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<article xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:oasis="http://docs.oasis-open.org/ns/oasis-exchange/table" xml:lang="en" dtd-version="3.0" article-type="methods-article">
  <front>
    <journal-meta><journal-id journal-id-type="publisher">BG</journal-id><journal-title-group>
    <journal-title>Biogeosciences</journal-title>
    <abbrev-journal-title abbrev-type="publisher">BG</abbrev-journal-title><abbrev-journal-title abbrev-type="nlm-ta">Biogeosciences</abbrev-journal-title>
  </journal-title-group><issn pub-type="epub">1726-4189</issn><publisher>
    <publisher-name>Copernicus Publications</publisher-name>
    <publisher-loc>Göttingen, Germany</publisher-loc>
  </publisher></journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">10.5194/bg-23-4859-2026</article-id><title-group><article-title>Technical note: Kinetically resolved volatile and redox fingerprints of geologic materials by ramped combustion microchromatography</article-title><alt-title>Kinetically resolved volatile and redox fingerprints</alt-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author" corresp="yes" rid="aff1 aff2">
          <name><surname>Wu</surname><given-names>Shuzhuang</given-names></name>
          <email>shuzhuangwu@gmail.com</email>
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1">
          <name><surname>Jaccard</surname><given-names>Samuel L.</given-names></name>
          
        <ext-link>https://orcid.org/0000-0002-5793-0896</ext-link></contrib>
        <contrib contrib-type="author" corresp="yes" rid="aff1">
          <name><surname>Galvez</surname><given-names>Matthieu E.</given-names></name>
          <email>matthieu.galvez@unil.ch</email>
        </contrib>
        <aff id="aff1"><label>1</label><institution>Institute of Earth Sciences, University of Lausanne, Lausanne, Switzerland</institution>
        </aff>
        <aff id="aff2"><label>2</label><institution>State Key Laboratory of Deep-sea Science and Intelligence Technology, Institute of Deep-sea Science and Engineering, Chinese Academy of Sciences, Sanya, China</institution>
        </aff>
      </contrib-group>
      <author-notes><corresp id="corr1">Shuzhuang Wu (shuzhuangwu@gmail.com) and Matthieu E. Galvez (matthieu.galvez@unil.ch)</corresp></author-notes><pub-date><day>14</day><month>July</month><year>2026</year></pub-date>
      
      <volume>23</volume>
      <issue>13</issue>
      <fpage>4859</fpage><lpage>4872</lpage>
      <history>
        <date date-type="received"><day>23</day><month>January</month><year>2026</year></date>
           <date date-type="rev-request"><day>4</day><month>February</month><year>2026</year></date>
           <date date-type="rev-recd"><day>15</day><month>May</month><year>2026</year></date>
           <date date-type="accepted"><day>18</day><month>May</month><year>2026</year></date>
      </history>
      <permissions>
        <copyright-statement>Copyright: © 2026 Shuzhuang Wu et al.</copyright-statement>
        <copyright-year>2026</copyright-year>
      <license license-type="open-access"><license-p>This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit <ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</ext-link></license-p></license></permissions><self-uri xlink:href="https://bg.copernicus.org/articles/23/4859/2026/bg-23-4859-2026.html">This article is available from https://bg.copernicus.org/articles/23/4859/2026/bg-23-4859-2026.html</self-uri><self-uri xlink:href="https://bg.copernicus.org/articles/23/4859/2026/bg-23-4859-2026.pdf">The full text article is available as a PDF file from https://bg.copernicus.org/articles/23/4859/2026/bg-23-4859-2026.pdf</self-uri>
      <abstract><title>Abstract</title>

      <p id="d2e111">The biogeochemical cycles of carbon, oxygen and sulfur are fundamentally interlinked, yet quantifying the reactivity of these elements within complex geological matrices remains a major analytical challenge. We present a novel integrated TGA/DSC-MicroGC system that simultaneously monitors mass loss, heat flow, and evolved gas composition during controlled heating in a gas mixing furnace. This approach kinetically resolves and quantifies distinct carbon and sulfur materials through their thermal decomposition profiles. Furthermore, continuous monitoring of oxygen consumption provides a direct measure of a material's oxidability in various temperature windows, a redox fingerprint. Validation against geochemical standards and application to sediments from the Congo Basin and Alpine Lake Cadagno (Switzerland) reveal diagenetic transitions and paleoenvironmental fluxes that are invisible to conventional bulk methods. This integrated methodology provides a mechanistic, high-resolution insight into electron-transfer processes in natural materials. This offers new avenues for probing biogeochemical cycling, redox evolution and environmental reactivity across Earth systems.</p>
  </abstract>
    
<funding-group>
<award-group id="gs1">
<funding-source>Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung</funding-source>
<award-id>215097</award-id>
<award-id>200021_163003</award-id>
<award-id>200020_192361</award-id>
</award-group>
<award-group id="gs2">
<funding-source>Chinese Academy of Sciences</funding-source>
<award-id>E5710403</award-id>
</award-group>
</funding-group>
</article-meta>
  </front>
<body>
      

