Reflection Intensity Research Articles

Computational ghost imaging with adaptive intensity illumination for scenes featuring specular surfaces

Imaging a target scene with specular surfaces is a daunting challenge for both direct imaging and indirect computational imaging techniques. The intense specular reflection component during the measurement severely degrades the quality of the reconstructed image, resulting in a substantial loss of scene information. To address this issue, we propose a computational ghost imaging (CGI) method with adaptive intensity illumination. Capitalizing on the encoded imaging feature of CGI, this method enables effective imaging of target scenes with specular surfaces through two series of measurements, eliminating the necessity for additional optical components. Based on the position and intensity information of pixels in the specular regions from the first series of measurements, our method modulates the illumination patterns to weaken the intensity of the specular region in the second series of measurements. Simulation and experimental results demonstrate that the utilization of these modulated illumination patterns for target scene measurement effectively mitigates interference from the specular surface during imaging. Consequently, the reconstructed image is capable of presenting more detailed information about the target scene other than the specular regions. Our work introduces a novel approach for imaging target scenes with specular surfaces and broadens the scope of applications for CGI in reality.

Three-Dimensional Point Cloud Denoising for Tunnel Data by Combining Intensity and Geometry Information

At present, three-dimensional laser scanners are used to scan subway shield tunnels and generate point cloud data as the basis for extracting a variety of information about tunnel defects. However, there are obstacles in the tunnel such as pipelines, tracks, and signaling systems that cause noise in the point cloud. Usually, the data of the tunnel point cloud are huge, and the efficiency of artificial denoising is low. Faced with this problem, based on the respective characteristics of the geometric shape and reflection intensity of the tunnel point cloud and their correlation, this paper proposes a tunnel point cloud denoising method. The method includes the following three parts: reflection intensity threshold denoising, joint shape and reflection intensity denoising, and shape denoising. Through the experiment on the single-ring segment point cloud of a shield tunnel, the method proposed in this paper takes 2 min to remove 99.77% of the noise in the point cloud. Compared with manual denoising, the method proposed in this paper takes two fifteenths of the time to achieve the same denoising effect. The method proposed in this paper meets the requirements of a tunnel point cloud data survey. Thus, it provides support for the efficient, accurate, and automatic daily maintenance and surveys of tunnels.

Switchable visible and near-infrared light bifocal lens based on hybrid graphene-metasurface structures

We propose a wideband visible (VIS) and near infrared (NIR) light reflective flat lens with switching capability base on hybrid graphene-metasurface structure. This tunable flat lens structure consists of a slotted gold reflector substrate, a graphene monolayer and TiO2 nanofins that provide excellent focusing performance and tunability in the VIS and NIR range. In order to achieve the switching feature, a bi-functional device capable of switching reflection/absorption modes is required. To this end, the gold substrate with two cross slots has been used to create absorption properties and a graphene layer has been used to change the intensity of absorption and reflection. The switching property for incident light with circular polarization arises from uniformly changing the chemical potential of the graphene layer. By setting the chemical potential of graphene, μc, equal to 1.1 eV, we obtained the reflection property. The reflection mode exhibits a broadband (715–800 nm) optical response with high polarization conversion efficiency (more than 70 %). On the other hand, by choosing μc = 0.75 eV, the absorption feature is created at the wavelength of 700–780 nm. The reflected light can be focused on an arbitrary position above the structure by rotating the TiO2 nanofins with the help of Pancharatnam-Berry (PB) phase theory. In addition to obtaining the single and dual focus tunable flat lenses, we have also presented a multifunction configuration that can switch between different modes, such as non-focus, single and dual focus modes. Such switchable flat lenses can have various applications in imaging and sensing systems.

Fabrication and sensing properties of a molecularly imprinted polymer on a photonic PDMS substrate for the optical detection of C-reactive protein

This work presents the development of a novel photonic-based sensor on a nanoscale patterned polydimethylsiloxane (PDMS) substrate acting as transducer, combined with a molecularly imprinted polymer for the rapid and sensitive detection of C-reactive protein (CRP). A silicon wafer was patterned using electron-beam lithography with a one-dimensional (1D) reflective grating (periodicity of about 400 nm) and then replicated using nanoimprint lithography. The latter silicon mould was used for replica moulding to generate a patterned PDMS. The unique 1D design gave PDMS photonic properties that were suitable as a substrate to assemble the biorecognition layer. The surface of the photonic PDMS was modified to become hydrophilic and additionally functionalized with vinyl silane groups to bind the imprinted polymer, resulting in a photonic molecularly imprinted polymer substrate (PMIPS). The developed method was successfully used to detect CRP in serum samples. The reflectance intensity decreased with increasing CRP concentrations, and the sensing PMIPS showed a linear response between 0.005 and 1.215 µg mL−1, a limit of detection of 0.003 µg mL−1, and a selective response to CRP when tested against serum calprotectin. Overall, this novel optical sensor can be used to detect CRP, a serum biomarker of inflammation, particularly in inflammatory bowel diseases.

