surface-enhanced infrared absorption spectroscopy for microorganisms discrimination on silver nanoparticle substrates

Extracting molecular level label-free information from complex biological processes for a range of purposes including disease diagnosis and microbial identification and discrimination is always a challenging task. This is mostly due to lack of a technique providing rich molecular information with a high spatial and temporal resolution properties. Two surface-enhanced vibrational spectroscopic (SEVS) techniques, surface-enhanced Raman scattering (SERS) and surface-enhanced infrared absorption spectroscopy (SEIRAS), are recently attracting considerable attention to study biosystems at an interface since they can satisfy these requirements to a certain level by providing rich intrinsic molecular information from molecules and molecular systems in a close proximity of nanostructured noble metal surfaces. In this study, these two surface-enhanced vibrational spectroscopic techniques are comparatively evaluated for the discrimination and identification of Candida albicans (C. albicans), Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus) by paying attention to the source of the observed spectral pattern. The citrate-reduced colloidal silver nanoparticles (AgNPs) were used as substrates. The results show that the SEIRAS provides very rich molecular information about the biomolecular species adsorbed onto AgNPs similar to the case of SERS. The discrimination power of SEIRAS is much improved compared to FTIR demonstrated by PCA analysis. This study suggests that SEIRAS can be a potential technique for microbial analysis.

The use of optically pumped Magnetoencephalography (OP-MEG) in epilepsy

Magnetoencephalography (MEG) has been used in specific cases of refractory epilepsy due to its inherent properties of excellent spatial resolution and relative immunity to muscle artefact. However current MEG systems are high maintenance and restrictive for the patient, meaning that recording sessions are usually brief (1–2 h), thereby limiting its availability in the clinical setting. Our aim was to demonstrate the first use of Optically Pumped Magnetoencephalography (OP-MEG) in two epilepsy patients with unrestricted head movement. Unlike conventional MEG that uses a superconducting SQUID system, here we use a different type of sensor (OPM), which operates at room temperature and crucially can be placed directly on the patient's scalp, permitting free head movement.