<sec id="Ch1.S1" sec-type="intro">
  <label>1</label><title>Introduction</title>
      <p id="d2e123">The fate of carbon (C) and sulfur (S) in sediments depends not only on their bulk concentration, but also on their molecular speciation and intrinsic reactivity (Alt and Shanks III, 2006; Galvez, 2020; Hayes and Waldbauer, 2006; Paytan et al., 1998). For example, the oxidation of labile biomolecules occurs over temperature windows distinct from that of refractory C phases, such as graphite. Similarly, monosulfides display oxidation profiles that differ from those of crystalline sulfides such as pyrite (Boudreau, 1992; Hemingway et al., 2018; Ordoñez et al., 2019; Sebag et al., 2016). Their combustion enthalpy and O<sub>2</sub> demand differ too, reflecting the specific bonding environment and redox state of each compound or mineral phase (Galvez and Jaccard, 2021). Existing analytical techniques are poorly suited to capture these key dimensions of reactivity. Standard methods, such as elemental analysis or X-ray fluorescence, quantify total C and S, but are blind to molecular speciation and oxidation behavior (Berg et al., 2022; Carter et al., 2024; Salonen, 1979; Yoon et al., 2018; Zhao et al., 2020). Meanwhile, Rock-Eval, a widely used tool in organic petrology, provides bulk chemical and kinetic parameters for hydrocarbon source rocks (Espitalie et al., 1985). However, its focus is primarily on the ramped pyrolysis and oxidation of carbon-bearing materials, and only the latest generation, Rock-Eval 7, allows sulfur to be characterized simultaneously (Cohen-Sadon et al., 2022). Moreover, Rock-Eval does not directly link thermal degradation and combustion profiles to their underlying oxygen demands, which is a key parameter for quantifying the redox state and oxidation potential of rocks and sediments.</p>
      <p id="d2e135">Recent advances, such as the high-temperature titration method of Galvez and Jaccard (2021), have successfully quantified the total redox capacity of natural samples, partially bridging the gap between compositional and redox characterization. But this protocol was designed as an endpoint measurement and, as such, does not kinetically resolve the oxidation process, which we define here as the “oxidability” of the sample: the temperature-dependent distribution of oxygen consumption, mass loss heat release and gas production during controlled heating In addition, the endpoint approach does not provide direct insight into the individual contributions of C, S, and redox-active iron (Fe) species – including iron locked in mineral lattices (e.g., Fe(II) in silicates, oxides, and sulfides such as pyrite), as well as iron complexed within or adsorbed onto organic biomass – to the overall oxygen demand (Galvez and Jaccard, 2021; Galvez et al., 2025). Consequently, a major disconnect persists between our understanding of global redox cycles and our ability to characterize the combustion kinetics of the materials that drive them. To address this gap, an analytical approach is required that goes beyond bulk characterization, providing a simultaneous, kinetically resolved view of mass loss, heat flow, oxygen consumption and gas evolution during ramped pyrolysis or combustion.</p>
      <p id="d2e138">We have developed such a platform: a novel integrated system combining thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), and micro gas chromatography (MicroGC). This configuration enables real-time, correlated measurements of mass loss, heat flow, and the evolving composition of gases, including O<sub>2</sub>, CO<sub>2</sub>, SO<sub>2</sub>, H<sub>2</sub>S, COS, throughout controlled thermal decomposition and oxidation. Specifically, this integration enables two key innovations: <list list-type="order"><list-item>
      <p id="d2e179">It distinguishes thermally reactive C and S pools based on their characteristic decomposition or oxidation profiles and activation energies.</p></list-item><list-item>
      <p id="d2e183">It measures oxygen demand during combustion in a continuous, kinetically resolved set-up by directly monitoring oxygen consumption, yielding a unique chemical, thermal and redox fingerprint for each sample.</p></list-item></list></p>
      <p id="d2e186">Together, these capabilities reveal a new means of characterizing redox reactivity. By linking mass loss, heat flow, gas evolution and oxygen demand, this approach can reveal diagenetic pathways and paleoenvironmental signatures in complex natural archives that remain inaccessible to conventional bulk analytical techniques.</p>
</sec>
<sec id="Ch1.S2">
  <label>2</label><title>Materials and Methods</title>
<sec id="Ch1.S2.SS1">
  <label>2.1</label><title>Sample selection and preparation</title>
      <p id="d2e204">Sediment samples for TGA/DSC-MicroGC were obtained from archived freeze-dried material. We have analyzed a range of representative sediment materials containing C, S and hydrogen (H)-bearing compounds from contrasting depositional environments: the Congo Basin, Democratic Republic of Congo (DRC) and Lake Cadagno, Switzerland. The Congo Basin hosts the world's largest tropical peatland complex (Crezee et al., 2022; Garcin et al., 2022). These sediments provide a natural archive for studying organic matter degradation pathways associated with peat burial and climate-driven shifts since the Last Glacial Maximum. In contrast, Lake Cadagno, a meromictic lake in Switzerland with a permanently anoxic deep layer, is an ideal natural laboratory for studying microbial redox processes and their long-term preservation in the sedimentary record (Berg et al., 2022; Berg et al., 2025; Janssen et al., 2022; Dupeyron et al., 2025). Our approach illuminates the evolving speciation and reactivity of C-and S-bearing phases across distinct sedimentary redox environments.</p>
</sec>
<sec id="Ch1.S2.SS2">
  <label>2.2</label><title>Instrumentation and ultra-fast configuration mode</title>
      <p id="d2e215">We coupled a TGA/DSC 3<inline-formula><mml:math id="M6" display="inline"><mml:mo>+</mml:mo></mml:math></inline-formula> (Mettler Toledo) with a MicroGC 990 (Agilent SRA Instruments) (Fig. 1) and calibrated the integrated system for precise, semi-continuous H-, C-, O- and S-bearing gases (Fig. 3). The TGA/DSC simultaneously monitors mass and heat flow changes as a function of temperature within a programmable continuous-flow gas-mixing furnace. The MicroGC, a gas chromatograph equipped with short columns, separates, identifies, and quantifies light volatile compounds, including H<sub>2</sub>O, O<sub>2</sub>, CH<sub>4</sub>, CO<sub>2</sub>, H<sub>2</sub>S, COS and SO<sub>2</sub>, present in the evolving gas mixture. In our hybrid setup, samples are heated in a programmable furnace at 10 °C min<sup>−1</sup> from 100–1000 °C under a controlled atmosphere (typically 10 mL min<sup>−1</sup> N<sub>2</sub> and 1 mL min<sup>−1</sup> O<sub>2</sub>). A secondary oxidation furnace downstream of the primary furnace, maintained at temperatures between 300–800 °C was used to promote complete oxidation of evolved volatile species across the heating ramp. All carrier gases (N<sub>2</sub>, O<sub>2</sub> and He) are of ultra-high purity (<inline-formula><mml:math id="M20" display="inline"><mml:mrow><mml:mo>&gt;</mml:mo><mml:mn mathvariant="normal">5.8</mml:mn></mml:mrow></mml:math></inline-formula>; Carbagas and Linde).</p>
      <p id="d2e363">Evolved gases are transferred via a heated transfer line (80 °C) to the MicroGC, equipped with Molsieve 5 Å (for N<sub>2</sub>, O<sub>2</sub>) and PORAPLOT PPU (for CO<sub>2</sub>, H<sub>2</sub>S and SO<sub>2</sub>) columns for efficient separation and quantification. The integrated TGA/DSC-MicroGC analytical system therefore provides simultaneous, temperature-resolved measurements of mass loss, heat flow, and gas composition enabling real-time characterization of oxidation and redox processes.</p>

      <fig id="F1" specific-use="star"><label>Figure 1</label><caption><p id="d2e413">TGA/DSC-MicroGC instrument schematic.</p></caption>
          <graphic xlink:href="https://bg.copernicus.org/articles/23/4859/2026/bg-23-4859-2026-f01.png"/>

        </fig>

</sec>
<sec id="Ch1.S2.SS3">
  <label>2.3</label><title>Ultra-fast chromatographic mode</title>
      <p id="d2e430">To resolve discrete devolatilization events, we configured our Agilent Micro GC990 Solia system for ultra-fast dual-channel operation with a total cycle time of 54 s, comprising 15 s of sampling time and 39 s of chromatographic separation. The objective was not to maximize separation of light or heavy hydrocarbons, as is typically prioritized in chromatographic cycles lasting more than two minutes, but rather to maximize sampling density and capture transient devolatilization profiles of light molecules at high temporal resolution. The following ultra-fast MicroGC configuration prioritized cycle speed while maintaining robust peak separation for the target gases.</p>
      <p id="d2e433">Channel A employed an MS5A SS column (10 m <inline-formula><mml:math id="M26" display="inline"><mml:mrow><mml:mo>×</mml:mo><mml:mn mathvariant="normal">0.25</mml:mn></mml:mrow></mml:math></inline-formula> mm) held at 100 °C and 2.30 bar He pressure, with the injector heated at 90 °C. A 15 ms injection time was used for sharp sample introduction and backflush was initiated after 13 s of chromatography to purge retained species. This setup optimized rapid separation and detection of permanent gases (O<sub>2</sub>, N<sub>2</sub>, CO) using a thermal conductivity detector (TCD). Channel B used a PORAPLOT U FS column (10 m <inline-formula><mml:math id="M29" display="inline"><mml:mrow><mml:mo>×</mml:mo><mml:mn mathvariant="normal">0.32</mml:mn></mml:mrow></mml:math></inline-formula> mm) maintained at 98 °C and 2.30 bar column pressure, with the injector heated at 90 °C. A 50 ms injection to ensure adequate signal to noise ratio for volatile species at low concentrations, and backflush was initiated after 10 s. This configuration enabled fast elution and quantification of CO<sub>2</sub>, H<sub>2</sub>S, H<sub>2</sub>O, and SO<sub>2</sub>.</p>
      <p id="d2e511">This configuration, refined empirically through repeated optimization, delivers a complete gas composition datapoint every 54 s. A typical 90 min experiment therefore generates approximately 100 high-density gas-evolution spectra, sufficient to capture transient volatile release profiles with high temporal resolution.</p>
</sec>
<sec id="Ch1.S2.SS4">
  <label>2.4</label><title>Data processing and kinetic parameter estimation</title>
      <p id="d2e522">Raw thermogravimetric and calorimetric data were initially processed using Mettler Toledo STARe software. Prior to kinetic fitting, baseline drift and buoyancy effects were corrected by subtracting blank runs performed under identical analytical conditions. We quantified evolved gas concentrations by external calibration against known standards and processed the MicroGC chromatograms with the Solia software (Fig. 3).</p>
      <p id="d2e525">Bulk mass-loss measurement alone does not capture the intrinsic reactivity of complex materials. To overcome this limitation, our approach uses pyrograms, heat-flow data and gas-evolution profiles to characterize the reaction kinetics of C and S devolatilization and/or combustion pathways based on Arrhenius-type behavior (Eq. 1).

