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1. Gold and silver oxide conducting nanocomposite cathode for glucose biofuel cell

   https://doi.org/10.1109/SENSORS47087.2021.9639600  

   Banerjee, Saikat; Nguyen, Mathew; Slaughter, Gymama (October 2021, 2021 IEEE Sensors)

   A glucose biofuel cell on a flexible bacterial nanocellulose film was prepared. The bioelectrodes were printed using gold ink as the conductive material. The anode was modified with colloidal platinum for the oxidation of glucose. The cathode was modified with a nanocomposite comprising gold nanoparticles (AuNPs) and silver oxide (Ag2O) nanoparticles. The cathode was characterized via cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), and UV spectroscopy techniques. The assembled biofuel cell generated a maximum open circuit voltage (V oc ) of 0.485 V, short circuit current (I sc ) of 0.352 mA/cm 2 , and a maximum peak power density (P max ) of 0.032 mW/cm 2 when operating in 30 mM concentration. This system showed a stable and linear performance with a linear range of 1 mM to 30 mM glucose. The gold printed electrode process is applicable to the development of wearable and implantable abiotic biofuel cell. 

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2. Data from: A look inside a flexible open-source scanning electrochemical probe microscope

   https://doi.org/10.5061/dryad.9p8cz8wx3  

   McKelvey, Kim; Edwards, Martin; Kang, Minkyung; Cabre, Marc; Jones, Nicholas; Unwin, Patrick (December 2025, Dryad)

   Scanning electrochemical probe microscopy (SEPM) maps and investigates the chemical and physical properties of surfaces and interfaces using a micro- or nanoscale electrochemical probe, e.g., a microelectrode or a nanopipette, positioned close to an interface of interest. SEPMs share a common general architecture but are distinct from each other through the choice of probe and in the different physicochemical properties of the sample that can be investigated, including, among others, interfacial charge transfer rates, topography, permeability, or surface charge. Thus, a single instrument, with an appropriately flexible control system, can facilitate widespread access to the family of SEPM techniques - scanning electrochemical microscopy (SECM), scanning ion conductance microscopy (SICM), scanning electrochemical cell microscopy (SECCM), the scanning micropipette contact method (SMCM), and hybrid varieties of these techniques. Herein, we describe in detail a flexible open-source SEPM instrument that can perform common and widely applicable SEPM techniques and experimenter-defined methodologies, with minimal programming from the user. The instrument makes use of a field programmable gate array (FPGA)-based data acquisition card, and this contribution further illustrates the benefits of adopting FPGA architecture generally in electrochemical instrumentation. We describe the software and hardware for the instrument, using examples from the literature to illustrate how common SEPM operation modes and hyphenated techniques are readily implemented. Additionally, to demonstrate the application of custom-developed scanning protocols, we briefly present some further experimental examples. This Tutorial seeks to serve the needs of expert users of SEPMs and encourage new entrants alike.  To this end, to encourage those who are interested in either setting up their own instruments or making optimal use of commercially available instruments, we also briefly include some more basic and general information on SEPM techniques and uses, to put the more advanced work and instrumental aspects in context. # Data from: A look inside a flexible open-source scanning electrochemical probe microscope Dataset DOI: [10.5061/dryad.9p8cz8wx3](https://doi.org/10.5061/dryad.9p8cz8wx3) ## Description of the data and file structure Comma-separated text file representing SICM imaging of topography and BH~4~^-^ electrooxidation on a Au nanocluster (AuNC) in 30 mM NaOH (pH 12.5) aqueous solution with 3 mM NaBH~4~. Figures and tables refer to the associated primary article. ### Files and variables * ScanHopping.tsv Columns represent: | Data Channel Name | Units | Description | Breakout Box Connector | | :---------------- | :----------------------- | :---------------------------------------------------------------------------------------------------------- | :--------------------- | | X | µm | Measured X Piezo Position | AI-0 | | Y | µm | Measured Y Piezo Position | AI-1 | | Z | µm | Measured Z Piezo Position | AI-2 | | V1 | volts | Voltage 1 | AO-3 | | V2 | volts | Voltage 2 | AO-4 | | Current1 | amperes | Current 1 | AI-3 | | Current2 | amperes | Current 2 | AI-4 | | Current3 | amperes | Current 3 | AI-6 | | Feedback Type | integer | Feedback type for z-piezo control. See Table S2 details. |   | | Line Number | integer | Indicates the waypoint number being approached (starts = 1 and increments by 1 as each waypoint is reached) |   | | Lock-in Amplitude | amperes[^\[1\]^](#_ftn1) | Oscillation amplitude (rms/peak-to-peak depending on the lock-in model) | AI-5 | | Lock-in phase | degrees | Oscillation phase vs the reference phase (from lock-in amplifier). | AI-7 | | δ*t*~data~ | seconds | Time between data point and the preceding data point (see above for definition) |   |   --- [\[1\]](#_ftnref1) This presupposes that the current is being fed into the lock-in amplifier; a common setup that is shown in Figure S8. If other inputs are fed to the lock-in units will be different. 

