Dataset for article Hybrid thermoresponsive κ-carrageenan-based polymer nanogels respond differently to mono- and divalent metal cations

---------------------------------------------------------------------------------------------------------------------------Dataset for article Hybrid thermoresponsive κ-carrageenan-based polymer nanogels respond differently to mono- and divalent metal cations--------------------------------------------------------------------------------------------------------------------------- ReadMe version: 1.0 (2026-08-21) Dataset version: 1.0 (2026-08-21) Dataset DOI: 10.5281/zenodo.22013218 Related paper DOI:10.1016/j.molliq.2025.128782 --------------------------------------------------------------------CONTACT-------------------------------------------------------------------- Martin Hrubý [e-mail] ORCID: 0000-0002-5075-261X Institute of Macromolecular Chemistry, Czech Academy of Sciences (IMC CAS) Heyrovského náměstí 2, 162 00 Prague 6, Czech Republic ______Creators______ Zulfiya Cernochová (ORCID 0000-0002-7119-7205; IMC CAS), [Data Curation; Formal Analysis; Investigation; Methodology; Conceptualization] Lenka Loukotová (ORCID 0000-0002-8087-1425; IMC CAS), [Investigation] Petr Štěpánek (ORCID 0000-0002-9854-3972; IMC CAS), [Project Administration; Investigation; Funding Acquisition; Formal Analysis] Franck Artzner (ORCID 0000-0002-5613-576X; CNRS, Institut de Physique de Rennes / Université de Rennes), [Methodology; Investigation; Funding Acquisition] Miroslav Šlouf (ORCID 0000-0003-1528-802X; IMC CAS), [Methodology; Investigation] Martin Hrubý (ORCID 0000-0002-5075-261X; IMC CAS), [Supervision; Project Administration; Methodology; Investigation; Funding Acquisition; Conceptualization] Peter Černoch (ORCID 0000-0002-1136-2280; IMC CAS), [Software; Investigation] --------------------------------------------------------------------DATA AVAILABILITY AND ACCESS INSTRUCTIONS-------------------------------------------------------------------- The dataset is openly accessible under the DOI listed above. The license and terms of reuse are shown in the following chapter below. --------------------------------------------------------------------LICENSE--------------------------------------------------------------------______ReadMe file license______ ReadMe by Martin Hrubý is licensed under CC BY 4.0 License information: https://creativecommons.org/licenses/by/4.0/ ______Dataset license______ Dataset for article Hybrid thermoresponsive κ-carrageenan-based polymer nanogels respond differently to mono- and divalent metal cations by Zulfiya Cernochová, Lenka Loukotová, Petr Štěpánek, Franck Artzner, Miroslav Šlouf, Martin Hrubý and Peter Černoch is licensed under CC BY 4.0 License information: https://creativecommons.org/licenses/by/4.0/--------------------------------------------------------------------DESCRIPTION AND METHODOLOGY-------------------------------------------------------------------- ______About the dataset______This dataset supports the article “Hybrid thermoresponsive κ-carrageenan-based polymer nanogels respond differently to mono- and divalent metal cations” (Journal of Molecular Liquids 440 (2025) 128782; DOI 10.1016/j.molliq.2025.128782). The study examines κ-carrageenan graft copolymers bearing thermoresponsive poly(2-isopropyl-2-oxazoline-co-2-butyl-2-oxazoline) (POX) side chains and their response to temperature and Na+, K+, Rb+, Ca2+ and Sr2+ ions using light scattering, SAXS and TEM.The supplied repository package contains DLS correlation-function exports for RbCl and SrCl2 systems, reduced one-dimensional SAXS q–I(q) data for selected Rb/Sr sample and solvent series, TEM source/intermediate/final images corresponding to Fig. 8, and the associated TEM processing notes, calculation workbook and Python scripts. It represents the deposited data subsets; it does not contain every measurement reported in the article or its supplementary information (for example, no raw 2-D SAXS detector frames are supplied). ______Ethics______This is a physicochemical materials-characterization study and the supplied dataset contains no human participants, animal subjects, personal data or sensitive personal data. No ethics-board approval or informed-consent documentation is applicable to these data. ______Sample preparation______Graft-copolymer solutions were prepared by dissolving the κ-carrageenan-graft-POX polymer in water at 5 mg/mL and stirring overnight at room temperature. An equal volume of salt