# Fileset [pixel-level-image-based-analysis-of-spatial-kinetics-and-resistance-variation-in-a-large-area-electrochromic-device.pdf.pdf](https://mdr.nims.go.jp/filesets/a5f6fb46-04fb-4798-b72b-ab50c76227b5/download) ## Creator [Shifa Sarkar](https://orcid.org/0009-0008-4641-6569), [Takefumi Yoshida](https://orcid.org/0000-0003-3479-7890), [Banchhanidhi Prusti](https://orcid.org/0000-0003-4489-2509), [Satya Ranjan Jena](https://orcid.org/0009-0009-8344-6131), [Kuo-Chuan Ho](https://orcid.org/0000-0001-7501-1271), [Masayoshi Higuchi](https://orcid.org/0000-0001-9877-1134) ## Rights [Creative Commons BY Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/) ## Other metadata [Pixel-Level Image-Based Analysis of Spatial Kinetics and Resistance Variation in a Large-Area Electrochromic Device](https://mdr.nims.go.jp/datasets/990935e2-d22f-4bb4-9344-e2309f320bb2) ## Fulltext Pixel-Level Image-Based Analysis of Spatial Kinetics and ResistanceVariation in a Large-Area Electrochromic DeviceShifa Sarkar, Takefumi Yoshida, Banchhanidhi Prusti, Satya Ranjan Jena, Kuo-Chuan Ho,and Masayoshi Higuchi*Cite This: ACS Appl. Electron. Mater. 2025, 7, 10651−10663 Read OnlineACCESS Metrics & More Article Recommendations *sı Supporting InformationABSTRACT: Electrochromic (EC) materials and devices (ECDs) have been extensivelystudied, providing valuable insight into their optical switching properties and materialcharacteristics. To analyze dynamic and subtle spatial variations during switching, we developedfor the first time a noninvasive, automated, image-based methodology capable of resolving ECbehavior at the pixel level. Grayscale images, extracted from video recordings at 0.0333 s intervalsduring cyclic operation, were segmented into 8,448 localized pixel positions to quantify contrast−time profiles. First, switching nonuniformity was visualized, revealing reaction times from 0.8 to2.0 s to reach 90% contrast change, with faster responses at the edges and slower transitions inthe center. Second, segmental kinetics were evaluated by fitting time constants (τ) to each profileand linking them to composite resistances using a simplified RC model with a uniform arealcapacitance (0.0133 F cm−2). The edge segment exhibited lower τ (1.79) and resistance (134.5Ω·cm2), whereas the central pixel position showed delayed switching (1.80 s) with larger τ (2.46)and higher resistance (184.9 Ω·cm2). Finally, spatial distributions were visualized through 2Dheat maps, confirming slower central regions (1.60−1.95 s), with regional average resistance of 185.7 Ω·cm2 compared to lower-resistance peripheral regions (136.8−149.6 Ω·cm2). This noninvasive approach enables a high-resolution diagnostic platform forquantifying uniformity, kinetics, and resistance distributions in large-area ECDs and is broadly applicable to diverse EC architecturesinfluenced by spatial transport effects.KEYWORDS: metallo-supramolecular polymer, electrochromic device, Python Library, pixel values, image analysis■ INTRODUCTIONElectrochromic (EC) materials are characterized by theirability to reversibly change color or transparency when avoltage is applied, forming the basis for electrochromic devices(ECDs) that dynamically modulate optical properties such ascolor and transmittance under electrical stimulation.1−3 Suchdevices have been developed to control the transmission orabsorption of light, typically in the visible or near-infraredregions of the electromagnetic spectrum.4−6 In recent years,ECDs have received growing attention due to their low powerconsumption characteristics, in addition to their flexibility andversatility in various applications, such as smart windows,information displays, e-papers, rearview mirrors, and evensome types of eyewear.7−11 An ECD comprises at least one ECactive layer, an electrolyte, and transparent conductiveelectrodes.12,13 The EC layer can include materials such astransition metal oxides (e.g., tungsten oxide), conductingpolymers, or organic compounds.14−18 Among the various ECmaterials reported to date, metallosupramolecular polymers(MSPs), such as Fe(II)-based MSP polyFeL1, have receivedgrowing attention due to their unique properties, such as highcontrast ratios, fast response times, and excellent stabil-ities.19−21The core concept underlying polyFeL1-based ECDs is theoccurrence of reversible redox reactions inside the thin-filmstructure. When a voltage is applied across the device, ions orelectrons are driven into or out of the EC material, causing areversible change from a transparent state to a colored one, orfrom one colored state to another.22,23 A methanolic solutionof this Fe polymer is purple, wherein the color originates fromthe absorption of the metal-to-ligand charge transfer (MLCT)transition (i.e., from Fe(II) to the ligand) at a wavelength of∼580 nm.24,25 ECDs possess various benefits over conven-tional methods of controlling light and heat (e.g., mechanicalblinds or curtains) since they can be precisely adjusted andrespond fast to changes in the electrical signal.26 In the case ofa smart window, the EC material may change from a clear to acolorful or reflecting state, controlling the amount of light andheat that can pass through. Such dynamic control of the opticalcharacteristics provides several benefits, including an improvedReceived: August 25, 2025Revised: November 1, 2025Accepted: November 16, 2025Published: November 21, 2025Articlepubs.acs.org/acsaelm© 2025 The Authors. Published byAmerican Chemical Society10651https://doi.org/10.1021/acsaelm.5c01754ACS Appl. Electron. Mater. 