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3. Programmable assembly of mechanically robust and functional polymer–spore biocomposites in organic solvent

   https://doi.org/10.5061/dryad.573n5tbmt  

   Kawada, Masamu; Cui, Ziyu; Chen, Justin; Dorit, Reis; Cho, Jaeho; Sim, Seunghyun (January 2025, Dryad)

   {"Abstract":["Microbial biocomposites offer genetically programmable and regenerative\n functionality, but their mechanical tunability remains limited by the mild\n conditions required for biological activity and viability. Here, we report\n the programmable self-assembly of Bacillus subtilis spores with\n benzalcyanoacetate (BCA)-functionalized polymers to form robust composites\n exhibiting tunable viscoelastic and tensile properties. Surface-exposed\n cysteines on the spore coat react with BCA motifs, forming dynamic\n thia-Michael networks with Young’s moduli of >100 MPa. Systematic\n variation of BCA reactivity and comonomer-dependent polymer dynamics\n enabled control over stiffness, stress-relaxation behavior, microscale\n morphology, and covalent biocontainment. Incorporation of engineered\n spores confers catalytic function that can be regenerated following\n solvent-triggered disassembly. This work establishes a modular platform\n for constructing biocomposites that are both mechanically and genetically\n programmable, bridging the synthetic and biological domains through\n molecularly defined interfaces."],"TechnicalInfo":["# Programmable assembly of mechanically robust and functional\n polymer–spore biocomposites in organic solvent Dataset DOI:\n [10.5061/dryad.573n5tbmt](10.5061/dryad.573n5tbmt) ## Description of the\n data and file structure The dataset folder contains tabular data and TIFF\n files that are organized into folders that correspond to each figure set\n found in the journal article and supporting information. For example, the\n folder named **Figure 1** contains data that was used to create Figure 1.\n Below are the background and table of contents pertaining to each dataset\n folder. More detailed descriptions of experimental methods can be found in\n the main text and supporting information. ### Files and variables ###\n File: Dryad.zip #### **Folder: Figure 1** **Description:** Data in Figure\n 1 were collected to investigate whether the key chemical motif\n benzalcyanoacetate (BCA) can covalently bind to cysteine residues on spore\n surface proteins.  Fluorescent nitrobenzoxadiazole–BCA (NBD–BCA) probes\n were synthesized and incubated with Bacillus subtilis spores in dimethyl\n sulfoxide (DMSO, 120 µM) at 25 °C and 800 rpm for 20 h to examine whether\n thia-Michael (tM) linkages can be formed on the spore surface proteins. A\n control probe without BCA, NBD–OH, was also incubated with spores as a\n control. Aryl substituents (R = CF~3~, H, OMe) on the BCA were varied to\n test the hypothesis that the extent of spore labeling can be adjusted\n based on the electronic profile of the BCA. TIFF files correspond to the\n fluorescence microscopy images of spores after incubation with the probes\n and washing with DMSO and were set to the same brightness contrast setting\n to visualize the relative differences in spore surface fluorescence that\n should represent relative spore labeling differences between different\n probes. Normalized fluorescence was calculated by subtracting  (bulk\n fluorescence of spore samples - supernatant fluorescence) / (OD600 of\n spore samples - OD600 of supernatant), of triplicate spore samples are\n tabulated in .txt files for spore labeling and are unitless, relative\n measurements. Methyl esters of BCA small molecules were also synthesized\n and incubated in DMSO with B. subtilis spores to test whether similar\n covalent bonding patterns can be observed in NMR and Rman spectroscopy.\n ^1^H NMR spectroscopy was used to confirm diagnostic peaks belonging to\n the expected BCA–spore tM adducts could be detected. Numerical values of\n ^1^H NMR signal intensities against chemical shifts were tabulated in .txt\n files for DMSO-d~6~ samples of BCA–CF~3~, BCA–CF~3~ with equimolar amounts\n of 1-octanethiol, and 200 mM of BCA–CF~3~ with 1 mg/mL of B. subtilis\n spores.  