solution was then added: 0.30 M NaCl, KCl or RbCl, or 0.15 M CaCl2 or SrCl2. Final nanodispersions contained 2.5 mg/mL polymer, 0.15 M monovalent-cation salt or 0.075 M divalent-cation salt. For the TEM images used in Fig. 8, the article identifies copolymer C11 in NaCl, KCl and CaCl2, prepared by the fast-drying method and negatively stained with uranyl acetate; the Fig. 8 caption reports preparation at 37 °C. ______Methods of data collection______* Dynamic light scattering (DLS) Instrument: Zetasizer Nano-ZS, model ZEN3600 (Malvern Instruments, UK). Measurements used a backscattering angle of 173° over approximately 10–50 °C. The deposited exports contain 205 measurements per salt, nominally five 60 s measurements at each temperature step, together with viscosity, count-rate and correlation-function metadata. * Static light scattering (SLS) SLS measurements reported for the study were performed at 37 °C using ALV-6000 equipment (ALV-GmbH, Langen, Germany). The article reports scattering angles of 24–149° in 4° increments, polymer concentrations of approximately 1.56–1.92 mg/mL, 0.45 µm PVDF filtration, three measurements and a 40 s acquisition time. Berry-plot analysis used ALV/static and Dynamic FIT and PLOT 4.31 10/01. The deposited data containssummary and per-concentration analysis outputs for C11_Ca, C11_K and C11_Na, including Rh, Rg, molecular weight, second virial coefficient A2, shape factor and particle density. * Small-angle X-ray scattering (SAXS) SAXS was measured on a laboratory Guinier setup at the Institut de Physique de Rennes using a Pilatus 300K detector (Dectris) and a GeniX 3D microsource (Xenocs) operated at 30 W with Cu Kα radiation, λ = 1.541 Å. The article reports q calibration with silver behenate and a measurement range of q = 0.013–1.72 Å⁻¹. The deposited workbook contains reduced q–I(q) curves at 15, 23, 29, 35, 39.5 and 45 °C for C10/Rb, C11/Sr and corresponding Rb/Sr solvent-background series. *Transmission electron microscopy (TEM) Instrument: Tecnai G2 Spirit TEM (FEI), 120 kV, standard bright-field imaging. Samples were fast-dried and negatively stained with uranyl acetate. The three deposited image series a, b and c reproduce the panels of Fig. 8 and correspond respectively to C11 in NaCl, KCl and CaCl2; the published scale bar is 500 nm. ______Methods of data processing______* DLS: the TXT files are correlation-function instrument exports. In the article, intensity correlation functions g2(t) were converted to field correlation functions g1(t) with the Siegert relation, analyzed by the REPES inverse-Laplace routine, and converted to hydrodynamic radii Rh using the Stokes–Einstein relation after confirming diffusive behavior. * SLS: data contains derived SLS quantities and exported analysis tables for C11_Ca, C11_K and C11_Na. Reports apparent quantities at the measured concentrations and summary/extrapolated Rh, Rg, Mw and A2 values; the article states that Berry plots were analyzed using ALV/static and Dynamic FIT and PLOT 4.31 10/01. * SAXS: SAXS_Data_petr_2024.xlsx contains reduced one-dimensional q–I(q) curves. *TEM:000_ReadMe_TEM.txt documents the sequence:1) calculate equivalent real-world image (RWI) dimensions in 01_same-rwi.xlsx; crop/rescale in ImageJ; then2) use 02_myimg-lm.py to convert *_uu.png images to grayscale, add labels a–c and a 500 nm scale bar (rwi = 1689.8 nm); then3) use 03_myimg_mrep.py to assemble a 1×3 montage. --------------------------------------------------------------------DATASET STRUCTURE--------------------------------------------------------------------── 000_ReadMe.txt── 001_Data.zip ├── DLS │ ├── DLS_-_RbCl.csv │ ├── DLS_-_SrCl2.csv │ ├── DLS_C10_CaCl2.csv │ ├── DLS_C10_KCl.csv │ ├── DLS_C10_NaCl.csv │ ├── DLS_C10_RbCl.csv │ ├── DLS_C10_SrCl2.csv │ └── DLS_C11_RbCl.csv ├── SAXS │ ├── SAXS_C10Rb.csv │ ├── SAXS_C11Sr.csv │ ├── SAXS_RbSolvant.csv │ └── SAXS_SrSolvant.csv ├── SLS │ ├── SLS_-_General.csv │ ├── SLS_C11_Ca_Dz+Rh.csv │ ├── SLS_C11_Ca_MwRg.csv │ ├── SLS_C11_K_DzRh.csv │ ├── SLS_C11_K_MwRg.csv │ ├── SLS_C11_Na_DzRh.csv │ └── SLS_C11_Na_MwRg.csv └── TEM ├── 000_ReadMe_TEM.txt ├── 01_same-rwi.xlsx ├── 02_myimg-lm.py ├── 03_myimg_mrep.py ├── 03_myimg_mrep.py.png ├── a_g74_024_52kx.tif ├── a_g74_024_52kx_u.tif └── a_g74_024_52kx_uu.png --------------------------------------------------------------------FILENAME STRUCTURE--------------------------------------------------------------------* DLS DLS_[sample]_[salt].csv [sample] = C10 or C11 (- for