            <disp-formula id="Ch1.E1" content-type="numbered"><label>1</label><mml:math id="M34" display="block"><mml:mrow><mml:mi>k</mml:mi><mml:mo>=</mml:mo><mml:mi>A</mml:mi><mml:mi>exp⁡</mml:mi><mml:mfenced open="(" close=")"><mml:mrow><mml:mo>-</mml:mo><mml:mstyle displaystyle="true"><mml:mfrac style="display"><mml:mrow><mml:mi>E</mml:mi><mml:mi mathvariant="normal">a</mml:mi></mml:mrow><mml:mrow><mml:mi>R</mml:mi><mml:mi>T</mml:mi></mml:mrow></mml:mfrac></mml:mstyle></mml:mrow></mml:mfenced></mml:mrow></mml:math></disp-formula>

          where <inline-formula><mml:math id="M35" display="inline"><mml:mi>k</mml:mi></mml:math></inline-formula> is the temperature-dependent rate constant, <inline-formula><mml:math id="M36" display="inline"><mml:mi>A</mml:mi></mml:math></inline-formula> is the empirically derived Arrhenius pre-exponential (“frequency”) factor, <inline-formula><mml:math id="M37" display="inline"><mml:mrow><mml:msub><mml:mi>E</mml:mi><mml:mi mathvariant="normal">a</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> is the activation energy, <inline-formula><mml:math id="M38" display="inline"><mml:mi>R</mml:mi></mml:math></inline-formula> is the ideal gas constant, and <inline-formula><mml:math id="M39" display="inline"><mml:mi>T</mml:mi></mml:math></inline-formula> is the measured temperature (see Table 1 for symbol descriptions).</p>

<table-wrap id="T1" specific-use="star"><label>Table 1</label><caption><p id="d2e601">List of mathematical symbols used throughout this study.</p></caption><oasis:table frame="topbot"><oasis:tgroup cols="3">
     <oasis:colspec colnum="1" colname="col1" align="justify" colwidth="1cm"/>
     <oasis:colspec colnum="2" colname="col2" align="justify" colwidth="6cm"/>
     <oasis:colspec colnum="3" colname="col3" align="justify" colwidth="2cm"/>
     <oasis:thead>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1">Symbol</oasis:entry>
         <oasis:entry colname="col2">Parameter</oasis:entry>
         <oasis:entry colname="col3">Units</oasis:entry>
       </oasis:row>
     </oasis:thead>
     <oasis:tbody>
       <oasis:row>
         <oasis:entry colname="col1"><inline-formula><mml:math id="M40" display="inline"><mml:mi>A</mml:mi></mml:math></inline-formula></oasis:entry>
         <oasis:entry colname="col2">Arrhenius pre-exponential (“frequency”) factor</oasis:entry>
         <oasis:entry colname="col3">s<sup>−1</sup></oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">aE<sub>a</sub></oasis:entry>
         <oasis:entry colname="col2">Apparent activation energy for the reaction</oasis:entry>
         <oasis:entry colname="col3">kJ mol<sup>−1</sup></oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1"><inline-formula><mml:math id="M44" display="inline"><mml:mrow><mml:mi mathvariant="normal">Δ</mml:mi><mml:mi>H</mml:mi></mml:mrow></mml:math></inline-formula></oasis:entry>
         <oasis:entry colname="col2">Enthalpy</oasis:entry>
         <oasis:entry colname="col3">kJ mol<sup>−1</sup></oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1"><inline-formula><mml:math id="M46" display="inline"><mml:mi mathvariant="italic">β</mml:mi></mml:math></inline-formula></oasis:entry>
         <oasis:entry colname="col2">Temperature heating rate</oasis:entry>
         <oasis:entry colname="col3">°C min<sup>−1</sup></oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1"><inline-formula><mml:math id="M48" display="inline"><mml:mi>T</mml:mi></mml:math></inline-formula></oasis:entry>
         <oasis:entry colname="col2">Temperature</oasis:entry>
         <oasis:entry colname="col3">K</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1"><inline-formula><mml:math id="M49" display="inline"><mml:mi>R</mml:mi></mml:math></inline-formula></oasis:entry>
         <oasis:entry colname="col2">Ideal gas constant</oasis:entry>
         <oasis:entry colname="col3">kJ mol<sup>−1</sup> K<sup>−1</sup></oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1"><inline-formula><mml:math id="M52" display="inline"><mml:mi>k</mml:mi></mml:math></inline-formula></oasis:entry>
         <oasis:entry colname="col2">Temperature-dependent rate constant</oasis:entry>
         <oasis:entry colname="col3">–</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1"><inline-formula><mml:math id="M53" display="inline"><mml:mi>p</mml:mi></mml:math></inline-formula>(0,E)</oasis:entry>
         <oasis:entry colname="col2">Continuous aE<sub>a</sub> of fraction of initial mass</oasis:entry>
         <oasis:entry colname="col3">–</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1"><inline-formula><mml:math id="M55" display="inline"><mml:mi mathvariant="italic">λ</mml:mi></mml:math></inline-formula></oasis:entry>
         <oasis:entry colname="col2">Regularization weighting factor</oasis:entry>
         <oasis:entry colname="col3">–</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1"><inline-formula><mml:math id="M56" display="inline"><mml:mi>d</mml:mi></mml:math></inline-formula>O<sub>2</sub></oasis:entry>
         <oasis:entry colname="col2">Redox capacity</oasis:entry>
         <oasis:entry colname="col3">umol g<sup>−1</sup></oasis:entry>
       </oasis:row>
     </oasis:tbody>
   </oasis:tgroup></oasis:table></table-wrap>

      <p id="d2e908">To extract kinetic parameters, we employed a dual-model approach: <list list-type="order"><list-item>
      <p id="d2e913">Model-Free Isoconversional Method: First, apparent activation energies (aE<sub>a</sub>) were determined using the model-free isoconversional method of Vyazovkin (Vyazovkin and Wight, 1999), implemented within the kinetics evaluation module of the STARe software. This reaction was evaluated the across discrete fractional conversion intervals (<inline-formula><mml:math id="M60" display="inline"><mml:mi mathvariant="italic">α</mml:mi></mml:math></inline-formula>). This approach does not require prior assumptions about the reaction model and served as our baseline for parameter validation.</p></list-item><list-item>
      <p id="d2e933">Distributed Activation Energy Model (DAEM): Second, we compared the isoconversional results with those obtained using a parallel-reaction Distributed Activation Energy Model (DAEM), following the methodology outlined by Hemingway et al. (2017). A critical step in our parameterization was the treatment of the pre-exponential factor (<inline-formula><mml:math id="M61" display="inline"><mml:mi>A</mml:mi></mml:math></inline-formula>). Use of a fixed <inline-formula><mml:math id="M62" display="inline"><mml:mi>A</mml:mi></mml:math></inline-formula> value led to systematic overestimation of activation energies for certain standards such as calcite that do not follow a first-order decomposition kinetics (Fig. 4). Consequently, <inline-formula><mml:math id="M63" display="inline"><mml:mi>A</mml:mi></mml:math></inline-formula> was iteratively optimized across a boundary range of 10<sup>5</sup> to 10<sup>12</sup> s<sup>−1</sup> to ensure consistency between DAEM-derived apparent activation energies to those obtained using the isoconversional method. This optimized protocol was subsequently applied to all natural samples.</p></list-item></list></p>
</sec>
</sec>
<sec id="Ch1.S3">
  <label>3</label><title>Results</title>
<sec id="Ch1.S3.SS1">
  <label>3.1</label><title>Method validation: thermal signatures and calibration</title>
      <p id="d2e1004">The integrated analytical system produces distinct thermal decomposition and combustion signatures for a range of C- and S-bearing standards, allowing robust characterization of their thermal reactivity and associated gas-evolution patterns (Fig. 2). Organic standards, such as L-cystine and cellulose, exhibited multi-stage decomposition profiles (Fig. 2A, B). L-cystine, a sulfur-rich amino acid, decomposed primarily between 200 and 300 °C under N<sub>2</sub> / O<sub>2</sub> (10 : 1 vol : vol) mix atmosphere, releasing CO<sub>2</sub>, H<sub>2</sub>S, and COS (Fig. 2A, H). Cellulose combusted between 250 and 350 °C, releasing primarily CO<sub>2</sub> and H<sub>2</sub>O (Fig. 2B, I). The IFPEN-160 000 (IFP) standard displays overlapping mass-loss events, reflecting the concurrent oxidation of organics and devolatilization of carbonates (a non-redox decarbonation reaction) (Fig. 2C, J). In contrast, inorganic standards yielded sharper and more distinct thermal spectra. Calcium carbonate (CaCO<sub>3</sub>), for instance, exhibited a single, well-defined mass loss of <inline-formula><mml:math id="M74" display="inline"><mml:mrow><mml:mo>∼</mml:mo><mml:mn mathvariant="normal">44</mml:mn></mml:mrow></mml:math></inline-formula> % between 600 and 800 °C, consistent with its thermal decomposition into CaO and CO<sub>2</sub> (Fig. 2D, K).</p>