Raman spectroscopy was used to detect a characteristic shift in\n the CN peak associated with tM adduct formation. Numerical values of Raman\n signal intensities against Raman shifts were tabulated in .txt files for\n DMSO-d~6~ samples of 1 mg/ mL B. subtilis spores with 200 mM each of\n BCA–CF~3~, BCA–H, and BCA–OMe (n = 7 for BCA–CF~3~, n = 5 for BCA–H and\n BCA–OMe). *Table of contents:* 1mg/mL&200mM_NMR Detection of Spore\n Surface tM Bond (CF3).txt 120uM_5wash_NBD-CF3_gfp.TIFF\n 120uM_5wash_NBD-OMe_gfp.TIFF 120uM_5wash_NBD-OH_gfp.TIFF\n 120uM_5wash_NBD-H_gfp.TIFF Average_BCA-CF3-200mM-PY79-1mgmL-DMSO-spore.txt\n Average_BCA-OMe-200mM-PY79-1mgmL-DMSO-spore.txt\n Average_BCA-H-200mM-PY79-1mgmL-DMSO-spore.txt\n NMR_BCA-CF3+1-octanethiol.txt NMR of BCA-CF3.txt\n SporeLabeling_Triplciate_NFL_120uM.txt #### Folder: Figure 2\n **Background**: Data in Figure 2 were collected to demonstrate the\n assembly of B. subtilis spores with synthetic polymers bearing the BCA\n motif. A fluorescent BCA–bearing polymer (P1–NBD) was synthesized and\n incubated with B. subtilis spores in DMSO (1.2 µM) at 25 °C and 800 rpm\n for 20h to examine whether polymer–spores linkages can facilitate\n micron-scale assemblies that are observable under the fluorescence\n microscope. The TIFF file corresponds to the fluorescence microscopy image\n of the polymer–assemblies after the incubation and washing with DMSO.  To\n create macroscopic assemblies that could be characterized under shear\n rheology experiments, lyophilized B. subtilis spores were mixed with 30%\n w/v DMSO solutions of BCA–bearing polymers (P3 and P4) and were\n characterized after 2 days of benchtop incubation. Rheological\n characterizations were performed at 25 °C on a Discovery series HR-2\n hybrid rheometer from TA instruments with a 8 mm diameter parallel plate\n geometry. Frequency sweep experiments were performed under 1% strain\n amplitude and helped determine the relative shear stiffness of the\n materials depending on their BCA and comonomer side chain structure.\n Numerical value of storage (G') and loss (G") moduli over a\n range of angular frequencies (rad/s) were tabuled in .txt files with names\n beginning with "2d_DMSO leak_...". Characteristic relaxation\n times of materials made with EGMEMA-based polymers were calculated by\n taking the inverse of the crossover frequency. Hammett correlation\n analysis was performed by plotting log(𝜏R,R/𝜏R,H) against Hammett\n constants 𝜎+ of the chosen aryl substituents. Numerical values of\n log(𝜏R,R/𝜏R,H) and the corresponding 𝜎+ were tabulated in a .txt file\n beginning with "Hammett...". *Table of contents:* 2d_DMSO\n Leak_MMA_CF3 vs H vs OMe.txt 2d_DMSO Leak_CF3_MMA vs EGMEMA vs HEMA.txt\n 2d_DMSO Leak_EGMEMA_CF3 vs H vs OMe.txt Hammett +_LOG_Relaxation\n time_2d_DMSO Leak_EGMEMA_CF3 vs H vs OMe.txt WT_M90-NBD-CF3_gfp.TIFF ####\n Folder: Figure 3 **Background**: Dry biocomposite materials were made to\n demonstrate molecular-level control over the polymer–spore interface to\n program material properties, as characterized through tensile testing,\n spore leakage, and optical and scanning electron microscopy (SEM)\n experiments. To fabricate the biocomposite, lyophilized B. subtilis spores\n were mixed with 30% w/v DMSO solutions of various BCA–bearing polymers\n (P4, P7, P8, P9, P10, and P11) before being transferred to a\n polytetrafluoroethylene (PTFE) dog bone mold and were incubated overnight.\n The entire PTFE mold was then submerged in ethanol for two hours for\n solvent exchange, and the material was taken out of the mold and placed on\n a PTFE sheet for 24 hours to dry for tensile testing. Materials were\n characterized by tensile testing, and stiffness (Young's moduli) was\n used as a method to determine differences in their mechanical properties.\n Young's moduli of biocomposite materials were determined by the slope\n of the linear region of the stress-strain curve. Numerical values of\n Young's moduli were tabulated in .txt files containing the label\n "Young's Modulus". In addition, toughness was determined by\n the area under the stress-strain curve before fragmentation. Numerical\n values of toughness was tabulated in a .txt file with the label\n "Toughness". To assess the covalent biocontainment ability of\n the biocomposite materials, various material fragments were incubated in\n PBS (pH = 7.16) and shaken at 250 rpm and 37 °C for two days. Numerical\n values of solution turbidity (OD600), indicative of the extent of spore\n leakage, were measured at 16 and 40 hours and were tabulated in a .txt\n file with the label "Spore Leakage". Optical