2025, 7, 10651−10663This article is licensed under CC-BY 4.0Downloaded via NATL INST FOR MATLS SCIENCE (NIMS) on December 9, 2025 at 11:14:00 (UTC).See https://pubs.acs.org/sharingguidelines for options on how to legitimately share published articles.https://pubs.acs.org/action/doSearch?field1=Contrib&text1="Shifa+Sarkar"&field2=AllField&text2=&publication=&accessType=allContent&Earliest=&ref=pdfhttps://pubs.acs.org/action/doSearch?field1=Contrib&text1="Takefumi+Yoshida"&field2=AllField&text2=&publication=&accessType=allContent&Earliest=&ref=pdfhttps://pubs.acs.org/action/doSearch?field1=Contrib&text1="Banchhanidhi+Prusti"&field2=AllField&text2=&publication=&accessType=allContent&Earliest=&ref=pdfhttps://pubs.acs.org/action/doSearch?field1=Contrib&text1="Satya+Ranjan+Jena"&field2=AllField&text2=&publication=&accessType=allContent&Earliest=&ref=pdfhttps://pubs.acs.org/action/doSearch?field1=Contrib&text1="Kuo-Chuan+Ho"&field2=AllField&text2=&publication=&accessType=allContent&Earliest=&ref=pdfhttps://pubs.acs.org/action/doSearch?field1=Contrib&text1="Masayoshi+Higuchi"&field2=AllField&text2=&publication=&accessType=allContent&Earliest=&ref=pdfhttps://pubs.acs.org/action/doSearch?field1=Contrib&text1="Masayoshi+Higuchi"&field2=AllField&text2=&publication=&accessType=allContent&Earliest=&ref=pdfhttps://pubs.acs.org/action/showCitFormats?doi=10.1021/acsaelm.5c01754&ref=pdfhttps://pubs.acs.org/doi/10.1021/acsaelm.5c01754?ref=pdfhttps://pubs.acs.org/doi/10.1021/acsaelm.5c01754?goto=articleMetrics&ref=pdfhttps://pubs.acs.org/doi/10.1021/acsaelm.5c01754?goto=recommendations&?ref=pdfhttps://pubs.acs.org/doi/10.1021/acsaelm.5c01754?goto=supporting-info&ref=pdfhttps://pubs.acs.org/doi/10.1021/acsaelm.5c01754?fig=agr1&ref=pdfhttps://pubs.acs.org/toc/aaembp/7/23?ref=pdfhttps://pubs.acs.org/toc/aaembp/7/23?ref=pdfhttps://pubs.acs.org/toc/aaembp/7/23?ref=pdfhttps://pubs.acs.org/toc/aaembp/7/23?ref=pdfpubs.acs.org/acsaelm?ref=pdfhttps://pubs.acs.org?ref=pdfhttps://pubs.acs.org?ref=pdfhttps://doi.org/10.1021/acsaelm.5c01754?urlappend=%3Fref%3DPDF&jav=VoR&rel=cite-ashttps://pubs.acs.org/acsaelm?ref=pdfhttps://pubs.acs.org/acsaelm?ref=pdfhttps://acsopenscience.org/researchers/open-access/https://creativecommons.org/licenses/by/4.0/https://creativecommons.org/licenses/by/4.0/https://creativecommons.org/licenses/by/4.0/energy efficiency, user comfort, glare reduction, privacy, andaesthetic appeal.27−29 Understanding these properties thereforeis crucial for analyzing the ECDs.In the dynamic realm of materials science, data analyticalmethods are crucial tools for understanding, optimizing, anddeveloping materials with remarkable efficiency and preci-sion.30 These methods include techniques such as statisticalanalysis and machine learning algorithms.31,32 In the context ofmaterials science research, visual data representation, such as inthe form of pictures or videos, is essential because it allowsresearchers to directly observe and evaluate the characteristicsand behaviors of materials, allowing them to discover hiddenpatterns.33,34 Conventional methods for EC analysis inmaterials science predominantly rely on scanning electronmicroscopy, ultraviolet−visible (UV−vis) spectroscopy, andelectrochemical impedance spectroscopy; have providedvaluable insights into switching mechanisms and materialproperties.14,35−39 Despite the widespread application of thesetraditional methods, they often exhibit limited spatialresolutions and require sophisticated instrumentation. Forexample, previous work on thermal degradation of ECDs usingtransmittance spectroscopy successfully monitored opticalchanges at 580 nm.40 However, this approach was limited toa single wavelength, potentially omitting information regardingchanges occurring in other parts of the spectrum. Theselimitations highlight the need for alternative data analysisstrategies capable of providing spatially resolved, time-depend-ent information.To address this challenge, this work reports a novel image-based analytical method for two-dimensional (2D) inves-tigation of ECD using images and movie. This method isdeveloped using the Python Library,41−45 and is specificallydesigned to analyze grayscale images of ECDs, potentiallyrendering it an effective tool for quantitative investigation ofthe color-changing behavior of MSP-based ECDs. Notably,Python provides a robust image processing and analysisenvironment based on OpenCV (Open-Source ComputerVision Library).46 More specifically, grayscale images representvariations in intensity using different shades of gray, while thepixel values in the grayscale images represent the intensity oflight at each pixel,47 thereby allowing changes in the opticalproperties to be monitored over time. Through this non-invasive and automated methodology, the aim is to analyzeECDs by tracking and quantifying EC changes at the pixellevel. Consequently, this study aims to enhance our under-standing of EC behavior, including their kinetics, uniformity,and spatial distributions.■ EXPERIMENTAL SECTIONMaterials and Instruments. All purchased chemicals andreagents were of analytical grade and were used as receivedwithout further purification. Poly(methyl methacrylate)(PMMA, Mw = 350 kg mol−1) and the indium tin oxide(ITO)-coated glass substrate (resistivity 8−12 Ω/sq) werepurchased from Sigma-Aldrich, USA. Methanol (MeOH) andpropylene carbonate (PC) were obtained from Wako PureChemical Industries, Ltd., Japan, while lithium perchlorate(LiClO4) and nickel hexacyanoferrate (NiHCF) were obtainedfrom Kanto Chemical Co., Inc., Japan. The Fe(II)-based MSP(polyFeL1) was purchased from Tokyo Chemical Industry(TCI) Co., Ltd., Japan. Thermo Electron LED GmbH(Smart2Pure 6 UV) was purchased from Nikko Hansen &Co., Ltd., Japan, and used to prepare deionized water for thepreparation of the counter material solution.An Apeiros API Corporation (Tokyo) automated spraycoater machine was used for preparing the polyFeL1 andcounter material films. An ESPEC Corp. benchtop-typechamber (SH-242) was used as the temperature and humiditycontrol chamber. An ALS/CHI electrochemical workstation(CH Instruments, Inc.) was used as the potentiostat for allchronoamperometric measurements.Spatiotemporal Image Acquisition. A VHX-970F digitalmicroscope was used to capture movies and photographicimages of the polyFeL1-based ECD during cycling. Themicroscope was equipped with a Z00 lens operated at ×5magnification, and the exposure time (1/60 s) wassynchronized with the 60 Hz LED illumination to preventflicker and rolling-shutter artifacts. For kinetic analysis, framescorresponding to −1 to 3 s (120 frames in total) wereanalyzed, providing a 33 ms temporal resolution that wassufficient to resolve the EC switching behavior without