      <fig id="F2" specific-use="star"><label>Figure 2</label><caption><p id="d2e1092">Mass loss and devolatilized components from standards evaluated by using TGA/DSC-MicroGC system. <bold>(A, H)</bold> L-Cystine; <bold>(B, I)</bold> Cellulose; <bold>(C, J)</bold> IFP; <bold>(D, K)</bold> CaCO<sub>3</sub>; <bold>(E, L)</bold> Elemental sulfur; <bold>(F, M)</bold> Pyrite; <bold>(G, N)</bold> Al(OH<inline-formula><mml:math id="M77" display="inline"><mml:mrow><mml:msub><mml:mo>)</mml:mo><mml:mn mathvariant="normal">3</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula>.</p></caption>
          <graphic xlink:href="https://bg.copernicus.org/articles/23/4859/2026/bg-23-4859-2026-f02.png"/>

        </fig>

      <p id="d2e1143">Elemental S underwent rapid volatilization followed by partial oxidation to SO<sub>2</sub> between 100 and 300 °C under oxidative condition (Fig. 2E, L). In contrast, pure pyrite (FeS2) showed a distinct oxidative mass loss between 400 and 600 °C, corresponding to its conversion to Fe2O3 and release of SO<sub>2</sub> (Fig. 2F, M). Aluminium hydroxide [Al(OH)<sub>3</sub>] released structural water (<inline-formula><mml:math id="M81" display="inline"><mml:mrow><mml:mo>∼</mml:mo><mml:mn mathvariant="normal">13</mml:mn></mml:mrow></mml:math></inline-formula> % mass loss) between 200 and 300 °C as it dehydrated to Al<sub>2</sub>O<sub>3</sub> (Fig. 2G, N). The combined TGA/DSC-MicroGC profiles thus differentiate organic from inorganic C- and S-bearing species based on their unique thermal decomposition kinetics or oxidation behaviour and diagnostic gas-phase reaction products. This kinetically resolved information is not accessible with conventional bulk characterization techniques.</p>
      <p id="d2e1203">We operated the hybrid system in oxidation mode, enabling accurate quantitative determinations of H-, C- and S-bearing volatile production and thereby allowing estimation of the corresponding reactive H, C and S contents. Under these conditions, reactive H-, C-, S-bearing species are completely oxidized to H<sub>2</sub>O, CO<sub>2</sub> and SO<sub>2</sub>, respectively, allowing direct quantification from gas-evolution signals. H<sub>2</sub>O was calibrated by dehydrating Al(OH)<sub>3</sub> under controlled conditions (Fig. 3A). CO<sub>2</sub> calibration was performed using multiple certified reference materials to ensure accuracy and linearity across diverse matrices and CO<sub>2</sub> generation mechanisms. Synthetic vitreous carbon, natural graphite, and the IFP standard were combusted in oxidation mode; pure CaCO<sub>3</sub> was thermally decomposed to release structural carbonate CO<sub>2</sub>; and ambient air (427 ppm CO<sub>2</sub>) served as an independent gas-phase standard (Fig. 3B). Sulfur species were calibrated using the oxidation of pyrite, IFP, CLB_1 and JSD_1 standards, yielding well defined SO<sub>2</sub> peaks (Fig. 3C). The resulting calibration curves for H<sub>2</sub>O, CO<sub>2</sub> and SO<sub>2</sub> exhibit excellent linearity (<inline-formula><mml:math id="M98" display="inline"><mml:mrow><mml:msup><mml:mi>R</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msup><mml:mo>&gt;</mml:mo><mml:mn mathvariant="normal">0.99</mml:mn></mml:mrow></mml:math></inline-formula>), confirming the robustness and precision of quantitative measurements in oxidation mode (Fig. 3).</p>

      <fig id="F3" specific-use="star"><label>Figure 3</label><caption><p id="d2e1351">Gas concentration calibrations. <bold>(A)</bold>, Hydrogen; <bold>(B)</bold>, Carbon; <bold>(C)</bold>, Sulfur. Note: The different mass ranges for H, C and S calibration due to our calibrations focus on the typical ranges of H, C, S concentrations in the natural samples. S is typically low, while H and C can reach much higher concentrations in rocks and sediments, so the calibration reflects this wider range (Table S2 in the Supplement). The different slopes are reflecting different behaviors of the elements and response of the sensor.</p></caption>
          <graphic xlink:href="https://bg.copernicus.org/articles/23/4859/2026/bg-23-4859-2026-f03.png"/>

        </fig>

</sec>
<sec id="Ch1.S3.SS2">
  <label>3.2</label><title>Kinetically-resolved characterization of carbon and sulfur in natural samples</title>
      <p id="d2e1377">To test the kinetic approach, we first analyzed CaCO<sub>3</sub> (99.9 % purity) using the model-free isoconversional method (Fig. 4A–D). The method yielded a reaction enthalpy (<inline-formula><mml:math id="M100" display="inline"><mml:mi mathvariant="normal">Δ</mml:mi></mml:math></inline-formula>H) of 177 kJ mol<sup>−1</sup> and aE<sub>a</sub> of 164 kJ mol<sup>−1</sup>, both consistent with published values (L'vov, 1997). In contrast, the DAEM with a fixed pre-exponential factor (<inline-formula><mml:math id="M104" display="inline"><mml:mrow><mml:mi>A</mml:mi><mml:mo>=</mml:mo><mml:msup><mml:mn mathvariant="normal">10</mml:mn><mml:mn mathvariant="normal">10</mml:mn></mml:msup></mml:mrow></mml:math></inline-formula> s<sup>−1</sup>) produced an overestimated aE<sub>a</sub> of <inline-formula><mml:math id="M107" display="inline"><mml:mrow><mml:mo>∼</mml:mo><mml:mn mathvariant="normal">250</mml:mn></mml:mrow></mml:math></inline-formula> kJ mol<sup>−1</sup>. Adjusting <inline-formula><mml:math id="M109" display="inline"><mml:mi>A</mml:mi></mml:math></inline-formula> to 10<sup>5.5</sup> s<sup>−1</sup> resulted in a aE<sub>a</sub> of 162 kJ mol<sup>−1</sup>, closely matching the isoconversional estimate and literature values (Fig. 4D). Applying this approach to a natural sediment from the Congo Basin (Fig. 4E–H) revealed two distinct modes with aE<inline-formula><mml:math id="M114" display="inline"><mml:mrow><mml:msub><mml:mi/><mml:mi mathvariant="normal">a</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:mn mathvariant="normal">160</mml:mn></mml:mrow></mml:math></inline-formula>  and 120 kJ mol<sup>−1</sup>, respectively. These modes are consistent with a multi-step decomposition process involving organic matter fraxtions with contrasting thermal reactivity. The DAEM with fixed <inline-formula><mml:math id="M116" display="inline"><mml:mrow><mml:mi>A</mml:mi><mml:mo>=</mml:mo><mml:msup><mml:mn mathvariant="normal">10</mml:mn><mml:mn mathvariant="normal">10</mml:mn></mml:msup></mml:mrow></mml:math></inline-formula> s<sup>−1</sup> again overestimated activation energies (<inline-formula><mml:math id="M118" display="inline"><mml:mrow><mml:mo>∼</mml:mo><mml:mn mathvariant="normal">180</mml:mn></mml:mrow></mml:math></inline-formula>  and 140 kJ mol<sup>−1</sup>), whereas an optimized prefactor of <inline-formula><mml:math id="M120" display="inline"><mml:mrow><mml:mi>A</mml:mi><mml:mo>=</mml:mo><mml:msup><mml:mn mathvariant="normal">10</mml:mn><mml:mn mathvariant="normal">8.5</mml:mn></mml:msup></mml:mrow></mml:math></inline-formula> s<sup>−1</sup> produced values comparable to the isoconversional results. These results demonstrate that the pre-exponential factor is not a universal constant across mineral and sedimentary matrices, but depends on sample structure, crystallinity, reaction pathway and the nature of the reacting phase. Careful parametrization is therefore required, particularly for natural samples containing mixed organic and mineral phases.</p>