microscopy images\n were taken of the organogel precursors to assess the relative extents of\n spore integration with the polymer phase. TIFF files of the microscopy\n images were saved with the label "Optical_...". To assess the\n retention of polymer–spore interactiosn after the solvent switching and\n drying procedure, SEM images were taken of material fragments. TIFF files\n of SEM images were saved with the label "SEM_..." *Table of\n contents:* Optical_EG90CF3+SPORES.tiff Optical_EG90OME+SPORES.tiff\n Optical_EG90H+SPORES.tiff SEM_EG90_H_Spores.tif SEM_EG90_OMe_Spores.tif\n SEM_EG90_CF3_Spores.tif Triplicate_Young's Modulus_EG90-R_0414.txt\n Spore Leakage_Kinetics.txt Triplicate_Toughness_CF3_dispersity.txt\n Triplicate_Young's Modulus_CF3_spore mass.txt Triplicate_Young's\n Modulus_CF3_%BCA.txt Triplicate_Young's Modulus_CF3_dispersity.txt\n #### Folder: Figure 4 **Background**: The reversible nature of the\n BCA–thiol binding suggested a possible material disassembly pathway that\n is entropically favored in select organic solvents. Material fragments\n were thus submerged in DMSO or acetone, and the extent and efficacy of\n disassembly were characterized by optical microscopy of the mixture\n and ^1^H NMR spectroscopy of the recovered polymer. In addition, growth\n curves of the recovered spores were measured by OD600 to determine whether\n spore viability was impacted by solvent exposure. ^1^H NMR spectra of the\n recovered polymers were compared to the pristine polymer NMR spectra to\n visualize whether they could be recovered from the disassembly process.\n Numerical values of ^1^H NMR signal intensities over a range of chemical\n shifts were tabulated in .txt files with the label "NMR_...".\n The extent of disassembly was visualized by microscopy images of spores\n recovered from the disassembly attempts and were saved as TIFF files\n beginning with "Disassembly_...". To enable mass tracking,\n materials were first lyophilized and large scale disassemblies were\n attempted. Microscopy images of spores recovered were saved as TIFF files\n beginning with "Lyophilized_...". Spore viability was assessed\n using solution turbidity (OD600) tracking over time after material\n disassembly using spores recoevered from acetone-driven partial\n disassemblies. Numerical values of OD600 over 12 hours were tabulated in a\n .txt file beginning with "Recovered WT_...". *Table of\n contents:* Recovered WT_Grrowth Curve_100 uL.txt\n Disassembly_ACETONE_0.5h_trans.tiff Disassembly_DMSO_0.5h_trans.tiff\n Lyophilized material large scale disassembly_CF3_DMSO_day14.TIFF\n Lyophilized material large scale disassembly_H_DMSO_day7.TIFF Lyophilized\n material large scale disassembly_OMe_DMSO_day7.TIFF NMR_Acetone\n Disassembly_EG90CF3.txt NMR_DMSO Disassembly_EG90CF3.txt\n NMR_Pristine_EG90CF3.txt #### Folder: Figure 5 **Background**: Engineered\n APEX2-displaying spores were incorporated into the biocomposite materials\n to investigate whether bio-derived functionality could be installed into\n the hybrid system. Because organic solvents are known to damage cells and\n enzymes, the stability and activity retention of the engineered spores\n were tested by incubating them in various organic solvents and running\n activity assays. Once an ideal condition was identified, materials were\n made to demonstrate the catalytic activity of the biocomposite system. In\n addition, engineered spores were recovered and renewed to show that any\n loss in activity could be recovered by an autonomous growth process.\n Spores were incubated in various organic solvents overnight, removed from\n solvents, and resuspended in PBS at 1 mg/mL concentration with 0.1 mM\n Amplex Red and 0.2 mM hydrogen peroxide and shaken at 205 cpm and 37 °C\n for 15 hours to assess the retention of the spore's catalytic\n activity. The relative Amplex Red conversion, indicative of the activity\n level of the enzyme-displaying spore, were assesed by normalizing against\n a spore without organic solvent exposure (bulk fluorescence of the\n sample/bulk fluorescence of the spore without organic solvent\n exposure)(100%). Numerical values of the normalized conversion were\n tabulated in a .txt file starting with "Organic Solvent\n Incubation_". In addition to the enzyme activity retention, the\n effect of organic solvents on the morphology of APEX2 spores were observed\n under optical microscopy. Microscopy