motionblur or illumination distortion. The original microscope videowas recorded at a resolution of 800 × 600 pixels. For imageanalysis, the EC active area (48 × 44 mm) was cropped as theregion of interest, corresponding to 480 × 440 pixels in thelength scale. After 5 × 5 pixel averaging, the final analytical gridcontained 96 × 88 segments, yielding an effective spatialresolution of approximately 0.50 × 0.50 mm (0.25 mm2) persegment, which defines the smallest unit analyzed for grayscalekinetics. In addition to spatial resolution, the temporalacquisition parameters were also optimized. Video acquisitionwas performed at 30 frames s−1, corresponding to a temporalresolution of 0.0333 s per data point in the contrast−timeplots. This frame rate was sufficient to capture the completeEC transition without distortion or loss of kinetic detail of thepolyFeL1-based ECD. Increasing the frame rate did not alterthe validity or spatial pattern of the extracted kinetics;therefore, 30 fps was adopted as an optimal setting for reliableand efficient image-based analysis. For devices exhibiting fasterEC switching (≤1 s), a higher acquisition rate (e.g., 50 fps) isrecommended to achieve improved temporal resolution.Preparation of the PolyFeL1 and NiHCF Films on ITO-Glass Substrates. Initially, polyFeL1 was dissolved in MeOHto prepare a 3 mg/mL polymer solution, which wassubsequently filtered through a microsyringe (polyvinylidenefluoride, 0.45 μm) to eliminate any trace amounts of insolubleresidue. The ITO-glass substrate was cleaned using acetoneand treated with UV irradiation and ozone for 20 min prior tothe preparation of the polymer film. After this time, the ITO-glass substrate was heated at 57−60 °C on a hot plate beforespraying. More specifically, the automated spray coater wasused to generate a uniform, smooth, and purple-coloredpolyFeL1 coating on the ITO-glass substrate using the above-prepared polymer solution. The coating process was performedat 200 rpm, and a total of three coatings were applied to givethe desired film.Using the same procedure, a NiHCF thin film was preparedon another ITO-glass substrate. In this case, an aqueoussolution of NiHCF (75 μL/mL) was employed, and the hotplate temperature was set to 93−95 °C, and a total of twocoatings were applied to give the desired film thickness. Theresulting material was used as the counter electrode.Preparation of the Gel Electrolyte. LiClO4 (0.3 g) andPC (2.0 mL) were added to a screw-neck vial and stirred for 20min. Subsequently, PMMA (2.0 g) was added gradually withACS Applied Electronic Materials pubs.acs.org/acsaelm Articlehttps://doi.org/10.1021/acsaelm.5c01754ACS Appl. Electron. Mater. 2025, 7, 10651−1066310652pubs.acs.org/acsaelm?ref=pdfhttps://doi.org/10.1021/acsaelm.5c01754?urlappend=%3Fref%3DPDF&jav=VoR&rel=cite-asvigorously stirring (LiClO4/PC/PMMA = 8:46:46, wt %), andthe resulting mixture was stirred under vacuum for 1 h at roomtemperature. After this time, a cloudy and viscous liquidelectrolyte was obtained.Device Fabrication. To prepare the solid-state polyFeL1-based ECD, the gel electrolyte was poured onto a polyFeL1-coated ITO-glass substrate (working electrode, WE, 5.0 × 5.0cm2) and covered by another ITO-glass substrate, which wascoated with NiHCF (counter electrode, CE, 5.0 × 5.0 cm2) toform a sandwich configuration solid-state device in which theelectrodes were separated by the gel electrolyte. The resultingdevice was placed in a temperature/humidity control chamber,where the temperature was set to 95 °C and the relativehumidity (RH) to 40%. Once the temperature of the chamberreached 95 °C, it was cooled to 25 °C at 80% RH. Finally, thedevice was cleaned, and a voltage (1.2 V) was applied toobserve the color change (Scheme S1a).The image-based analysis in this study was performed on asingle representative polyFeL1-based ECD during its first ECswitching cycle. Because the primary objective of this work isto establish and validate the image-based analytical method-ology rather than to perform a statistical comparison amongmultiple devices, thus only one device was analyzed in detail.■ RESULTS AND DISCUSSIONMovie Capture and Processing of Image Data. In theUV−vis spectrum of the prepared purple polyFeL1 specimen,an absorption band appeared at ∼580 nm, which wasattributed to the MLCT transition from Fe(II) to the ligand,and accounted for the purple color of the methanolicsolution.2,55 It was considered that upon subjecting Fe(II) toelectrochemical oxidation upon the application of a voltage,Fe(III) would be generated, and the MLCT absorption bandwould be eliminated, leading to a change in color.24Importantly, it was observed that the fabricated polyFeL1-based ECD displayed reversible color changes from purple tocolorless at low voltages of 1.2−0 V. More specifically, Figure 1illustrates a time-dependent image sequence depicting the ECproperties of the polyFeL1-based ECD under chronoampero-metric analysis at a constant potential of 1.2 V over 3 s.Initially, the device appeared deep purple, transitioning tocolorless as Fe2+ (original state) underwent electrochemicaloxidation to Fe3+ (oxidized state) upon voltage application.The device exhibited a fast EC response, with completedecolorization being observed after 2.0 s (fifth image).However, the intermediate state at 1.0 and 1.5 s (third andfourth images) revealed nonuniform color transitions, withlighter edges and a purple color in the center, revealing variableregional response rates. This observed nonuniformity under-scores the need for image data analysis to capture spatial andtemporal variations in the optical response of ECDs.To develop the new analytical method, movie of thepolyFeL1-based ECD was captured using a digital microscopeequipped with an appropriate Z00 lens at × 5 magnification tofocus on the display surface and record the color-changingproperty of the device during the switching process (SchemeS1b). The complete camera setting was provided in Table S1.Using the Python Library, image data were collected from themovie at a regular interval of 0.0333 s (≈30 fps). From theseoriginal images, the region of interest (ROI) was selected