generalized data) [salt] = RbCl or SrCl2 Examples: DLS_data_RBCl.csv Decimal operator: . Delimiter: , Dimensions & units in row 1 * SLS DLS_[sample]_[salt].csv [sample] = C10 or C11 (- for generalized data) [salt] = RbCl or SrCl2 Examples: DLS_data_RBCl.csv Decimal operator: . Delimiter: , Dimensions & units in row 1 * SAXS SAXS_[sample].csv [sample] = C10Rb, C11Sr, Rb_solvant and Sr_solvant. Examples: SAXS_C10Rb.csv Decimal operator: , Delimiter: ; Dimensions & units in row 1 * TEM [panel]_g74_[imageNo]_[magnification]x.tif [panel] = a/b/c in the final Fig. 8 montage these map respectively to NaCl/KCl/CaCl2 g74 = internal experiment/session identifier [imageNo] = internal microscope image number (024, 040 or 051). [magnification] = nominal microscope magnification (52kx or 26kx). Examples: a_g74_024_52kx.tif; b_g74_040_52kx.tif; c_g74_051_26kx.tif TEM processed-image suffixes *_u.tif = intermediate ImageJ-derived TIFF *_uu.png = 800×800 px harmonized field-of-view PNG used as input by 02_myimg-lm.py *_uuls.png = labeled and scale-barred 800×800 px PNG generated by 02_myimg-lm.py *03_myimg_mrep.py.png = final 2460×820 px 1×3 montage generated by 03_myimg_mrep.py and corresponding to Fig. 8. TEM support/code files 000_ReadMe_TEM.txt = brief processing workflow. 01_same-rwi.xlsx = calculation sheet for selecting crop sizes with the same real-world image field of view. 02_myimg-lm.py = Python script adding panel labels and scale bars to *_uu.png inputs using myimg.api. 03_myimg_mrep.py = Python script assembling *_uuls.png inputs into the final 1×3 montage using myimg.api. --------------------------------------------------------------------FILE TYPES & FORMATS, SW TO OPEN AND DIMENSIONS & UNITS--------------------------------------------------------------------* DLS correlation data Converted format: CSV Decimal operator: . Delimiter: , Dimensions & units in row 1 Columns 1–9: Type; Sample Name; Measurement Date and Time; Duration (s); Temperature (°C); Viscosity (cP); Derived Count Rate (kcps); Scattering Angle (°); Dispersant RI (dimensionless) Columns 10–201: Correlation Delay Times[1–192] (µs) Columns 202–393: Correlation Data[1–192] (dimensionless/instrument-normalized correlation values) SW: - generated by Malvern Zetasizer software - can be opened by any text/tabular editor * SLS Converted format: CSV Decimal operator: . Delimiter: , Dimensions & units in row 1 SW: - can be opened by any text/tabular editor * SAXS reduced curves Converted format: CSV Decimal operator: , Delimiter: ; Dimensions & units in row 1 q plus I(q) at 15, 23, 29, 35, 39.5 and 45 °C; 393 q points in rows 4–396 q unit: Å⁻¹, inferred/confirmed from the article; I(q) unit should be treated as arbitrary or instrument-normalized units SW: - can be opened by any text/tabular editor * TEM source and intermediate TIFF images original source files: a_g74_024_52kx.tif, b_g74_040_52kx.tif, c_g74_051_26kx.tif; 1024×1024 px intermediate *_u.tif files: 1024×1024 px, 16-bit grayscale physical intensity units: none; spatial scale is represented through microscope calibration/real-world image scaling and the added scale bar software to open/process: ImageJ/Fiji or other TIFF-capable scientific image software TEM PNG images *_uu.png: 800×800 px grayscale processing inputs *_uuls.png: 800×800 px grayscale images with panel label and 500 nm scale bar 03_myimg_mrep.py.png: 2460×820 px final grayscale montage software to open: any PNG-capable image viewer; processing scripts require Python and myimg.api TEM real-world-image calculation workbook format: .XLSX, 3 worksheets; only “List1” is populated (A1:I14) content: crop/RWI calculation. The sheet records 1024 px source images with nominal real-world lengths 2163 and 4326 [unit], pixel sizes 2.1123 and 4.2246 [unit/px], and equivalent crop widths/heights of 800 and 400 px, both corresponding to 1689.84375 [unit]. The intended physical unit is nm. software to open: Microsoft Excel, LibreOffice Calc or equivalent Python processing code format: .PY plain-text Python source files: 02_myimg-lm.py and 03_myimg_mrep.py dependencies: Python 3, glob (standard library), and custom package myimg.api; the myimg package/version is not included in the supplied dataset output: labeled/scale-barred PNGs and final montage TEM processing notes format: .TXT, plain ASCII text, English language file: TEM/00_readme.txt content: four-step image-processing workflow (RWI calculation, ImageJ crop/rescale, Python labeling/scale bar, Python montage)