      <fig id="F4" specific-use="star"><label>Figure 4</label><caption><p id="d2e1643">Thermal properties for standard calcium carbonate (left) and natural sample from Congo basin (right). <bold>(A, D)</bold> Mass loss curves from thermogravimetric analysis (TGA) at heating rates of 5, 10, and 20 °C min<sup>−1</sup>; <bold>(B, E)</bold> Apparent activation energy (aE<sub>a</sub>) as a function of conversion determined via the model-free isoconversional method (Vyazovkin and Wight, 1999); <bold>(C, F)</bold> We derive the apparent activation energy distribution <inline-formula><mml:math id="M124" display="inline"><mml:mi>p</mml:mi></mml:math></inline-formula>(0,<inline-formula><mml:math id="M125" display="inline"><mml:mi>E</mml:mi></mml:math></inline-formula>) from the distributed activation energy model (DAEM) for various pre-exponential factors (<inline-formula><mml:math id="M126" display="inline"><mml:mi>A</mml:mi></mml:math></inline-formula>) following (Hemingway et al., 2017).</p></caption>
          <graphic xlink:href="https://bg.copernicus.org/articles/23/4859/2026/bg-23-4859-2026-f04.png"/>

        </fig>

      <p id="d2e1704">Thermograms of peatland sediment from the Congo Basin reveal two predominant organic fractions: a thermally labile component (<inline-formula><mml:math id="M127" display="inline"><mml:mi>L</mml:mi></mml:math></inline-formula>) with a decomposition peak near 370 °C and an aE<sub>a</sub> of <inline-formula><mml:math id="M129" display="inline"><mml:mrow><mml:mo>∼</mml:mo><mml:mn mathvariant="normal">120</mml:mn></mml:mrow></mml:math></inline-formula> kJ mol<sup>−1</sup>, and a more refractory fraction (<inline-formula><mml:math id="M131" display="inline"><mml:mi>R</mml:mi></mml:math></inline-formula>), consistent with more aromatic, thermally stable organic matter, peaking at 500 °C with an aE<sub>a</sub> of <inline-formula><mml:math id="M133" display="inline"><mml:mrow><mml:mo>∼</mml:mo><mml:mn mathvariant="normal">160</mml:mn></mml:mrow></mml:math></inline-formula> kJ mol<sup>−1</sup> (Fig. 5A). The ratio of the labile-to-refractory peak areas (L <inline-formula><mml:math id="M135" display="inline"><mml:mo>/</mml:mo></mml:math></inline-formula> R) increases markedly following peat initiation, indicating a higher proportion of thermally labile organic material. This substantial increase suggests either a shift in organic matter sources and/or enhanced preservation of labile compounds within the peat profile. This interpretation would be consistent with independent paleoenvironmental proxies that point to a transition to wetter conditions that promoted peat accumulation and selective preservation of labile organic matter (Garcin et al., 2022).</p>
      <p id="d2e1792">Similarly, thermograms from Lake Cadagno sediments reveal three distinct S-bearing fractions. Hydrogen sulfide (H<sub>2</sub>S) and carbonyl sulfide (COS) evolved at relatively low temperatures, with aE<sub>a</sub> values of approximately 110  and 130 kJ mol<sup>−1</sup>, respectively. This behaviour is consistent with the decomposition of labile organic sulfur compounds, such as thiols, sulfides, and disulfides associated with fresh organic matter (Fig. 5B). A higher temperature SO<sub>2</sub> peak with an aE<sub>a</sub> value of approximately 150 kJ mol<sup>−1</sup> reflects the oxidative decomposition of pyrite. This differentiation clearly separates low-temperature organic S-bearing pools from more thermally stable inorganic sulfide minerals.</p>
      <p id="d2e1856">The low-temperature release of H<sub>2</sub>S and COS reflects partial sulfurization associated with dissimilatory sulfate reduction (DSR), the dominant anaerobic microbial pathway for S cycling in the lake's euxinic bottom waters. Conversely, the higher-temperature SO<sub>2</sub> peak reflects the thermal stability of pyrite, the end product of long-term biological sulphate reduction and diagenetic mineralization. Together, these results demonstrate that the TGA/DSC-MicroGC approach yields quantitative, kinetically resolved signatures of microbial sulfur cycling and organic matter transformation, linking molecular-scale reactivity to environmental processes.</p>

      <fig id="F5" specific-use="star"><label>Figure 5</label><caption><p id="d2e1879">Kinetically-resolved carbon and sulfur speciation. <bold>(A)</bold>, aE<sub>a</sub> distributions for carbon speciation in peat samples from the Congo Basin, including labile and refractory carbon fractions. <bold>(B)</bold>, aE<sub>a</sub> distributions of sulfur speciation in a sample from Lake Cadagno, including H<sub>2</sub>S, COS, and SO<sub>2</sub>.</p></caption>
          <graphic xlink:href="https://bg.copernicus.org/articles/23/4859/2026/bg-23-4859-2026-f05.png"/>

        </fig>

</sec>
<sec id="Ch1.S3.SS3">
  <label>3.3</label><title>Absolute Redox Capacity and Oxidation Profiles</title>
      <p id="d2e1939">Having established the capacity to kinetically resolve individual species, we next address a more integrated property: the specific redox capacity, <inline-formula><mml:math id="M148" display="inline"><mml:mi>d</mml:mi></mml:math></inline-formula>O<sub>2</sub>. To move beyond thermodynamic proxies and bulk speciation, we developed a method to quantify the oxygen demand during combustion, through time-resolved monitoring of oxygen consumption during a controlled heating ramp. Previous work has determined the redox capacity by measuring the total oxygen exchanged between a solid-state oxygen donor and the sample at elevated <inline-formula><mml:math id="M150" display="inline"><mml:mi>T</mml:mi></mml:math></inline-formula> under vacuum (Galvez and Jaccard, 2021; Galvez et al., 2020). But this approach is limited to endpoint measurements. Our method overcomes this limitation by continuously measuring O<sub>2</sub> concentrations at high temporal resolution during the heating ramp. Samples are heated at a controlled rate (e.g. 10 °C min<sup>−1</sup>), under a specific gas mixture, inducing progressive devolatilization and oxidation while the MicroGC continuously monitors the evolving gas composition. The decline in the O<sub>2</sub> concentrations is integrated over time to calculate the cumulative volume of O<sub>2</sub> consumed, which is then normalized to the initial sample mass and the evaluated relative to the stoichiometric oxidation reactions of the major gas-producing components (Fig. 6A). This approach yields an absolute oxygen demand while simultaneously preserving the temperature-resolved oxidation profile. It therefore provides both the total redox capacity and the distribution of that capacity across distinct thermal domains. The method shows high reproductivity with a relative standard deviation of 0.82 % for vitreous carbon standards. Importantly, our results are consistent with those obtained by the vacuum line protocol (Galvez and Jaccard, 2021), validating the precision and robustness of this continuous, kinetically resolved redox quantification method.</p>
      <p id="d2e2005">Redox capacity (<inline-formula><mml:math id="M155" display="inline"><mml:mi>d</mml:mi></mml:math></inline-formula>O<sub>2</sub>) was calculated as: <list list-type="custom"><list-item><label>i.</label>
      <p id="d2e2026">Volume of O<sub>2</sub> supply (<inline-formula><mml:math id="M158" display="inline"><mml:mrow><mml:msub><mml:mi>V</mml:mi><mml:mi mathvariant="normal">sup</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>)<disp-formula id="Ch1.E2" content-type="numbered"><label>2</label><mml:math id="M159" display="block"><mml:mrow><mml:msub><mml:mi>V</mml:mi><mml:mi mathvariant="normal">sup</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:mi>f</mml:mi><mml:mo>×</mml:mo><mml:mi mathvariant="normal">Δ</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:math></disp-formula>where <inline-formula><mml:math id="M160" display="inline"><mml:mi>f</mml:mi></mml:math></inline-formula> is the O<sub>2</sub> flow rate (1 ml min<sup>−1</sup>) and <inline-formula><mml:math id="M163" display="inline"><mml:mrow><mml:mi mathvariant="normal">Δ</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:math></inline-formula> is time interval.</p></list-item><list-item><label>ii.</label>
      <p id="d2e2110">Volume of O<sub>2</sub> consumed (<inline-formula><mml:math id="M165" display="inline"><mml:mrow><mml:msub><mml:mi>V</mml:mi><mml:mi mathvariant="normal">con</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>)<disp-formula id="Ch1.E3" content-type="numbered"><label>3</label><mml:math id="M166" display="block"><mml:mrow><mml:msub><mml:mi>V</mml:mi><mml:mi mathvariant="normal">con</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:mstyle displaystyle="true"><mml:mfrac style="display"><mml:mrow><mml:msubsup><mml:mo>∫</mml:mo><mml:mrow><mml:mi>t</mml:mi><mml:mn mathvariant="normal">1</mml:mn></mml:mrow><mml:mrow><mml:mi>t</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:mrow></mml:msubsup><mml:msub><mml:mi>A</mml:mi><mml:mi mathvariant="normal">eff</mml:mi></mml:msub><mml:mspace width="0.125em" linebreak="nobreak"/><mml:mi mathvariant="normal">d</mml:mi><mml:mi>t</mml:mi></mml:mrow><mml:mrow><mml:msubsup><mml:mo>∫</mml:mo><mml:mrow><mml:mi>t</mml:mi><mml:mn mathvariant="normal">1</mml:mn></mml:mrow><mml:mrow><mml:mi>t</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:mrow></mml:msubsup><mml:msub><mml:mi>A</mml:mi><mml:mi mathvariant="normal">tot</mml:mi></mml:msub><mml:mspace width="0.125em" linebreak="nobreak"/><mml:mi mathvariant="normal">d</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:mfrac></mml:mstyle><mml:mo>×</mml:mo><mml:msub><mml:mi>V</mml:mi><mml:mi mathvariant="normal">sup</mml:mi></mml:msub></mml:mrow></mml:math></disp-formula>where <inline-formula><mml:math id="M167" display="inline"><mml:mrow><mml:msub><mml:mi>A</mml:mi><mml:mi mathvariant="normal">eff</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> is time-integrated effective area corresponding to consumed O<sub>2</sub> and <inline-formula><mml:math id="M169" display="inline"><mml:mrow><mml:msub><mml:mi>A</mml:mi><mml:mi mathvariant="normal">tot</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> is time-integrated total area.</p></list-item><list-item><label>iii.</label>
      <p id="d2e2236">Redox capacity (<inline-formula><mml:math id="M170" display="inline"><mml:mi>d</mml:mi></mml:math></inline-formula>O<sub>2</sub>)<disp-formula id="Ch1.E4" content-type="numbered"><label>4</label><mml:math id="M172" display="block"><mml:mrow><mml:mi>d</mml:mi><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">O</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:mrow><mml:mo>=</mml:mo><mml:mstyle displaystyle="true"><mml:mfrac style="display"><mml:mrow><mml:mi>P</mml:mi><mml:msub><mml:mi>V</mml:mi><mml:mi mathvariant="normal">con</mml:mi></mml:msub></mml:mrow><mml:mrow><mml:mi>R</mml:mi><mml:mi>T</mml:mi><mml:mo>×</mml:mo><mml:mi>m</mml:mi></mml:mrow></mml:mfrac></mml:mstyle><mml:mo>,</mml:mo></mml:mrow></mml:math></disp-formula>where <inline-formula><mml:math id="M173" display="inline"><mml:mi>P</mml:mi></mml:math></inline-formula> is gas pressure, <inline-formula><mml:math id="M174" display="inline"><mml:mi>R</mml:mi></mml:math></inline-formula> is the ideal gas constant, <inline-formula><mml:math id="M175" display="inline"><mml:mi>T</mml:mi></mml:math></inline-formula> is the room temperature and <inline-formula><mml:math id="M176" display="inline"><mml:mi>m</mml:mi></mml:math></inline-formula> is sample mass.</p></list-item></list></p>