andcropped, and the cropped images were converted intograyscale images. In this work, grayscale contrast values wereexpressed on an 8-bit scale, where 0 represents black and 255represents white, obtained through OpenCV’s standardconversion (cv2.cvtColor). For example, a value of 30 indicatesa dark gray region, while 240 corresponds to a light gray area,directly representing the optical brightness in the standardizedgrayscale image. A total of 211,200 pixels were extracted fromthe grayscale images, and adjacent pixel values were merged toobtain a single segment for every 25 (5 × 5) pixels. Thissegmentation step was essential to reduce data noise, smoothlocal intensity fluctuations, and improve the reliability ofcontrast−time analysis across the device. Additionally,aggregating pixel data into segments allowed manageablecomputation while retaining sufficient spatial resolution tocapture meaningful switching behavior. After averaging theFigure 1. Movie capture in a polyFeL1-based ECD under an applied voltage of 1.2 V, showing the transition from colored (purple) to bleached(colorless) states over time (0−2.5 s).ACS Applied Electronic Materials pubs.acs.org/acsaelm Articlehttps://doi.org/10.1021/acsaelm.5c01754ACS Appl. Electron. Mater. 2025, 7, 10651−1066310653https://pubs.acs.org/doi/suppl/10.1021/acsaelm.5c01754/suppl_file/el5c01754_si_001.pdfhttps://pubs.acs.org/doi/suppl/10.1021/acsaelm.5c01754/suppl_file/el5c01754_si_001.pdfhttps://pubs.acs.org/doi/suppl/10.1021/acsaelm.5c01754/suppl_file/el5c01754_si_001.pdfhttps://pubs.acs.org/doi/suppl/10.1021/acsaelm.5c01754/suppl_file/el5c01754_si_001.pdfhttps://pubs.acs.org/doi/10.1021/acsaelm.5c01754?fig=fig1&ref=pdfhttps://pubs.acs.org/doi/10.1021/acsaelm.5c01754?fig=fig1&ref=pdfhttps://pubs.acs.org/doi/10.1021/acsaelm.5c01754?fig=fig1&ref=pdfhttps://pubs.acs.org/doi/10.1021/acsaelm.5c01754?fig=fig1&ref=pdfpubs.acs.org/acsaelm?ref=pdfhttps://doi.org/10.1021/acsaelm.5c01754?urlappend=%3Fref%3DPDF&jav=VoR&rel=cite-aspixel values, a total of 8448 segments (consisting of 96columns and 88 rows) were obtained for the EC active area ofthe device (Scheme 1). Consistent lighting conditions andcamera settings were maintained throughout this experiment toensure the reproducibility of the results.Visualization of Switching Nonuniformity. To assessthe EC change quantitatively, the reaction time required toreach 90% of the total contrast change was calculated for everypixel position within the entire EC active area, resulting in adata set covering 8,448 individual segments. The reactiontimes ranged from 0.8 to 2.0 s, providing a comprehensiveevaluation of switching speed across the device. The results arevisualized as a two-dimensional heatmap (Figure 2), whereeach segment is color-coded according to its individualreaction time, with the accompanying color bar depicting theoverall reaction time distribution across the device. In thisheatmap, the light gray regions correspond to shorter reactiontimes (faster EC transitions, ≈0.80−1.40 s), indicating a higherdevice efficiency in these areas. Conversely, the dark grayregions signify longer reaction times (up to ≈2.00 s),representing slower EC responses. This pixel-level kineticmapping provides a comprehensive visualization of thespatially resolved EC response and allows identification ofareas with varying switching behavior. Such analysis providesvaluable insight into the spatial uniformity of grayscale imagecontrast changes, serving as a proxy for optical contrast, acrosslarge-area ECDs.Segmental Kinetic Study on EC Changes. Opticalproperty variations of the ECD were assessed by trackinggrayscale contrast values (30 = dark gray, 240 = light gray) overtime for a selected segment, E (pixel coordinates (45, 46)),located at the device center. Grayscale intensity served as adirect proxy for optical contrast, enabling quantitativemonitoring of dynamic color changes during the switchingprocess. A sequence of grayscale images collected between 0and 2.5 s (Figure 3a) visually captures the progressivetransition from the initial dark, colored state (Fe2+) to thelight gray (bleached), colorless state (Fe3+). At 0 s, the selectedsegment appeared dark, reflecting its fully colored state. After1.5 s, the segment appeared nearly colorless, and by 2.0 s, thebleaching process was essentially complete. The correspondingcontrast−time profile (Figure 3b) numerically represents thischange, clearly showing that the grayscale intensity increasedfrom ∼56 in the fully colored state at 0 s to about 214 in thefully bleached state at 2.0 s. The shape of the contrastevolution curve reveals a smooth, sigmoidal transition, with themain intensity change occurring between 0.5 and 1.5 s beforeapproaching saturation near the maximum contrast level. Toquantify the switching kinetics, the reaction time was definedas the time required to achieve 90% of the total contrastchange. For this central segment E, the switching time wasdetermined to be 1.8 s, as indicated by the red dashed line inFigure 3b. This analysis reveals the localized EC switchingbehavior of the ECD in the central area, showcasing the time-dependent grayscale contrast changes.To examine localized EC switching behavior, multiple pixelpositions (segments) were selected along a horizontal line ofthe device for detailed image-based analysis. This approachenabled precise, pixel-level determination of reaction timesassociated with the Fe2+ → Fe3+ oxidation process, allowingdetection of subtle spatial variations across the device. Fiverepresentative positions (A−E), distributed from the edge tothe center, were identified in the grayscale map of ECD(Figure 4a), and their corresponding reaction times weredetermined from contrast−time responses (Figure 4b) andsummarized in Table S1. The edge segment (A) exhibited afast reaction time of 1.1 s, whereas the central segment (E)showed slow switching time of 1.8 s, with intermediatepositions (B−D) displaying gradually increasing times. Thissystematic trend indicates a progressive increase in switchingtime from the edge toward the center. These resultsdemonstrate the capability of the image-based analyticalmethod to resolve spatial heterogeneities in EC switchingScheme 1. Processing the Image Data using the Python LibraryACS Applied Electronic Materials pubs.acs.org/acsaelm Articlehttps://doi.org/10.1021/acsaelm.5c01754ACS Appl. Electron. Mater. 