images were saved as TIFF files\n beginning with "TIEDAPEX2_INCUBATION_...". The activity assay\n and spore morphology experiments helped determine that acetone is the\n least harmful of the solvents. In addition, acetone could dissolve the\n polymer and help facilitate polymer–spore bond formation for effective\n material fabrication. Catalytic biocomposite materials were thus\n fabricated in acetone and were tested for their ability to convert Amplex\n Red to resorufin. Numerical values of fluorescence spectroscopy\n measurements, indicative of catalytic material activity, were tabulated in\n .txt files beginning with "Acetone Material_..." for the first\n reaction cycle and "Cycle 2_..." for the second reaction cycle.\n Repeated activity showed the reusability of the material, although the\n second cycle was much slower in catalysis. Catalysis was also attempted in\n the presence of organic solvent in the reaction mixture to show its\n potential benefit in increasing substrate scope for more hydrophobic\n compounds. 12.5% acetone in PBS mixtures were used to show a lower yet\n significant activity level of the material. Because the reaction kinetics\n slowed down and the natural decomposition of Amplex Red became\n significant, bulk fluorescence values of the control mixture without the\n material were subtracted from the bulk fluorescence of the reaction\n mixture with the material. Numerical values of this corrected,\n "deltaFluorescence Intensity" were tabulated in a .txt file\n beginning with "12.5%...". Because diminished activity was\n observed in both the repeated biocatalysis and the 12.5% organic solvent\n experiments, materials were partially disassembled in acetone to recover\n and renew spores to demonstrate the recovery of catalytic activity.\n Pristine spores and renewed spores were incubated in PBS at 1 mg/mL with\n 0.1 mM Amplex Red and 0.2 mM hydrogen peroxide at 205 cpm and 37 °C for 16\n hours to compare the renewed spore activity against the expected full\n catalytic activity. Numerical values of fluorescence intensity over time\n were tabulated in a .txt file beginning with "Renewed*..."\n *Table of contents:* TIEDAPEX2_INCUBATION_PBS_trans.TIFF 12.5%\n Acetone_Corrected.txt Acetone Material_TIED APEX2_PBS_Material vs\n Control.txt Cycle 2_Triplicate_Days_Acetone Material_TIED\n APEX2_PBS_Material vs Control.txt Organic Solvent\n Incubation_TIEDAPEX2_Normalized Conversion.txt Renewed APEX2 vs\n Pristine.txt TIEDAPEX2_INCUBATION_ACETONE_trans.TIFF\n TIEDAPEX2_INCUBATION_DMSO_trans.TIFF TIEDAPEX2_INCUBATION_ETOH_trans.TIFF\n #### Folder: SI **Background**: The supporting information (SI) includes a\n comprehensive list of data that are both included and not included in the\n main article. BCA Reactivity: ^1^H NMR analysis of the tM reaction to\n determine the effects of dilution, exogeneous BCA, aryl substituent,\n solvent, and exogeneous thiol on the equilibrium. Calculated values used\n for Hammett plots and k~a~ vs. k~d~ plots. Biocatalysis: determination of\n catalytic efficacy of spores and biocomposite materials in various solvent\n conditions. Morphology of engineered spores after exposure to solvents.\n Disassembly: Optical micrographs showing polymer–spore suspensions in\n various conditions. GPC: GPC traces of polymers used in this project.\n Material microscopy: optical and SEM images of polymer and spore mixtures\n (materials). Rheology: G' and G" values of the materials over a\n range of angular frequency at 1% shear strain and calculated values of\n relaxation times. Solvent Removal: mass tracking of materials over time to\n obverse solvent leakage and evaporation. Spore Labeling: detection of tM\n bond formation on spores using NMR and fluorescence measurements,\n including relevant standard curves and control samples. Spore Viability:\n Growth curve of the B. subtilis from material surface germination. Stress\n Strain Curve: stress strain curves from tensile testing. *Table of\n contents:* BCA Reactivity_%Dissociation by Dilution (concentration)\n Effect-R.txt BCA Reactivity_BCA exchange OMe vs CF3.txt BCA\n Reactivity_BCA-thiol-R_Acetone.txt BCA Reactivity_BCA-thiol-R_DMSO.txt BCA\n Reactivity_Exogeneous Thiol_Reversibility_2 equivalents.txt BCA\n Reactivity_Hammett + vs ln(kdx/kdH) BCA_DMSO.txt BCA Reactivity_Hammett +\n vs ln(Kx/KH) BCA_Acetone.txt BCA Reactivity_Hammett + vs ln(Kx/KH)\n BCA_DMSO.txt BCA