Fast Extraction Algorithm of Planar Targets Based on Point Cloud Data for Monitoring the Synchronization of Bridge Jacking Displacements

Transverse synchronization of vertical displacements of all jacking-up points is an important monitoring indicator to replace bearings in assembled multigirder bridges during the jacking phase. Currently, using target paper to identify the 3D coordinates of control points reduces the complexity of monitoring operations and improves the stability of data precision. However, the existing planar target locating methods have low accuracy, inefficiency, and subjectivity, which seriously hinders the construction process of bearing replacement. Accurately obtaining the center coordinates of multiple targets from a point cloud in a short monitoring period remains a challenge. This study proposes a high-precision automated algorithm to extract target center points in low-density point clouds to quickly calculate real target center points. First, we construct a standard point cloud model of the target papers for scanning, including color and geometric features. Then, we extract the measured point cloud of the typical jacking operation phase based on the reflection intensity and size information. Next, we map the intensity values of the measured point cloud into the color channel and register the measured point cloud with its standard point cloud model using the normal vector estimation and colored ICP algorithms. Finally, we extract the center point of the measured targets. Numerical experiments and engineering test results show that the proposed method converges quickly with high precision and good robustness, which saves 91.4% of the time compared with the traditional method. In general, this research can provide effective technical support for 3D laser scanning in monitoring the operation phase of bridge jacking.

Characteristics of blood flow velocity in the radial artery and finger capillaries using magnetoplethysmogram and photoplethysmogram

The Hall element and optical sensor, which can detect the magnetic field change caused by the minute fluctuations of the permanent magnet according to the periodic movement of the radial artery and the light absorption and reflection intensity according to the change in wrist blood flow, respectively, are used as key elements in digital healthcare devices. The pulse waves of the radial artery inside the wrist were measured and analyzed using a clip-type pulsimeter, magnetoplethysmogram (MPG). The pulse wave, which is the change in blood flow obtained by photoplethysmogram (PPG), was measured on the dorsal side of the wrist or the fingers simultaneously with MPG. The ABFV (artery blood flow velocity) is a few ten cm/s as DH (distance between MPG and PPG) divided by ΔT(a) which is the time difference between the first peaks of MPG and PPG waveforms on the dorsal side of the wrist. The PBFV (peripheral blood flow velocity) is a few mm/s as LH (hand length) divided by ΔT(p), which is the time difference between the first peak of MPG waveform and the third peak PPG waveform on the finger. For four subjects of 20s, SPWV was approximately 0.98∼1.19 m/s, ABFV in the radial artery was approximately 0.40∼0.44 m/s, and finger capillary PBFV was approximately 6.3∼8.6 cm/s. These results show reasonable values similar to the blood velocity of many blood vessels flowing through the human body.

Oil film identification via windspeed interference estimation using hyperspectral data

ABSTRACT Sea surface windspeed is one of the important factors that affects the reflection and radiation intensity of the sea surface oil film, posing challenges for the oil spill classification via marine optical remote sensing. A new method is proposed to extract significant windspeed features from the original hyperspectral remote sensing data and apply them to the qualitative analysis and quantitative estimation of local windspeed. First, a portable push-broom hyperspectral sensor was utilized to collect the hyperspectral data under different experimental windspeed conditions. After that the data was divided into spatial-spectral hyperspectral data along the spectrum slit direction and finally converted into spatial-spectral joint heat maps (SSHMs). Incorporating a transfer learning methodology, we utilized SSHMs as input to optimize the training process of windspeed estimation. This approach yielded dependable evaluation results. Moreover, we developed an effective deep learning framework that integrates spatial and spectral analyses for the categorization of sea surface oil films. This framework utilizes hyperspectral image datasets which have already removed the influence of windspeed variables. The proposed method demonstrated outstanding performance in estimating windspeed and classifying oil films using both experimental and AVIRIS datasets. This paper introduces a feasibility study for ocean surface windspeed estimation and precise identification of oil spills using optical remote sensing technology.