2025, 7, 10651−1066310654https://pubs.acs.org/doi/suppl/10.1021/acsaelm.5c01754/suppl_file/el5c01754_si_001.pdfhttps://pubs.acs.org/doi/10.1021/acsaelm.5c01754?fig=sch1&ref=pdfhttps://pubs.acs.org/doi/10.1021/acsaelm.5c01754?fig=sch1&ref=pdfpubs.acs.org/acsaelm?ref=pdfhttps://doi.org/10.1021/acsaelm.5c01754?urlappend=%3Fref%3DPDF&jav=VoR&rel=cite-asbehavior and establish a foundation for mapping localizedresistance variations within the ECD.The EC switching dynamics of the device were analyzedusing an equivalent-circuit approach adapted from theconceptual framework proposed by Ho and coworkers,48,49who demonstrated that large-area ECDs operated underpotentiostatic control can be represented by a dominantcapacitance in series with an effective resistance thatincorporates electrolyte resistance, electrode resistance,charge-transfer resistance, and ionic mass-transport resistance.In such systems, the double-layer capacitance at the electrode/electrolyte interface is much smaller than the workingelectrode capacitance, allowing the device to be approximatedas a single “large capacitor” connected in series with a lumpedresistance. This simplified treatment has been widely appliedfor evaluating EC behavior because it provides direct insightinto time-dependent switching characteristics without requir-ing full impedance deconvolution.57The overall capacitance of the present device wasdetermined from chronoamperometric measurement per-formed under a 1.2 V potentiostatic step (Figure S1). Theresulting current decay profile was fitted with a singleexponential function:y A x t yexp( / )1 1 0= × + (1)where t1 is the characteristic time constant of the RC circuit.From the best fit, the amplitude (A1) was determined to be1.13506, and t1 was 0.29688. Since A1 = V/R and V = 1.2 V,the overall device resistance (R) was calculated as 1.057 Ω.Using the RC relationship (t1 = R × C), the total devicecapacitance (C) was obtained as 0.28087 F. Given the device’sEC active area of 21.12 cm2, the capacitance per unit area wascalculated as 0.0133 F/cm2.In the subsequent spatial analysis, this derived arealcapacitance was assumed to be uniform across the deviceand used to convert the local time constants (τ) into resistancevalues according to (eq 2). This assumption is reasonable foruniformly processed thin-film devices and was necessarybecause the image-based contrast−time data do not allowindependent extraction of both C and R at each segment.Moreover, experimentally, the polyFeL1 film exhibited uni-form coloration, indicating homogeneous metal (Fe) distribu-tion in the MSP and minimal variation in film thickness;therefore, capacitance variation across the device is expected tobe negligible. By fixing C, local resistance variations could beisolated and quantified usingFigure 2. Grayscale heat map visualization of reaction times to reach 90% contrast change for all 8,448 segments of the ECD. The color scaleranges from 0.8 to 2.0 s, representing reaction time, with light gray (0.8−1.4 s) indicating faster EC switching and dark gray (1.8−2.0 s) indicatingslower transitions.ACS Applied Electronic Materials pubs.acs.org/acsaelm Articlehttps://doi.org/10.1021/acsaelm.5c01754ACS Appl. Electron. Mater. 2025, 7, 10651−1066310655https://pubs.acs.org/doi/suppl/10.1021/acsaelm.5c01754/suppl_file/el5c01754_si_001.pdfhttps://pubs.acs.org/doi/10.1021/acsaelm.5c01754?fig=fig2&ref=pdfhttps://pubs.acs.org/doi/10.1021/acsaelm.5c01754?fig=fig2&ref=pdfhttps://pubs.acs.org/doi/10.1021/acsaelm.5c01754?fig=fig2&ref=pdfhttps://pubs.acs.org/doi/10.1021/acsaelm.5c01754?fig=fig2&ref=pdfpubs.acs.org/acsaelm?ref=pdfhttps://doi.org/10.1021/acsaelm.5c01754?urlappend=%3Fref%3DPDF&jav=VoR&rel=cite-asR C/segment = (2)where τ is the local time constant determined from a stretchedexponential fit of the contrast−time profile (Figure 5d):y y A x x(1 exp( (( )/ ) ))0 0= + × (3)Here, y0 is the initial contrast, A is the total contrast change,x0 is the initial time, and β is the stretched exponent obtainedfrom nonlinear curve fitting of the contrast−time data, whereboth τ and β were treated as free parameters and refined byminimizing the residual sum of squares. The fitting was carriedout in OriginPro using initial guesses of τ = 1 s and β = 0.5,and the final β values were extracted directly from the best-fitcurves.This segmental resistance is a composite parameterrepresenting contributions from the ITO electrode resistances,ionic resistance within the polymer gel electrolyte, and charge-transfer resistances at both the NiHCF counter electrode andpolyFeL1 working electrode (Figure 5b). Following theanalytical concept proposed by Ho,48 these contributions arerepresented as a single effective series resistance (Figure 5c):R R R R R Rsegment ITO ct,CM ion ct,Polymer ITO= + + + +(4)Although these components can be resolved by electro-chemical impedance spectroscopy, previous work has shownthat for potentiostatically switched devices, the transientbehavior is dominated by the working electrode capacitance,making it valid to represent them as one effective term.48 Thisapproach enables rapid, spatially resolved mapping of electro-chemical resistance without location-specific impedancemeasurements.Time constants (τ) extracted for five representative pixelpositions (edge, intermediate, center) (Figure S2) wereobtained by fitting the corresponding contrast−time profilesusing eq 3, which provided excellent agreement with theexperimental data (R2 = 0.93−0.99, reduced χ2 ≤ 350).Attempts to fit the same data using single- or double-exponential models resulted in poor convergence andunrealistic τ values, confirming that only the stretched-exponential model adequately represents the experimentalkinetics. The extracted τ values were then converted tosegmental resistances using eq 2, and the results areFigure 3. Color-changing rate of a central segment (pixel coordinatesx, y: 45, 46) as shown in the