Reactivity_Hammett +_LOG_Relaxation time_2d_DMSO\n Leak_EGMEMA_CF3 vs H vs OMe.txt BCA Reactivity_ka vs kd_Acetone.txt BCA\n Reactivity_ka vs kd_DMSO.txt Biocatalysis_%Organic\n Solvent_TIEDAPEX2_Normalized Conversion.txt Biocatalysis_12 5%\n Acetone_Triplicate_Days_Acetone Material_TIED APEX2_PBS_Material vs\n Control.txt\n Biocatalysis_20250226_TIEDAPEX_INCUBATION_ACETONE_expt_0005_trans.TIFF\n Biocatalysis_20250226_TIEDAPEX_INCUBATION_DMSO_expt_0010_trans.TIFF\n Biocatalysis_20250226_TIEDAPEX_INCUBATION_ETOH_expt_0003_trans.TIFF\n Biocatalysis_20250226_TIEDAPEX_INCUBATION_MEOH_expt_0005_trans.TIFF\n Biocatalysis_20250226_TIEDAPEX_INCUBATION_PBS_expt_0006_trans.TIFF\n Biocatalysis_Baseline Corrected_12 5% Acetone_Triplicate_Days_Acetone\n Material_TIED APEX2_PBS_Material vs Control.txt Biocatalysis_Renewed APEX2\n vs Pristine.txt Biocatalysis_TIEDAPEX2 Material_DMSO_Time Trace_FL.txt\n Biocatalysis_TIEDAPEX2_Normalized Final FL_Organic Solvent.txt\n Biocatalysis_TIEDAPEX2_Organic Solvent Incubation Overnight.txt\n Biocatalysis_Triplicate_Acetone Material_vs Pristine Spores.txt\n Biocatalysis_Triplicate_Days_Acetone Material_TIED APEX2_PBS_Material vs\n Control.txt Biocatalysis_Triplicate_Days_OD600_Acetone Material_TIED\n APEX2_PBS.txt Disassembly__ACETONE_8h_trans.tiff\n Disassembly_ACETONE_0.5h_trans.tiff Disassembly_ACETONE_0.5htrans.tiff\n Disassembly_ACETONE_8h_0002_trans.tiff Disassembly_ACETONE_8h_trans.tiff\n Disassembly_CF3_37C_1000RPM_0.2mM_7days_0015.TIFF\n Disassembly_CF3_37C_1000RPM_0.2mM_7days_0017.TIFF\n Disassembly_CF3_37C_1000RPM_0.2mM_7days_0027.TIFF\n Disassembly_CF3_50C_1000RPM_0.2mM_3days_0000.TIFF\n Disassembly_CF3_50C_1000RPM_0.2mM_3days_0003.TIFF\n Disassembly_CF3_50C_1000RPM_0.2mM_3days_0011.TIFF\n Disassembly_CF3_60C_1000RPM_0.2mM_1day_0012.TIFF\n Disassembly_CF3_60C_1000RPM_0.2mM_1day_0015.TIFF\n Disassembly_CF3_60C_1000RPM_0.2mM_1day_0022.TIFF\n Disassembly_CF3_70C_1000RPM_0.2mM_1day_0003.TIFF\n Disassembly_CF3_70C_1000RPM_0.2mM_1day_0004.TIFF\n Disassembly_CF3_70C_1000RPM_0.2mM_1day_0008.TIFF\n Disassembly_CF3_80C_1000RPM_0.2mM_1day.tif\n Disassembly_DMSO_0.5h_0002_trans.tiff\n Disassembly_DMSO_0.5h_0003_trans.tiff\n Disassembly_DMSO_0.5h_0004_trans.tiff Disassembly_DMSO_8h_0001_trans.tiff\n Disassembly_DMSO_8h_0003_trans.tiff Disassembly_DMSO_8h_0004_trans.tiff\n Disassembly_PY79_ACETONE_0.5h_trans.tiff Disassembly_ZOOM_CF3\n DISASSEMBLY_80C_1000RPM_0.2mM_1day_0013-1.tif Disassembly_ZOOM_CF3\n DISASSEMBLY_80C_1000RPM_0.2mM_1day.tif GPC_P1.txt GPC_P2.txt GPC_P3.txt\n GPC_P4.txt GPC_P5.txt GPC_P6.txt GPC_P7.txt GPC_P8.txt GPC_P9.txt\n GPC_P10.txt GPC_P11.txt Material\n Microscopy_Optical__EG90CF3_NOSPORE_0003.tiff Material\n Microscopy_Optical__EG90CF3+SPORES_0001.tiff Material\n Microscopy_Optical__EG90CF3+SPORES_0007.tiff Material\n Microscopy_Optical__EG90CF3+SPORES_0009.tiff Material\n Microscopy_Optical__EG90CF3+SPORES_0015.tiff Material\n Microscopy_Optical__EG90H_0414_NOSPORES_0001.tiff Material\n Microscopy_Optical__EG90H_0414_spores_0003.tiff Material\n Microscopy_Optical__EG90H_0414_spores_0009.tiff Material\n Microscopy_Optical__EG90H+SPORES_0013.tiff Material\n Microscopy_Optical__EG90H+SPORES_0014.tiff Material\n Microscopy_Optical__EG90OMe_0324_NOSPORES_0000.tiff Material\n Microscopy_Optical__EG90OME+SPORES_0013.tiff Material\n Microscopy_Optical__EG90OME+SPORES_0014.tiff Material\n Microscopy_Optical__EG90OME+SPORES_0017.tiff Material\n Microscopy_Optical__EG90OME+SPORES_0018.tiff Material\n Microscopy_Optical__EG100_NOSPORE_0002.tiff Material\n Microscopy_Optical__EG100_WT_0001_trans.tiff Material\n Microscopy_Optical__EG100_WT_0009_trans.tiff Material\n Microscopy_Optical__EG100+SPORES_0013.tiff Material\n Microscopy_Optical__EG100+SPORES_0014.tiff Material\n Microscopy_SEM__EG90_CF3_RAFT_Spores.tif Material\n Microscopy_SEM__EG90_CF3_Spore.tif Material\n Microscopy_SEM__EG90_H_Spores.tif Material\n Microscopy_SEM__EG90_OMe_Spores.tif Material\n Microscopy_SEM_EG100_Spores_001.tif NMR_BCA-CF3+1-octanethiol.txt\n NMR_BCA-H+1-octanethiol.txt NMR_BCA-OMe+1-octanethiol.txt Rheology_2d_DMSO\n Leak_EGMEMA_CF3 vs H vs OMe.txt Rheology_Relaxation time_2d_DMSO\n Leak_EGMEMA_CF3 vs H vs OMe.txt Solvent Removal_DMSO\n evaporation_Dogbone_CF3-M-EG-H.txt Solvent Removal_EtOH\n evaporation_Dogbone_CF3-M-EG-H.txt Spore Labeling_1mg/mL&200mM_NMR\n Detection of Spore Surface tM Bond (CF3).txt Spore\n Labeling_1mg/mL&200mM_NMR