      <fig id="F6"><label>Figure 6</label><caption><p id="d2e2323">Oxygen consumption profiles of elemental sulfur, pyrite, vitreous carbon and graphite during controlled thermal oxidation. Replicate measurements for vitreous carbon and graphite yield high precision and reproductivity (Fig. S1).</p></caption>
          <graphic xlink:href="https://bg.copernicus.org/articles/23/4859/2026/bg-23-4859-2026-f06.png"/>

        </fig>

      <p id="d2e2333">The oxidation of various standards was examined to validate this new time-resolved <inline-formula><mml:math id="M177" display="inline"><mml:mi>d</mml:mi></mml:math></inline-formula>O<sub>2</sub> protocol. The measured <inline-formula><mml:math id="M179" display="inline"><mml:mi>d</mml:mi></mml:math></inline-formula>O<sub>2</sub> value for pyrite (<inline-formula><mml:math id="M181" display="inline"><mml:mi>d</mml:mi></mml:math></inline-formula>O<inline-formula><mml:math id="M182" display="inline"><mml:mrow><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub><mml:mo>=</mml:mo><mml:mn mathvariant="normal">2.03</mml:mn><mml:mo>×</mml:mo><mml:mn mathvariant="normal">104</mml:mn></mml:mrow></mml:math></inline-formula> <inline-formula><mml:math id="M183" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi><mml:mi mathvariant="normal">mol</mml:mi></mml:mrow></mml:math></inline-formula> g<sup>−1</sup>) is approximately 11 % lower than its theoretical values (<inline-formula><mml:math id="M185" display="inline"><mml:mrow><mml:mn mathvariant="normal">3.13</mml:mn><mml:mo>×</mml:mo><mml:mn mathvariant="normal">104</mml:mn></mml:mrow></mml:math></inline-formula> and <inline-formula><mml:math id="M186" display="inline"><mml:mrow><mml:mn mathvariant="normal">2.29</mml:mn><mml:mo>×</mml:mo><mml:mn mathvariant="normal">104</mml:mn></mml:mrow></mml:math></inline-formula> <inline-formula><mml:math id="M187" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi><mml:mi mathvariant="normal">mol</mml:mi></mml:mrow></mml:math></inline-formula> g<sup>−1</sup>). In contrast, elemental sulfur a <inline-formula><mml:math id="M189" display="inline"><mml:mi>d</mml:mi></mml:math></inline-formula>O<inline-formula><mml:math id="M190" display="inline"><mml:mrow><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub><mml:mo>=</mml:mo><mml:mn mathvariant="normal">1.31</mml:mn><mml:mo>×</mml:mo><mml:mn mathvariant="normal">104</mml:mn></mml:mrow></mml:math></inline-formula> <inline-formula><mml:math id="M191" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi><mml:mi mathvariant="normal">mol</mml:mi></mml:mrow></mml:math></inline-formula> g<sup>−1</sup> corresponding to only <inline-formula><mml:math id="M193" display="inline"><mml:mrow><mml:mo>∼</mml:mo><mml:mn mathvariant="normal">42</mml:mn></mml:mrow></mml:math></inline-formula> % of its theoretical value (<inline-formula><mml:math id="M194" display="inline"><mml:mrow><mml:mn mathvariant="normal">3.12</mml:mn><mml:mo>×</mml:mo><mml:mn mathvariant="normal">104</mml:mn></mml:mrow></mml:math></inline-formula> <inline-formula><mml:math id="M195" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi><mml:mi mathvariant="normal">mol</mml:mi></mml:mrow></mml:math></inline-formula> g<sup>−1</sup>). This substantial under-recovery indicated that low-temperature volatilization can remove reactive sulfur tom the system before complete oxidation. By contrast, vitreous carbon (<inline-formula><mml:math id="M197" display="inline"><mml:mi>d</mml:mi></mml:math></inline-formula>O<inline-formula><mml:math id="M198" display="inline"><mml:mrow><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub><mml:mo>=</mml:mo><mml:mn mathvariant="normal">8.38</mml:mn><mml:mo>×</mml:mo><mml:mn mathvariant="normal">104</mml:mn></mml:mrow></mml:math></inline-formula> <inline-formula><mml:math id="M199" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi><mml:mi mathvariant="normal">mol</mml:mi></mml:mrow></mml:math></inline-formula> g<sup>−1</sup>) and natural graphite (<inline-formula><mml:math id="M201" display="inline"><mml:mrow><mml:mn mathvariant="normal">8.36</mml:mn><mml:mo>×</mml:mo><mml:mn mathvariant="normal">104</mml:mn></mml:mrow></mml:math></inline-formula> <inline-formula><mml:math id="M202" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi><mml:mi mathvariant="normal">mol</mml:mi></mml:mrow></mml:math></inline-formula> g<sup>−1</sup>) display excellent alignment with the theoretical prediction (<inline-formula><mml:math id="M204" display="inline"><mml:mrow><mml:mn mathvariant="normal">8.36</mml:mn><mml:mo>×</mml:mo><mml:mn mathvariant="normal">104</mml:mn></mml:mrow></mml:math></inline-formula> <inline-formula><mml:math id="M205" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi><mml:mi mathvariant="normal">mol</mml:mi></mml:mrow></mml:math></inline-formula> g<sup>−1</sup>). Their oxidation occurs at higher temperatures (<inline-formula><mml:math id="M207" display="inline"><mml:mrow><mml:mo>&gt;</mml:mo><mml:mn mathvariant="normal">400</mml:mn></mml:mrow></mml:math></inline-formula>° C), where losses due to non-oxidative volatilization are minimal, confirming precision and accuracy of the method for refractory phases. By tracking O<sub>2</sub> consumption throughout controlled heating, the method captures the cumulative amount of reduced material that oxidized under imposed analytical conditions (Fig. 6). The integrated O<sub>2</sub> consumption across the full temperature range thus provides a quantitative measure of intrinsic redox capacity, while the temperature-resolved profile reveals hoe this capacity is distributed among reactive pools with distinct thermal behavior.</p>
</sec>
<sec id="Ch1.S3.SS4">
  <label>3.4</label><title>Secondary oven and optimization of complete oxidation</title>
      <p id="d2e2708">To address the incomplete oxidation of volatile S and light hydrocarbon species, we introduced a secondary oven downstream of the primary heating chamber (Fig. 1). The oven is held at a constant, adjustable temperature (300–800 °C), allowing volatile intermediates (e.g., S vapor, reduced hydrocarbons) released at <inline-formula><mml:math id="M210" display="inline"><mml:mrow><mml:mi>T</mml:mi><mml:mo>&lt;</mml:mo><mml:mo>∼</mml:mo><mml:mn mathvariant="normal">300</mml:mn></mml:mrow></mml:math></inline-formula> °C to remain in the gas stream long enough to undergo near-complete oxidation (Fig. 7A). This configuration reduces losses caused by low-temperature volatilization and improves recovery of reactive volatile species independently of their release temperature in the primary furnace. Measurements on standards including elemental S and pyrite confirm that the secondary oven significantly improve the oxidation efficiency (Fig. 7B, C). For instance, the measured <inline-formula><mml:math id="M211" display="inline"><mml:mi>d</mml:mi></mml:math></inline-formula>O<sub>2</sub> of pyrite increases from <inline-formula><mml:math id="M213" display="inline"><mml:mrow><mml:mn mathvariant="normal">2.03</mml:mn><mml:mo>×</mml:mo><mml:mn mathvariant="normal">104</mml:mn></mml:mrow></mml:math></inline-formula> <inline-formula><mml:math id="M214" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi><mml:mi mathvariant="normal">mol</mml:mi></mml:mrow></mml:math></inline-formula> g<sup>−1</sup> (without secondary oven) to <inline-formula><mml:math id="M216" display="inline"><mml:mrow><mml:mn mathvariant="normal">2.28</mml:mn><mml:mo>×</mml:mo><mml:mn mathvariant="normal">104</mml:mn></mml:mrow></mml:math></inline-formula> <inline-formula><mml:math id="M217" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi><mml:mi mathvariant="normal">mol</mml:mi></mml:mrow></mml:math></inline-formula> g<sup>−1</sup> when the secondary oven was maintained at 800 °C, corresponding to 95 %–99 % of theoretical oxidation capacity (Fig. 7C). Similarly, elemental S and other S-bearing standards displayed consistent increases in <inline-formula><mml:math id="M219" display="inline"><mml:mi>d</mml:mi></mml:math></inline-formula>O<sub>2</sub> with rising secondary oven temperatures (Fig. 7B). Natural samples from the black shales, Congo Basin peat samples and Lake Cadagno sediments also showed systematic <inline-formula><mml:math id="M221" display="inline"><mml:mi>d</mml:mi></mml:math></inline-formula>O<sub>2</sub> increases of 10 %–20 % with a secondary oven held at 800 °C (Fig. 7D–F). These results highlight the important role of a secondary furnace in minimizing the low-temperature volatilization losses, particularly for labile sulfur- and fresh organic-rich materials. The secondary oven improves both the accuracy and reproducibility of redox capacity measurements by ensuring more complete oxidation of volatile reaction intermediates.</p>