form of (a) grayscale images over time(0−2.5 s) illustrate the transition to the colorless state, and (b) thecorresponding contrast vs time plot quantifies this change, with the90% contrast change defining the reaction time (1.8 s) for the centralposition.Figure 4. (a) Grayscale map of the ECD, with red-colored pixelcoordinates (X, Y) at the edges indicating regions responsible for thehigh contrast (≥84) values: (0, 0), (0, 84), (0, 85), (0, 88)−(0, 95),and (60, 0)−(87, 0), excluding (63, 0) and five pixel positions (A−E)selected along a horizontal line at Y = 46 in this grayscale map,spanning from the device edge to the center: A (1, 46), B (12, 46), C(23, 46), D (34, 46), and E (45, 46). These positions were chosen tocapture spatial variations in device response from the periphery (A,near the busbar) through intermediate regions (B−D) to the centralregion (E). (b) Corresponding contrast−time responses (−1 to 3 s)for these positions reveal progressively increasing reaction times fromA to E and with changes in pixel intensity.ACS Applied Electronic Materials pubs.acs.org/acsaelm Articlehttps://doi.org/10.1021/acsaelm.5c01754ACS Appl. Electron. Mater. 2025, 7, 10651−1066310656https://pubs.acs.org/doi/suppl/10.1021/acsaelm.5c01754/suppl_file/el5c01754_si_001.pdfhttps://pubs.acs.org/doi/10.1021/acsaelm.5c01754?fig=fig3&ref=pdfhttps://pubs.acs.org/doi/10.1021/acsaelm.5c01754?fig=fig3&ref=pdfhttps://pubs.acs.org/doi/10.1021/acsaelm.5c01754?fig=fig3&ref=pdfhttps://pubs.acs.org/doi/10.1021/acsaelm.5c01754?fig=fig3&ref=pdfhttps://pubs.acs.org/doi/10.1021/acsaelm.5c01754?fig=fig4&ref=pdfhttps://pubs.acs.org/doi/10.1021/acsaelm.5c01754?fig=fig4&ref=pdfhttps://pubs.acs.org/doi/10.1021/acsaelm.5c01754?fig=fig4&ref=pdfhttps://pubs.acs.org/doi/10.1021/acsaelm.5c01754?fig=fig4&ref=pdfpubs.acs.org/acsaelm?ref=pdfhttps://doi.org/10.1021/acsaelm.5c01754?urlappend=%3Fref%3DPDF&jav=VoR&rel=cite-assummarized in Table 1. Position A, located near the deviceedge, exhibited a small τ value (1.79), corresponding to a lowresistance value (134.5 Ω·cm2), whereas position E, at thedevice center, displayed a large τ (2.46), corresponding to ahigh resistance (184.9 Ω·cm2). These results reveal a clearspatial gradient in resistance, with slow switching toward thecenter of the device. The initial grayscale contrast averaged55.99 ± 3.48, indicating small variability likely arising fromoptical factors (focus or illumination) or slight thicknessdifferences. Based on this ± 3.48 (≈6%) variation in contrast, a± 6% sensitivity analysis was performed, producing onlyproportional shifts in resistance values without altering theoverall spatial trend (Table S3). Although minor film-thicknessor morphological differences may exist, but are expected tohave a negligible influence. Future spatially resolved impedanceor SECM mapping will be conducted to experimentally verifylocal capacitance uniformity.To further clarify the physical meaning of the obtainedresistance values, Rsegment represents an effective seriesresistance that combines electronic, ionic, and interfacialcontributions within the device. The sheet resistance of theITO used in the device was 8−10 Ω/sq, and the measuredtwo-probe resistance at ten different distances from the Ag baron the ITO electrode ranged from 5.1 to 5.3 Ω (average = 5.3± 0.06 Ω; Figure S3), indicating that the electronic componentof the ITO layer is minor compared with the total arealresistance (134−185 Ω·cm2) extracted from the imageanalysis. The PMMA/LiClO4 gel electrolyte typically exhibitsionic conductivities of 10−4−10−3 S cm−1 at room temper-ature,50 consistent with ionic and charge-transfer limitationsobserved in large-area ECD. Thus, the spatial variations inRsegment primarily reflect differences in ionic transport throughthe gel and charge-transfer processes at the electrodeinterfaces. Future studies will employ regional electrochemicalimpedance spectroscopy (EIS) at selected distances to separatethese components and verify the spatial interpretation ofRsegment.The observed spatial nonuniformity is more plausiblyattributed to variations in ionic conductivity within thePMMA-based gel electrolyte. Previous studies have shownthat ion mobility in PMMA/LiClO4 gels strongly depends onpolymer segmental flexibility and solvent content.50,51 In thepresent device, the gel layer near the edges likely remains softerand more plasticized, whereas the central region becomesrelatively compact and rigid after thermal conditioning, leadingto slower ion motion and higher resistance at the center.52,53This explanation is consistent with reported decreases in ionicconductivity for more rigid PMMA matrices.51,54 However,this interpretation remains speculative; other factors, such asslight nonuniformity in film thickness, small ITO sheet-resistance gradients, may also contribute.Visualization of Spatial Distributions in SwitchingDynamics. The color-changing pattern across the EC activearea of the polyFeL1-based ECD was subsequently evaluatedby plotting the corresponding contrast vs time curves for all8448 segments (Figure 6a). During the transition from purpleto colorless under the applied potential, the contrast increasedfrom 25 (black/dark gray) to 250 (white/light gray) in agrayscale spectrum, with a key transition phase occurringFigure 5. Determination of the local resistance of each segment (pixelposition) in the ECD. (a) Layer configuration of the ECD consistingof ITO/polyFeL1/gel electrolyte/NiHCF/ITO, (b) representation ofthe device as a generalized RC circuit, (c) simplified RCrepresentation for local resistance extraction, and (d) nonlinearcurve fitting of the time-dependent grayscale contrast at segment A (1,46), used to extract the time constant (τ) associated with the ECswitching process.Table 1. Summary of the τ Values and SegmentalResistancesPixel position Tau value (τ) Resistance, Rsegment (Ω·cm2)A (1, 46) 1.79 ± 0.003 134.5B (12, 46) 1.81 ± 0.03 136.1C (23, 46) 2.13 ± 0.02 160.1D (34, 46) 2.42 ± 0.01 181.9E (45, 46) 2.46 ± 0.01 184.9Figure 6. (a) Contrast vs time plot for all 8,448 segments of the ECD.