Detection of Spore Surface tM Bond (H).txt\n Spore Labeling_1mg/mL&200mM_NMR Detection of Spore Surface tM Bond\n (OMe).txt Spore Labeling_680_DMSO_20241003_Spore\n Labeling_Maleimide_TCEP.txt Spore\n Labeling_1015_NBD-OH_EndPoint_Fluorescence vs. Concentration_Standard\n Curve.txt Spore Labeling_1127_SporeLabeling_Triplciate_NFL_120uM.txt Spore\n Labeling_1127_SporeLabeling_Washes_120uM_Triplicates.txt Spore\n Labeling_2024_1003_sCy5_WT_AMINE_0004_cy5.TIFF Spore\n Labeling_2024_1003_sCy5_WT_AMINE_0004_trans.TIFF Spore\n Labeling_2024_1003_sCy5_WT_MALEIMIDE_0002_cy5.TIFF Spore\n Labeling_2024_1003_sCy5_WT_MALEIMIDE_0002_trans.TIFF Spore\n Labeling_2024_1003_sCy5_WT-red_AMINE_0004_cy5.TIFF Spore\n Labeling_2024_1003_sCy5_WT-red_AMINE_0004_trans.TIFF Spore\n Labeling_2024_1003_sCy5_WT-red_MALEIMIDE_0004_cy5.TIFF Spore\n Labeling_2024_1003_sCy5_WT-red_MALEIMIDE_0004_trans.TIFF Spore\n Labeling_2024_1011_WT_M90-NBD-CF3_0005_gfp.TIFF Spore\n Labeling_2024_1011_WT_M90-NBD-CF3_0005_trans.TIFF Spore\n Labeling_2024_1011_WT_M90-NBD-CF3_0022_gfp.TIFF Spore\n Labeling_2024_1011_WT_M90-NBD-CF3_0022_trans.TIFF Spore\n Labeling_2024_1011_WT_M90-NBD-CF3_0024_gfp.TIFF Spore\n Labeling_2024_1011_WT_M90-NBD-CF3_0024_trans.TIFF Spore\n Labeling_2024_1011_WT_M90-NBD-CF3_0028_gfp.TIFF Spore\n Labeling_2024_1011_WT_M90-NBD-CF3_0028_trans.TIFF Spore\n Labeling_2024_1011_WT_M90-NBD-CF3_0030_gfp.TIFF Spore\n Labeling_2024_1011_WT_M90-NBD-CF3_0030_trans.TIFF Spore\n Labeling_2024_1011_WT_M90-NBD-CF3_0034_gfp.TIFF Spore\n Labeling_2024_1011_WT_M90-NBD-CF3_0034_trans.TIFF Spore\n Labeling_2024_1011_WT_M90-NBD-OMe_0017_gfp.TIFF Spore\n Labeling_2024_1011_WT_M90-NBD-OMe_0017_trans.TIFF Spore\n Labeling_2024_1011_WT_M90-NBD-OMe_0023_gfp.TIFF Spore\n Labeling_2024_1011_WT_M90-NBD-OMe_0023_trans.TIFF Spore\n Labeling_2024_1011_WT_M90-NBD-OMe_0028_gfp.TIFF Spore\n Labeling_2024_1011_WT_M90-NBD-OMe_0028_trans.TIFF Spore\n Labeling_2024_1011_WT_M90-NBD-OMe_0035_gfp.TIFF Spore\n Labeling_2024_1011_WT_M90-NBD-OMe_0035_trans 2.TIFF Spore\n Labeling_2024_1011_WT_M90-NBD-OMe_0044_gfp.TIFF Spore\n Labeling_2024_1011_WT_M90-NBD-OMe_0044_trans.TIFF Spore\n Labeling_2024_1127_120uM_5wash_NBD-CF3_0004_gfp.TIFF Spore\n Labeling_2024_1127_120uM_5wash_NBD-CF3_0004_trans.TIFF Spore\n Labeling_2024_1127_120uM_5wash_NBD-H_0002_gfp.TIFF Spore\n Labeling_2024_1127_120uM_5wash_NBD-H_0002_trans.TIFF Spore\n Labeling_2024_1127_120uM_5wash_NBD-OH_0002_gfp.TIFF Spore\n Labeling_2024_1127_120uM_5wash_NBD-OH_0002_trans.TIFF Spore\n Labeling_2024_1127_120uM_5wash_NBD-OMe_0000_gfp.TIFF Spore\n Labeling_2024_1127_120uM_5wash_NBD-OMe_0000_trans.TIFF Spore\n Labeling_20250509_MaleimideCappedSpores_0009_cy5.tiff Spore\n Labeling_20250509_MaleimideCappedSpores_0009_trans.tiff Spore\n Labeling_Full Spectrum_DMSO_20241011_M90_CF3_OMe.txt Spore\n Labeling_NFL_540_DMSO_20241011_M90_CF3_OMe.txt Spore Labeling_Stock\n Solution Fluorescence_100x dilution.txt Spore\n Viability_Germination_EG90_CF3.txt Stress Strain Curve_EG90_CF3_1.txt\n Stress Strain Curve_EG90_CF3_2.txt Stress Strain Curve_EG90_CF3_3.txt\n Stress Strain Curve_EG90_CF3_halfspores_1.txt Stress Strain\n Curve_EG90_CF3_halfspores_2.txt Stress Strain\n Curve_EG90_CF3_halfspores_3.txt Stress Strain\n Curve_EG90_CF3_nospores_1.txt Stress Strain Curve_EG90_CF3_nospores_2.txt\n Stress Strain Curve_EG90_CF3_nospores_3.txt Stress Strain\n Curve_EG90_CF3_RAFT_0 29CTA_2.txt Stress Strain Curve_EG90_CF3_RAFT_0\n 29CTA_3.txt Stress Strain Curve_EG90_CF3_RAFT_0 29CTA.txt Stress Strain\n Curve_EG90_H_1.txt Stress Strain Curve_EG90_H_2.txt Stress Strain\n Curve_EG90_H_3.txt Stress Strain Curve_EG90_H_nospores_1.txt Stress Strain\n Curve_EG90_H_nospores_2.txt Stress Strain Curve_EG90_H_nospores_3.txt\n Stress Strain Curve_EG90_OMe_0414_2.txt Stress Strain\n Curve_EG90_OMe_0414_3.txt Stress Strain Curve_EG90_OMe_0414.txt Stress\n Strain Curve_EG90_OMe_nospore_0414_2.txt Stress Strain\n Curve_EG90_OMe_nospore_0414.txt Stress Strain Curve_EG95_CF3_2.txt Stress\n Strain Curve_EG95_CF3_3.txt Stress Strain Curve_EG95_CF3.txt Stress Strain\n Curve_EG100_spores_1.txt Stress Strain Curve_EG100_spores_2.txt Stress\n Strain Curve_EG100_spores_3.txt ## Access information Other publicly\n accessible locations of the data: *\n [https://pubs.acs.org/doi/10.1021/jacs.5c13976](https://pubs.acs.org/doi/10.1021/jacs.5c13976) Data was derived from the following sources: *"]} 