      <fig id="F7" specific-use="star"><label>Figure 7</label><caption><p id="d2e2845">Optimized redox capacity with secondary oven. <bold>(A)</bold>, Comparison of elemental sulfur measurements with secondary oven (at 800 °C, grey) and without secondary oven (teal); <bold>(B)</bold>, elemental sulfur; <bold>(C)</bold>, pyrite; <bold>(D)</bold>, black shale (MSG9); <bold>(E)</bold>, peat sediment from the Congo basin; <bold>(F)</bold>, Lake Cadagno sediment. Black dashed lines in <bold>(B)</bold>, <bold>(C)</bold> and <bold>(D)</bold> panels indicate the theoretical values.</p></caption>
          <graphic xlink:href="https://bg.copernicus.org/articles/23/4859/2026/bg-23-4859-2026-f07.png"/>

        </fig>

</sec>
</sec>
<sec id="Ch1.S4">
  <label>4</label><title>Kinetic fingerprints</title>
<sec id="Ch1.S4.SS1">
  <label>4.1</label><title>Oxygen consumption and redox-active components</title>
      <p id="d2e2898">Beyond quantifying total redox capacity, our approach yields kinetically resolved oxygen consumption profiles, providing a distinctive redox fingerprint of natural materials. The Congo Basin sample exhibits a bimodal oxidation pattern: <inline-formula><mml:math id="M223" display="inline"><mml:mrow><mml:mo>∼</mml:mo><mml:mn mathvariant="normal">37</mml:mn></mml:mrow></mml:math></inline-formula> % of the total O<sub>2</sub> corresponds to a low-temperature fraction oxidized in the 250–450 °C window, consistent with labile organic matter, whereas the dominant fraction (63.1 %) is associated with a higher-temperature fraction oxidized about 500 °C, consistent with more refractory organic matter (Fig. 8A). These two oxidation stages are supported with broad CO<sub>2</sub> peaks observed in the gas evolution data (Fig. 8D).</p>
      <p id="d2e2929">The Lake Cadagno sediment exhibits a mixed organic C–S oxidation signature, with 38.0 % of total O<sub>2</sub> uptake associated with low-temperature C and S-bearing compounds, and 62.0 % associated with higher-temperature oxidation of refractory carbon and pyrite oxidation extending to <inline-formula><mml:math id="M227" display="inline"><mml:mrow><mml:mo>∼</mml:mo><mml:mn mathvariant="normal">700</mml:mn></mml:mrow></mml:math></inline-formula> °C (Fig. 8B). The coupled evolution of CO<sub>2</sub>, SO<sub>2</sub>, COS and H<sub>2</sub>S (Fig. 8E) reflects the co-occurrence of sulfurized organic matter and pyrite, consistent with microbially mediated sulfurization of buried organic matter and pyrite formation under euxinic conditions.</p>
      <p id="d2e2978">In contrast, the fayalite standard is dominated by Fe(II) oxidation, which accounts for 88.1 % of the total O<sub>2</sub> consumption at high temperatures (500–900 °C), with only 11.9 % attributed to minor low-temperature organic matter oxidation (Fig. 8C). This interpretation is consistent with a weak low-temperature CO<sub>2</sub> peak and illustrates the method's ability to distinguish metal-driven versus organic- and sulfur driven redox carriers (Fig. 8C, F).</p>
      <p id="d2e2999">Together, these results demonstrate that integrating O<sub>2</sub> consumption profile with simultaneous gas evolution measurements enables the semi-quantitative partitioning of oxygen demand among labile organic matter, refractory carbon, sulfur-bearing phases, and Fe(II) minerals. This provides a kinetically resolved redox characterization of sediments and minerals that cannot be obtained from bulk elemental composition alone.</p>

      <fig id="F8" specific-use="star"><label>Figure 8</label><caption><p id="d2e3014">Oxidation profile with corresponding components. <bold>(A)</bold>, Congo Basin peat sample; <bold>(B)</bold>, Cadagno Lake sediment sample; <bold>(C)</bold>, fayalite sample; <bold>(D–F)</bold>, gases released through distinct reactions from samples <bold>(A)</bold>, <bold>(B)</bold> and <bold>(C)</bold>, respectively.</p></caption>
          <graphic xlink:href="https://bg.copernicus.org/articles/23/4859/2026/bg-23-4859-2026-f08.png"/>