(b) Corresponding plot highlighting segments with unusually highcontrast values (≥84, range 84−105), shown in red. (c) Contrast−time plot after removal of these high-contrast segments.ACS Applied Electronic Materials pubs.acs.org/acsaelm Articlehttps://doi.org/10.1021/acsaelm.5c01754ACS Appl. Electron. Mater. 2025, 7, 10651−1066310657https://pubs.acs.org/doi/suppl/10.1021/acsaelm.5c01754/suppl_file/el5c01754_si_001.pdfhttps://pubs.acs.org/doi/suppl/10.1021/acsaelm.5c01754/suppl_file/el5c01754_si_001.pdfhttps://pubs.acs.org/doi/10.1021/acsaelm.5c01754?fig=fig5&ref=pdfhttps://pubs.acs.org/doi/10.1021/acsaelm.5c01754?fig=fig5&ref=pdfhttps://pubs.acs.org/doi/10.1021/acsaelm.5c01754?fig=fig5&ref=pdfhttps://pubs.acs.org/doi/10.1021/acsaelm.5c01754?fig=fig5&ref=pdfhttps://pubs.acs.org/doi/10.1021/acsaelm.5c01754?fig=fig6&ref=pdfhttps://pubs.acs.org/doi/10.1021/acsaelm.5c01754?fig=fig6&ref=pdfhttps://pubs.acs.org/doi/10.1021/acsaelm.5c01754?fig=fig6&ref=pdfhttps://pubs.acs.org/doi/10.1021/acsaelm.5c01754?fig=fig6&ref=pdfpubs.acs.org/acsaelm?ref=pdfhttps://doi.org/10.1021/acsaelm.5c01754?urlappend=%3Fref%3DPDF&jav=VoR&rel=cite-asbetween 0 and 3 s. The initial time (0 s) is the time at whichthe voltage was applied. Although the device reached a fullytransparent state under 2.0 s, the contrast progressionexhibited spatial nonuniformity during this period. Initially,the contrast increased sharply, indicating a fast EC response,but a gradual transition followed this, reflected as a curvingtrend in the contrast vs time plot. Additionally, some highercontrast values (≥84, range 84−105) appeared as noise(Figure 6b), particularly from the pixel coordinates located inthe peripheral regions, as shown in Figure 4a, and this wasattributed to optical artifacts introduced during videoacquisition, most likely caused by a focus gradient or lensFigure 7. (a) Grayscale map of the ECD with five defined regions of interest selected for spatial switching analysis: four peripheral corner regions(1−4, green) and one central region (5, orange). Each region is indicated by four red (X, Y) corner coordinates: region 1 = (0, 0), (17, 0), (0, 19),(17, 19); region 2 = (70, 1), (87, 1), (70, 19), (87, 19); region 3 = (0, 77), (17, 77), (1, 95), (17, 95); region 4 = (70, 77), (87, 77), (70, 95),(87, 95); region 5 = (36, 39), (52, 39), (36, 57), (52, 57). (b−f) Corresponding contrast−time profiles (−1 to 3 s) illustrate spatial variations inswitching kinetics, with faster transitions in the peripheral regions (b−e) and slower responses in the central region (f).ACS Applied Electronic Materials pubs.acs.org/acsaelm Articlehttps://doi.org/10.1021/acsaelm.5c01754ACS Appl. Electron. Mater. 2025, 7, 10651−1066310658https://pubs.acs.org/doi/10.1021/acsaelm.5c01754?fig=fig7&ref=pdfhttps://pubs.acs.org/doi/10.1021/acsaelm.5c01754?fig=fig7&ref=pdfhttps://pubs.acs.org/doi/10.1021/acsaelm.5c01754?fig=fig7&ref=pdfhttps://pubs.acs.org/doi/10.1021/acsaelm.5c01754?fig=fig7&ref=pdfpubs.acs.org/acsaelm?ref=pdfhttps://doi.org/10.1021/acsaelm.5c01754?urlappend=%3Fref%3DPDF&jav=VoR&rel=cite-asdistortion in the digital microscope system. These edge-relatedirregularities were then removed, resulting in a more refinedcontrast vs time graph (Figure 6c), which accuratelyrepresented the true EC response of the device.To better understand the spatial variation in the ECresponse, the device was first divided into five vertical regions(columns 1−5) and five horizontal regions (rows 1−5), eachrepresented in distinct colors. The contrast evolution for thesevertical regions, relative to the overall device behavior, isshown in Figure S4 through color-coded contrast−timeprofiles. The reaction times (90% contrast change) werecalculated for each region and visualized using 2D heatmaps. Inthe vertical analysis (Figure S5), columns 1 and 5,corresponding to the left and right edge regions, exhibitedfast EC transition with similar reaction times ranging from 0.8to 1.7 s. In contrast, the central columns (2, 3, and 4) displayedslow responses with almost similar reaction times ranging from1.0 to 2.0 s. Similarly, in the horizontal analysis (Figures S6and S7), rows 1 and 5 (apricot and pink), corresponding to thetop and bottom edge regions, respectively, showed fasterresponses with reaction times of 0.8 to 1.7 s and 0.9 to 1.8 s.The central rows 2, 3, and 4 (green, periwinkle, and yellow)exhibited slower responses with reaction times between 1.0and 2.0 s. This consistent trend across both vertical andhorizontal divisions highlights distinct spatial variations, withthe edges exhibiting faster switching dynamics than the centralregions.Based on the results from the vertical and horizontalanalyses, the device was further subdivided into five discreteregions for more localized analysis: four peripheral cornerregions (1−4, green) and one central region (5, orange), asshown in Figure 7a. The contrast dynamics for each regionwere assessed by plotting contrast−time profiles (Figure 7b−f), providing a clear comparison of switching behavior acrossthe device. Reaction times to reach 90% contrast werecalculated for each region and visualized as 2D heat maps(Figure 8). The four corners exhibited similar EC responses,with maximum reaction times of 1.5−1.6 s, indicatingconsistent and efficient switching across these peripheralzones. In contrast, the central region displayed a broader andlonger reaction time range of 1.6−1.95 s, reflecting slower Fe2+→ Fe3+ transitions in this area. The average τ and R values foreach region were obtained using the same nonlinear curvefitting method described earlier, summarized in Table 2.Among the corner regions, region 1 showed the lowest averageresistance (136.8 Ω·cm2) along with a faster reaction timerange (0.8−1.5 s), whereas the central region exhibited thehighest average resistance (185.7 Ω·cm2), correlating with itsslower switching behavior.To further examine the spatial characterization, the fourregions immediately adjacent to the central area were analyzedseparately (highlighted in green in the grayscale map of theECD in Figure S8), along with their corresponding contrast−Figure 8. Heat map visualization of the reaction times required to reach 90% contrast change for five defined regions of the ECD: four cornerregions (1−4) and one central region (5). The pixel ranges (X, Y) used to define each region are as follows: region 1 = (0−17, 0−19), region 2 =(70−87, 1−19), region 3 = (0−17, 77−95), region 4 = (70−87, 77−95), and region 5 = (36−52, 39−57). The color bars (0.8−1.95 s) representthe reaction-time scale. The heat map highlights spatial differences in switching behavior, with faster response observed in the peripheral cornerregions (0.8−1.6 s) and slower switching in the central region (1.6−1.95 s).Table 2. Summary of the Average τ and R Values at Regions1−5 (Corners and Center)Region Average tau value (τ) Average resistance, R (Ω·cm2)1 1.82 ± 0.12 136.82 1.86 ± 0.09 139.83 1.92 ± 0.11 144.44 1.99 ± 0.09 149.65 2.47 ± 1.81 185.7ACS Applied Electronic Materials pubs.acs.org/acsaelm Articlehttps://doi.org/10.1021/acsaelm.5c01754ACS Appl. Electron. Mater. 