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4. Data from: Frugivore traits predict plant-frugivore interactions using generalized joint attribute modeling

   https://doi.org/10.5061/dryad.2v6wwpzwg  

   Yohe, Laurel; Miller, Leith; Kaliszewska, Zofia; Whitehead, Susan; Santana, Sharlene; Davalos, Liliana (January 2025, Dryad)

   Under an adaptive hypothesis, the reciprocal influence between mutualistic plants and frugivores is expected to result in suites of matching frugivore and plant traits that structure fruit consumption. Recent work has suggested fruit traits can represent adaptations to broad groups of functionally similar frugivores, but the role of frugivore traits and within-species variation in structuring fruit consumption is less understood. To address these knowledge gaps, we assess the presence of reciprocal trait matching for the mutualistic ecological network comprising of Carollia bats that feed on and disperse Piper seeds. We used generalized joint attribute modeling (GJAM), a Bayesian modeling approach that simultaneously accounts for multiple sources of variance across trait types. In support of frugivore adaptation to their dietary composition and suggesting niche partitioning among Carollia bats, we find differential consumption of a suite of Piper species influenced by bat traits such as body size; however, the Piper morphological traits considered had no effect on bat consumption. Slow evolutionary rates, dispersal by other vertebrates, and unexamined fruit traits, such as Piper chemical bouquets, may explain the lack of association between bat Piper consumption and fruit morphological traits. We have identified a potential asymmetric influence of frugivore traits on plant–frugivore interactions, providing a template for future trait analyses of plant–animal networks. As intraspecific trait variation is rarely included in studies on trait matching, this paper contributes to closing that important knowledge gap. # Data from: Frugivore traits predict plant-frugivore interactions using generalized joint attribute modeling [https://doi.org/10.5061/dryad.2v6wwpzwg](https://doi.org/10.5061/dryad.2v6wwpzwg) Bayesian models relating: 1\. head.R: relates *Carollia* traits to bite force (performance) via hierarchical ML models 2\. carollia3_0.R: relates *Carollia* traits to bite force (performance) via hierarchical Bayesian models 3\. gjam generated model of consumption relationship to traits for *Carollia* bats 4\. gjam processed model outputs 5\. piper.R: relates *Piper* traits and GJAM coefficients obtained from 3 and 4 ## Description of the data and file structure All cells marked as NA lacked data and correspond to missing data. ## Code/Software 1\. head.R: relates *Carollia* traits to bite force (performance) via hierarchical ML models. Requires data file bat_l_biteBody.csv and R library lme4. Prints results to .txt file. 2\. carollia3_0.R: relates *Carollia* traits to bite force (performance) via hierarchical Bayesian models. Requires data file bat_l_biteBody.csv, R library R2Jags, and 3 Jags files. The three Jags files are: * carollia_bf_size.txt * carollia_bf_mass.txt * carollia_bf_head.txt carollia3_0.R prints out the results of hierarchical Bayesian regressions in txt and saves an Rdata file. 3\. niche_Carollia.R: generates gjam model of consumption relationship t traits for *Carollia* bats Requires data files: * carollia_niche_xdata.csv: x or explanatory variables, bat traits * carollia_niche_ydata_trim.csv: y or response variables, bat consumption of *Piper* fruit from different species * carollia_type.csv: individual bat assignment to one of 3 species * and R libraries gjam, reshape2 and plyr plus function * bayesReg.R (which codes a function to run a Tobit and Bayesian regression from the NEON example here: [https://rstudio-pubs-static.s3.amazonaws.com/710083_480b1b43b4f0470691e95302483fdc08.html](https://rstudio-pubs-static.s3.amazonaws.com/710083_480b1b43b4f0470691e95302483fdc08.html)). This script generates the bayesian gjam model and saves an Rdata file. 4\. plot_Carollia_v2.r processes gjam model outputs. Requires data files: * models_Carollia.Rdata * and R libraries gjam, reshape2, plyr, ggplot2, MCMCvis and wesanderson This script generates the standardized summary and prints out a file called piper_medians.csv 5\. piper.R relates *Piper* traits and gjam coefficients obtained from steps 3 and 4.. Requires data files: * piper.nex: phylogeny of *Piper* plants * piper_k_traits.csv: correspondence between *Piper* traits and *Piper* species * output.csv: this is processed from piper_medians.csv to separate relate Piper species to bat trait values resulting from gjam * Requires R libraries MCMCglmm and geiger This script prints out the results of phylogenetic Bayesian regressions of gjam outputs as a function of *Piper* traits in txt and saves a Rdata file. 

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5. Pulsed Current Anion Sensor Data for Multiple Nonspecific Ion Selective Electrodes - Individual Ion Responses

   https://doi.org/10.5281/zenodo.20057671  

   Mehregan, Shima; Koenig, Henry; Murphy, Will EM; Gopaul, Colin; Khoury, Anthony; Veal, Charlie; Anderson, Derek; Brorsen, Kurt; Young, Matthias (January 2026, Zenodo)

   Dataset Overview This dataset contains time-resolved electrochemical and pH-related measurements from polymer-coated polypyrrole (PPy) sensing electrodes evaluated across multiple anions, concentrations, and pulse-current settings. The data are provided in long-format Parquet for scalable analysis and plotting. Experimental Setup and Fabrication Summary An AT-cut, 5 MHz, gold-coated quartz crystal (Phillip Technologies) mounted on a QCM-200 controller (Stanford Research Systems) was used as the substrate. PPy was electrodeposited onto the gold surface at 0.8 V vs. Ag/AgCl for 2 minutes in aqueous solution containing 0.1 M pyrrole and 0.1 M sodium nitrate under continuous UHP argon purge. After deposition, films were rinsed with methanol and dried at 50 C for 10 minutes. A polymer-screening workflow was used to identify stable, spin-coatable nonspecific coatings. Candidate polymers were evaluated for solvent compatibility, film formation, and aqueous stability. PAN, PVA, and PVC were down-selected for continued testing (using DMF, methanol, and DMSO, respectively) based on solubility, coating quality, and stability in aqueous conditions. Polymer coatings were prepared at 1 mg/mL and spin-coated onto PPy-coated quartz using 150 uL per substrate with a two-step spin program (1000 rpm for 40 s, then 3000 rpm for 10 s), followed by acetone rinse and argon drying. Electrochemical Measurement Summary Electrochemical response was measured for seven anions: nitrate (NO3-) chloride (Cl-) perchlorate (ClO4-) sulfate (SO4^2-) bromide (Br-) carbonate (CO3^2-) hydrogen phosphate (HPO4^2-) Each anion was tested at 10^-1 M, 10^-3 M, and 10^-5 M. Pulse-current measurements used a two-step sequence at each current setting: anodic pulse (10 s) to measure overpotential response vs. OCV cathodic pulse (10 s) to discharge/reset the PPy layer Ten current levels were tested from 0.1 uA to 100 uA. The overall campaign design comprised 21 measurement sets, tested across three electrode types with triplicate electrodes, yielding 1,890 individual pulse-current runs. Data Utility The dataset supports: direct plotting of potential-time and pH-related traces; comparison across polymer type, ion identity, concentration, and current step; replicate-aware aggregation and statistics; quality control at the source-file grouping level without exposing internal filenames. Data Organization Format: Parquet (columnar) Rows: 16,386,405 Columns: 34 Row unit: one recorded timepoint from one measurement-file segment Key analysis dimensions: Polymer Name, Electrode Name concentration fields Ca-Cg step_index, step_type anonymized provenance flags run_id and file_id Core signal/time fields include: time/s, control/mA, I/mA Ewe/V, /V Analog IN 1/V, pH_signal_V charge, capacitance, and energy derived columns Public Provenance Model To preserve analytical grouping while removing sensitive path metadata, direct source columns were excluded from this public file and replaced by deterministic anonymized flags: run_id: experiment-level grouping identifier file_id: source-file-level grouping identifier 

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