Structural, Morphological, Spectroscopic, and Dielectric Properties of Ca-Mg Nanoferrites for High-frequency Applications

Introduction:: Ca-Mg nanoferrites of composition Ca0.5Mg0.5Fe2O4 were synthesized by wet chemical citrate precursor approach. Methods:: The prepared nanoparticles were characterized by an “X-ray diffractometer, Scanning electron microscopy, Diffused reflectance spectroscopy, and Impedance analyzer”. In the XRD pattern, the most intense reflection was observed from the (311) peak that corresponds to the cubic spinel phase of the prepared ferrite. Results:: The average size of all crystallites was calculated to be 19 nm. The sample's porosity was found to be 61%. In the SEM micrograph, uniformity in the size of the particles was detected with some agglomeration. The calculated energy band gap (1.91eV) reveals the semiconducting nature of the prepared material. Conclusion:: The high value of the real part of the dielectric constant (290) and the low value of loss tangent (0.17) reveals that the synthesized material can be useful in magnetic cores, microwave components, and other high-frequency applications.

Origin of organic matter pore heterogeneity in oil mature Triassic Chang-7 mudstones, Ordos Basin, China

Organic matter (OM)-hosted pores play a crucial role in hydrocarbon storage and migration in unconventional shale oil and gas systems. An accurate identification of different OM compositions is critical to understanding the origin of heterogeneity in OM-hosted pore development, which remains challenging due to the lack of apparent intrinsic connection between them. This study correlates light-electron-Raman spectroscopy to investigate various OM maceral types and their molecular structures with respect to the pore characteristics in an oil mature Triassic Chang-7 lacustrine mudstone from the Ordos Basin, China. Moreover, the occurrence and distribution of soluble bitumen and its effect on OM-hosted pore development were directly characterized by comparison of a stitched large SEM image mosaic (∼1 mm2) before and after organic solvent extraction. Results show that terrestrial OM including inertinite and vitrinite fragments display well-preserved biological structure and high reflectance intensity in light microscopy. They often are nonporous under SEM, and have no change before and after solvent extraction. Solid bitumen is the predominant maceral of the examined mudstone, and shows two general populations: 1) nonporous or weakly porous solid bitumen (SB1) that often occurs in large-sized and structured accumulation; 2) porous solid bitumen (SB2) that is filled or dispersed in mineral matrix. After extraction, SB2 was completely removed and have a noticeable recovery of pores, while SB1 changed slightly. The heterogenous pore characteristics can be related to the varying OM molecular structure which shows a decreasing trend of aromaticity in the order of terrestrial macerals, SB1, and SB2 as indicated by Raman parameters. A relatively higher pore abundance in SB2 may be attributed to chromatographic fractionation which led to more mobile and compliant components of SB2.

Equal Emphasis on Data and Network: A Two-Stage 3D Point Cloud Object Detection Algorithm with Feature Alignment

Three-dimensional object detection is a pivotal research topic in computer vision, aiming to identify and locate objects in three-dimensional space. It has wide applications in various fields such as geoscience, autonomous driving, and drone navigation. The rapid development of deep learning techniques has led to significant advancements in 3D object detection. However, with the increasing complexity of applications, 3D object detection faces a series of challenges such as data imbalance and the effectiveness of network models. Specifically, in an experiment, our investigation revealed a notable discrepancy in the LiDAR reflection intensity within a point cloud scene, with stronger intensities observed in proximity and weaker intensities observed at a distance. Furthermore, we have also noted a substantial disparity in the number of foreground points compared to the number of background points. Especially in 3D object detection, the foreground point is more important than the background point, but it is usually downsampled without discrimination in the subsequent processing. With the objective of tackling these challenges, we work from both data and network perspectives, designing a feature alignment filtering algorithm and a two-stage 3D object detection network. Firstly, in order to achieve feature alignment, we introduce a correction equation to decouple the relationship between distance and intensity and eliminate the attenuation effect of intensity caused by distance. Then, a background point filtering algorithm is designed by using the aligned data to alleviate the problem of data imbalance. At the same time, we take into consideration the fact that the accuracy of semantic segmentation plays a crucial role in 3D object detection. Therefore, we propose a two-stage deep learning network that integrates spatial and spectral information, in which a feature fusion branch is designed and embedded in the semantic segmentation backbone. Through a series of experiments on the KITTI dataset, it is proven that the proposed method achieves the following average precision (AP_R40) values for easy, moderate, and hard difficulties, respectively: car (Iou 0.7)—89.23%, 80.14%, and 77.89%; pedestrian (Iou 0.5)—52.32%, 45.47%, and 38.78%; and cyclist (Iou 0.5)—76.41%, 61.92%, and 56.39%. By emphasizing both data quality optimization and efficient network architecture, the performance of the proposed method is made comparable to other state-of-the-art methods.