2025, 7, 10651−1066310659https://pubs.acs.org/doi/suppl/10.1021/acsaelm.5c01754/suppl_file/el5c01754_si_001.pdfhttps://pubs.acs.org/doi/suppl/10.1021/acsaelm.5c01754/suppl_file/el5c01754_si_001.pdfhttps://pubs.acs.org/doi/suppl/10.1021/acsaelm.5c01754/suppl_file/el5c01754_si_001.pdfhttps://pubs.acs.org/doi/suppl/10.1021/acsaelm.5c01754/suppl_file/el5c01754_si_001.pdfhttps://pubs.acs.org/doi/suppl/10.1021/acsaelm.5c01754/suppl_file/el5c01754_si_001.pdfhttps://pubs.acs.org/doi/10.1021/acsaelm.5c01754?fig=fig8&ref=pdfhttps://pubs.acs.org/doi/10.1021/acsaelm.5c01754?fig=fig8&ref=pdfhttps://pubs.acs.org/doi/10.1021/acsaelm.5c01754?fig=fig8&ref=pdfhttps://pubs.acs.org/doi/10.1021/acsaelm.5c01754?fig=fig8&ref=pdfpubs.acs.org/acsaelm?ref=pdfhttps://doi.org/10.1021/acsaelm.5c01754?urlappend=%3Fref%3DPDF&jav=VoR&rel=cite-astime profiles, to investigate whether they acted as transitionalzones between the high-performing edges and the slower-reacting center. The spatial heat maps of reaction times areshown in Figure S9, and the average τ and R values for theseintermediate regions are summarized in Table S4. Theseadjacent regions exhibited intermediate behavior, with reactiontimes ranging from 1.5 to 1.85 s, average τ values between 2.31and 2.39, and moderate resistances of 173.7−179.7 Ω·cm2.The data suggest that these transitional zones bridge the gapbetween the low-resistance, fast-switching corners and thehigh-resistance, slower central area, providing further insightinto the spatial gradients in switching kinetics and resistancevariation within the device.Artifact Filtering and Validation. High-contrast seg-ments caused by optical artifacts (e.g., focus gradients or lensedge effects) were manually identified and excluded based ontheir initial grayscale intensity values (≥84, range 84−105).The filtering was performed directly on the Excel data setbefore further analysis. To confirm that this correction did notinfluence the quantitative results, the average τ values of tworepresentative corner regions were compared before and afterremoval of these segments (Table S5). The τ values changedby less than 1%, confirming that artifact filtering had nomeasurable effect on the overall spatial trend. Representativebefore-and-after maps are shown in Figure S10.Overall, the combination of image-based analysis and asimplified RC model provides a rapid and noninvasive methodfor mapping spatially resolved composite resistances in largeECD. Although the calculated resistances represent acomposite of multiple contributions, this approach capturesthe net EC limitations controlling switching behavior andoffers a powerful diagnostic tool for analysis, device design, andprocess optimization.Furthermore, to validate the accuracy of the image-basedanalysis, a comparative measurement was conducted usingconventional UV−Vis spectroscopy on a separate polyFeL1-based ECD at a wavelength of 580 nm. The transmittance−time response at this wavelength showed a bleaching responsetime of 4.5 s to achieve 90% optical change. In comparison, theimage-based method resolved pixel-level reaction times rangingfrom 2.0 to 4.25 s for a 90% contrast change (Figure S11). Theclose agreement between the UV−Vis and image-based resultsconfirms the reliability and effectiveness of the proposedmethodology for quantifying EC switching kinetics. Notably,whereas UV−Vis spectroscopy provides information from asmall, localized region, the image-based approach enablessimultaneous spatiotemporal analysis across the entire devicearea.Finally, pixel distribution histograms were constructed(Figure 9) to illustrate the brightness levels (X-axis: pixelvalue) and their respective frequencies (Y-axis: frequency) attimes of 0 and 3 s during operation of the ECD. At 0 s, thehistogram reveals a prominent peak in the lower brightnessrange (∼45−70 pixel value), indicating that the device had notundergone its transition. This was consistent with the expectedstate of the device before activation. In contrast, at 3 s, thehistogram showed a peak in the higher brightness range(∼200−230 pixel value), reflecting the completed transition ofthe EC device to its colorless state. This further confirms theeffective transition of the device in response to the appliedvoltage.Choice of Segmentation. To evaluate the robustness ofthe pixel-averaging segmentation, the same data set wasreanalyzed with four different averaging windows (1 × 1, 3 × 3,5 × 5, and 10 × 10 pixels), and the results were summarized inTable S6. As shown in Figure S12, finer segmentations (1 × 1and 3 × 3) preserved high spatial variation but includedsignificant noise due to pixel-level fluctuations. Increasing theaveraging window to 5 × 5 significantly reduced noise whileretaining local switching details, and further averaging to 10 ×10 produced overly smoothed profiles, merging neighboringfast and slow regions. The overall switching trend remainedconsistent across segment sizes 5 × 5 and 10 × 10 (reactiontimes, 0.8−2.0 s), shown in Figure S13, confirming that thespatial kinetics are robust against segmentation choice.Corresponding τ and R values extracted from representativesegments, in Figure S14, followed the same pattern, edge
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