An autonomous Internet of Things spectral sensing system for in-situ optical monitoring of grape ripening: design, characterization, and operation

The present work proposes a novel autonomous Internet of Things (IoT) spectral sensing system for in-situ optical monitoring of grape ripening through reflectance signals. To this end, tailor-made hardware for this IoT end node was developed, characterized, and operated in both lab and field conditions. It included three complementary modules: the optical module, the host module, and the controller module. The optical module included four photodetectors and four LEDs with maximum emission wavelength centered at 530, 630, 690, and 730 nm that was placed in direct contact with the grape berry. The host module included the LED driver and the analog front-end for signal acquisition. Finally, the controller module provided full control of the system and ensured data storage, power management, and connectivity. The system was capable of measuring reflectance in the range of 4 – 100 % with a linear response (r2 > 998) and with a high reproducibility among different optical units. This design made it possible to collect reflectance signals from red (cv. Touriga Nacional) and white (cv. Loureiro) grape varieties in both lab and field environments. The relationship between this optical fingerprint (comprised of the different reflectance intensities recorded) and the evolution of grape berry quality parameters throughout the ripening period (for approximately two months), was analyzed and discussed. Lab data was used to establish a multivariate model based on Partial Least Squares for the prediction of the Total Soluble Solids (TSS) content in both varieties. The model error (Root Mean Square Error in Cross Validation) was 2.31 and 0.73 °Brix for Touriga Nacional and Loureiro, respectively. This model was applied to data acquired in the field in an illustrative example of the potential of the system to predict TSS in real time. The field observations collected during the monitoring period also provided relevant information about the potential issues that may occur during the unattended operation of the optical sensors. Additionally, the modular architecture of the optical module proposed makes it possible to use different LEDs and photodetectors, as well as the assembly of optical filters. This creates the possibility of using the same principles for measuring reflectance in different spectral ranges (e.g. IR) or even fluorescence. The results herein described paved the work for future developments of this technology that will include the development of prediction models for the most relevant grape ripening parameters based on reflectance data, as well as its operation as part of a Wireless Sensor Network.

Multitargets Orientation Technique Based on Reflection Characteristic Analysis Using an Inverse Diffraction Parabolic Equation

In this article, the inverse diffraction parabolic equation (IDPE) model based on the finite difference method is proposed, which is first applied in the multiple nonradiation targets orientation technology. In principle, the electromagnetic signal propagating in the transmission path will produce a reflected signal back to the source end while encountering the discontinuous objects. The distribution of the reflection or refraction intensity is directly associated with the distances and heights of the objects, so the location can be determined by means of analyzing the distribution. Here, according to the profile data of field intensity at the source end, the distribution of backward propagating electromagnetic waves are calculated rapidly by the IDPE. Then, the local extreme searching method is applied to search the coordinate of the convergence point of field intensity and the positions of multiple objects are finally determined. The piecewise linear function is used to model the irregular terrain. The influence of discontinuous terrain slopes on the false alarm probability of objects localization is also analyzed. The results show that the localization accuracy of the IDPE algorithm is affected by multiple factors, such as the radio frequency and sampling interval of field intensity. It is proved that the IDPE is a novel and efficient algorithm for multiple nonradiation targets orientation technology in long-range complicated terrain environment.

An in vitro cytotoxicity study on PANI/ZnO nanocomposites against HCT- 116 cancer cells using MTT assay

Polyaniline/ZnO nanocomposites were synthesized with different weight percentage of ZnO (20%, 40%, 60%, and 80%) using an in situ chemical polymerization method. The prepared composite samples were characterized by FT-IR, XRD, TEM, SEM with EDAX, UV-Vis, PL, electrical conductivity, photocatalytic properties, antibacterial and anticancer activity. The presence of functional groups in PANI-ZnO NCs is identified by FTIR spectral analysis. X-ray diffraction patterns show that the nanocomposites exhibit preferential orientational growth along the (002) plane with a hexagonal wurtzite structure. The intensity of the maximum reflection (101) increases with increasing ZnO doping concentrations. The TEM analysis indicates that the spherical shape,the particle size was found to be 36.8 nm. SAED pattern closely matched the peaks of the XRD pattern, confirming that the relevant SAED patterns correspond to hexagonal Wurtzite crystals of ZnO. SEM image shows a spherical morphology and ZnO nanoparticles are homogeniously mixed with PANI matrix. EDAX analysis showed the presence of Zn, C and O.UV-vis absorption spectra reveals increasing wavelength leads to the redshift in which there is a strong interaction between PANI and PANI/ZnO. The band gap energy of PANI/ZnO NCs is decreased to 1.30 eV by adding different concentrations of ZnO. PL analysis showed that the addition of ZnO dopants reduced the bandgap and increased oxygen defect vacancies of PANI/ZnO composites. An increase in ZnO content in the PANI matrix was also seen to cause a decrease in electrical conductivity. This alteration might be connected to ZnO’s doping and dedoping activity in the PANI matrix. Photocatalytic activity of 80 wt%ZnO-doped PANI-NPs through the degradation of methylene blue (MB) pigment studied under UV-A light and had detected 95% of degradation throughout 260 min. The lowest bandgap of 80 wt% ZnO shows the highest photocatalytic MB degradation performance. PANI/ZnO nanocomposite with high concentration of ZnO had good antibacterial activity against both P. aeruginosa and S. aureus. An in vitro study was used to evaluate the cytotoxic effects of PANI and PANI/ZnO NPs on the HCT-116 cell line. The results of the MTT test reveal that PANI/ZnO NPs have a significantly greater cytotoxic effect on the HCT-116 cell line than PANI.

Reflection Intensity Guided Single Image Reflection Removal and Transmission Recovery

Reflection Intensity Guided Single Image Reflection Removal and Transmission Recovery

Study on Ship Type Identification by Distribution of Radar Reflection Intensity Data

Study on Ship Type Identification by Distribution of Radar Reflection Intensity Data

Influence of the Zn/Al molar ratio over the photocatalytic hydrogen production by ZnS/ZnAl-LDH composites

In this work, ZnS/ZnAl-LDH composites were prepared by the sulfuration of pristine Zn–Al LDHs with Zn:Al molar ratios between 2 and 4. The sulfured samples (ZA2-S, ZA3-S, ZA4-S), were evaluated in the photocatalytic hydrogen production to verify the effect of the Zn:Al molar ratio. The X-ray powder diffraction revealed the presence of a well-crystallized hydrotalcite-like structure in all samples, but ZA4 also exhibited hydrozincite and Zn(OH)2 reflections. It was observed that as the Zn:Al ratio increased in the sulfured samples, more intense reflections of ZnS were detected. The sample with the highest amount of ZnS (ZA4-S) displayed the lowest bandgap value (3.54 eV) and the highest photocatalytic H2 production (4409.0 μmol g−1 h−1); although, it presented the lowest specific surface area (16 m2 g−1). Moreover, it was confirmed that after 5 cycles uses its activity not only was maintained but it increased slightly. As ZnS itself displayed a lower hydrogen production capacity compared to the ZA4-S composite, the photocatalytic activity of the prepared samples was explained in terms of the formation a type II heterojunction between ZnS and the LDH.

Intelligent Target Classification Algorithm for 77G Radar Based on Correction Data Set

Traffic participant classification is crucial in autonomous driving perception. Millimeter wave radio detection and ranging radar is a cost-effective and powerful method to perform the task in adverse traffic scenarios, especially in bad inclement weather (e.g. fog, snow and rain) and poor lighting conditions. This paper presents an intelligent target classification algorithm for 77G radar based on correction data set. First, in order to handle the problem that the original data set may easily be interfered by obstacles, the angle information is filtered by analyzing the spatial information of radar signals, which means the interference clutter of obstacles can be effectively removed. Second, the primary data set is corrected using the significant difference between the micro-Doppler of the human body and car. Finally, the characteristic information of the radar signal is extracted, including distance, speed, orientation, micro-Doppler and reflection intensity, and the obtained data sets containing three types of targets (vehicles, human bodies and obstacles) are generated. The generated dynamic and static data sets are collected by sufficient experiments to construct deep learning classification models. The results show that the classification accuracy is improved by the measure of data set correction.

Detailed Bathymetry and Seafloor Backscatter Image Dataset for Monitoring Ecosystem Environment: Southern Ulleungdo

We obtained a detailed bathymetry dataset using multi-beam echo sounder around Neunggeol located at Southern part in Ulleungdo. The survey period was from September 1, 2019 and the bathymetry survey was carried out by RV Jangmok No2 of Korea Institute of Ocean Science and Technology. The instrument equipment used for the survey were Kongsbergs’s EM2040 (multi-beam echo sounder). The detailed bathymetry of the study area shows gradually depth change from about 4 to 190 m. The study area has relatively gentle slopes from the coast to 25 m and below about 125 m in depth, but a steep slope from about 25 to 125 m. The rocky bottom zones identified from detailed bathymetry and backscattered seafloor images are distributed in the northeastern sea of the coastal area, the central sea, and around Neunggeol. In particular, Neunggeol and the near large outcropping rock are tumulus-like submarine landforms, forming big vertical walls. The submarine cable buried between Ulleungdo and the Korean mainland appears in the northern seafloor of the survey area. Stopper stones and gravels sediment are distributed around it. The rocky bottom zone has a strong and irregular reflection intensity in the seafloor backscattered image and the sandy sediment zone has a relatively weak reflection intensity and a uniform pattern, so the two zones are well divided. We classified the sediment zones into fine-grained sediments, sandy sediments, and gravel-like sediments by using the different reflection intensities due to differences in sediment particle size in the seafloor image. Detailed bathymetry and seafloor backscattered image dataset around Neunggeol can be used as a basic data for habitat environment mapping.

Atomic Layer Deposition for Tailoring Tamm Plasmon-Polariton with Ultra-High Accuracy

In this study, we demonstrate the potential capability to control Tamm plasmon-polaritons (TPP) by applying atomic layer deposition (ALD) as a highly precise technique for plasmonic applications. Applications in plasmonics usually require tens of nanometers or less thick layers; thus, ALD is a very suitable technique with monolayer-by-monolayer growth of angstrom resolution. Spectroscopic ellipsometry and polarized reflection intensity identified the TPP resonances in the photonic band gap (PBG) formed by periodically alternating silicon oxide and tantalum oxide layers. The sub-nanometer control of the Al2O3 layer by ALD allows precise tailoring of TPP resonances within a few nanometers of spectral shift. The employing of the ALD method for the fabrication of thin layers with sub-nanometer thickness accuracy in more complex structures proves to be a versatile platform for practical applications where tunable plasmonic resonances of high quality are required.

Pair Distribution Function Analysis of Local Structural Rearrangement in Spinel Vanadium Oxide Cathodes

Being a divalent cation with the lowest mass, Mg2+ has garnered quite attention in recent times due to the high volumetric capacity of it’s batteries. Theoretical studies reveal that the combination of a Mg anode and a spinel oxide cathode would be able to surpass the current limits found in widely used Li-ion batteries Therefore, to design materials with high diffusion and reversible insertion capacity, a conclusive demonstration of the intercalation mechanism of Mg2+ into metal oxides, is required.Among the numerous options for cathodes, vanadium oxides show evidence of the largest capability toward reversible intercalation of Mg ions. In particular, spinel-type MgV2O4 shows a capacity of 200 mAh/g, corresponding to the cycling of ~0.7 mol Mg2+ per mol of oxide, and layered α-V2O5 reaches 300 mAhg-1, intercalating 1 mol Mg2+ per mol of oxide. The extent and reversibility of these reactions were confirmed by elemental, redox and structural characterization in recent work [1,2]. This high capacity is only observed at 110°C and it is accompanied by a large hysteresis in potential during the electrochemical cycling. The push to overcome these bottlenecks demands a more comprehensive definition of the underlying mechanisms of reaction than is currently available.Previous X-ray Diffraction (XRD) measurements conducted on the oxide indicated a loss of reflection intensity due to a loss in their crystallinity which makes structural characterization using typical diffraction techniques difficult. Through this work, we mainly make use of pair distribution function techniques to study the changes in the local structures of these oxides in both their pristine and charged state, and the effect of annealing for longer hours with the goal of further pushing the efficiency of multivalent battery materials.Reference: Hu, L., Jokisaari, J.R., Kwon, B.J., Yin, L., Kim, S., Park, H., Lapidus, S.H., Klie, R.F., Key, B., Zapol, P. and Ingram, B.J., 2020. High capacity for Mg2+ deintercalation in spinel vanadium oxide nanocrystals. ACS Energy Letters, 5(8), pp.2721-2727. Yoo, H.D., Jokisaari, J.R., Yu, Y.S., Kwon, B.J., Hu, L., Kim, S., Han, S.D., Lopez, M., Lapidus, S.H., Nolis, G.M. and Ingram, B.J., 2019. Intercalation of magnesium into a layered vanadium oxide with high capacity. ACS Energy Letters, 4(7), pp.1528-1534.