        </fig>

</sec>
<sec id="Ch1.S4.SS2">
  <label>4.2</label><title>Sulfur cycle and diagenesis in Lake Cadagno</title>
      <p id="d2e3053">To further demonstrate the capabilities of our method, we applied it to a sediment profile from Lake Cadagno, a well-known system for studying microbial sulfur cycling and diagenetic processes (Berg et al., 2022; Berg et al., 2025; Dupeyron et al., 2025).</p>
      <p id="d2e3056">Our results reveal a distinct redox transition marked by a progressive decline in <inline-formula><mml:math id="M234" display="inline"><mml:mi>d</mml:mi></mml:math></inline-formula>O<sub>2</sub> and total organic carbon (TOC) with depth, while COS and H<sub>2</sub>S signals persist throughout the profile (Fig. 9A–D). In contrast, SO<sub>2</sub> release during analysis are low near the surface, increases sharply to a maximum around 18–20 cm, and then declines toward to the base of the core (Fig. 9E). This hump-shaped SO<sub>2</sub> pattern suggests a zone of intense S remobilization and re-oxidation, coinciding with the microbial redox transition zone in which sulfate reduction and sulfide mineral formation were particularly active.</p>
      <p id="d2e3102">The aE<sub>a</sub> spectra, derived from the SO<sub>2</sub> release kinetics, provide a deeper view into the progression of S diagenesis. In the upper zone (7–15 cm), the spectra exhibit multiple peaks spanning 80–240 kJ mol<sup>−1</sup>, consistent with metastable metal sulfides, including labile and metastable reduced-S phases, poorly crystalline Fe monosulfides and trace metal sulfides (Fig. 9F). In the transitional middle zone (15–19 cm), the spectra simplify to three partially overlapping peaks, indicating progressive loss of the most labile sulfide phases and their partial transformation into more stable S-bearing minerals (Fig. 9G). Below the 19–20 cm boundary, the spectra converge into a single dominant peak at approximately 140–160 kJ mol<sup>−1</sup>, consistent with the predominance of more thermally stable pyrite (Fig. 9H). This clear diagenetic transition from diverse assemblage of reactive sulfides to a more uniform and thermally stable phase of pyrite highlights the multi-stage and progressive nature of sulfur mineral maturation in anoxic sediments.</p>
      <p id="d2e3147">We interpret the 19–20 cm boundary as marking a sulfate-depletion front, consistent with recent study (Dupeyron et al., 2025). The transformation into stable pyrite is consistent with a reduction in the pool of reactive sulfur species and progressive sulfur mineral maturation with depth. By resolving activation-energy distributions, our method provides a new kinetic framework for tracking diagenetic fronts and reconstructing microbial sulfur cycling and redox transitions in anoxic environments.</p>

      <fig id="F9" specific-use="star"><label>Figure 9</label><caption><p id="d2e3153">Cadagno Lake depth profile. <bold>(A)</bold>, <inline-formula><mml:math id="M243" display="inline"><mml:mi>d</mml:mi></mml:math></inline-formula>O<sub>2</sub>; <bold>(B)</bold>, TOC content; <bold>(C)</bold>, COS; <bold>(D)</bold>, H<sub>2</sub>S; <bold>(E)</bold>, SO<sub>2</sub>; <bold>(F)</bold>, aE<sub>a</sub> spectral distributions of SO<sub>2</sub> from the upper zone (7–15 cm); <bold>(G)</bold>, aE<sub>a</sub> spectral distributions of SO<sub>2</sub> from the middle zone (15–19 cm). <bold>(H)</bold>, aE<sub>a</sub> spectral distributions of SO<sub>2</sub> from the lower zone (19–22 cm). Dash line indicates the diagenetic boundary.</p></caption>
          <graphic xlink:href="https://bg.copernicus.org/articles/23/4859/2026/bg-23-4859-2026-f09.png"/>

        </fig>

</sec>
<sec id="Ch1.S4.SS3">
  <label>4.3</label><title>Kinetic fingerprint of materials: redox chemistry</title>
      <p id="d2e3285">The TGA/DSC-MicroGC platform delivers a comprehensive combustion kinetic fingerprint, that is, a spectral signature of H, C, O, and S reactivity, captured in a single analytical run under controlled ramped oxidation. Fig. 10 represents the kinetic fingerprint for a representative sulfidic sediment from Lake Cadagno. It shows the progressive sample mass loss and the corresponding heat-flow profile, which together reflect oxidation reactions and associated bond transformations (Fig. 10A, B). Moreover, it tracks the oxidation-consumption profile and the evolution of diagnostic gases, including C- and S-bearing species (CO<sub>2</sub>, COS, SO<sub>2</sub> and H<sub>2</sub>S) (Fig. 10C, D and E). The apparent activation energy distributions further resolve the thermal reactivity of these phases. Low aE<sub>a</sub> (<inline-formula><mml:math id="M257" display="inline"><mml:mo lspace="0mm">∼</mml:mo></mml:math></inline-formula> 100–140 kJ mol<sup>−1</sup>) associated with H<sub>2</sub>S and COS evolution, are consistent with thermally labile organic S-bearing compounds, including sulfurized organic matter and/or, metastable sulfide phases as well as more labile carbon pools carbon. In contrast high aE<sub>a</sub> (<inline-formula><mml:math id="M261" display="inline"><mml:mo lspace="0mm">∼</mml:mo></mml:math></inline-formula> 150–180 kJ mol<sup>−1</sup>) associated with SO<sub>2</sub> and CO<sub>2</sub> evolution are consistent with the oxidation of more refractory phases, including pyrite and thermally stable carbon (Fig. 10F).</p>
      <p id="d2e3400">By synchronously resolving mass-loss kinetics, enthalpic transitions, activation-energy distributions, and near-continuous evolved gas profiles (Fig. 10), this approach provides a kinetically resolved characterization of redox-active phases. It links thermal stability, gas evolution and oxygen demand within a unified analytical framework.</p>

      <fig id="F10"><label>Figure 10</label><caption><p id="d2e3405">Kinetic fingerprint for a representative sulfidic sediment from Lake Cadagno. <bold>(A)</bold>, Sample mass (SM) changes at heating rates of 10 °C min<sup>−1</sup>; <bold>(B)</bold>, Heat flow (HF) profile; <bold>(C)</bold>, oxidation profile; <bold>(D)</bold>, H<sub>2</sub>S and COS profiles; <bold>(E)</bold>, SO<sub>2</sub> and CO<sub>2</sub> profiles; <bold>(F)</bold>, aE<sub>a</sub> distribution of H<sub>2</sub>S, COS, SO<sub>2</sub> and CO<sub>2</sub>.</p></caption>
          <graphic xlink:href="https://bg.copernicus.org/articles/23/4859/2026/bg-23-4859-2026-f10.png"/>

        </fig>

</sec>
</sec>
<sec id="Ch1.S5" sec-type="conclusions">
  <label>5</label><title>Conclusions</title>
      <p id="d2e3519">In this study, we present a novel, integrated TGA/DSC-MicroGC analytical system that enables kinetically resolved characterization of geological materials using high-resolution thermal analysis. By combining thermogravimetric analysis, differential scanning calorimetry and ultra-fast gas chromatography within a single experimental framework, this method provides a mechanistic approach for distinguishing complex C- and S-bearing reactive pools based on their thermal reactivity and gas-evolution signature It also quantifies total and fractional redox capacity (oxidability) through time-resolved monitoring of oxygen demand during controlled ramped oxidation. These insights about elemental speciation, reaction kinetics, and redox signature cannot be resolved using conventional bulk analyses alone.</p>
      <p id="d2e3522">Applications to natural archives from the Congo Basin and Lake Cadagno demonstrate the method's broad utility and environmental relevance. In Congo Basin peatland sediments, the method differentiated thermally labile and refractory organic carbon pools and quantifies their contrasting oxygen demand, providing a new framework for tracing changes in organic matter preservation and paleoenvionmental redox dynamics (Galvez et al., 2026). In Lake Cadagno sediments, our results reveal a diagenetic front defined by a shift in sulfide speciation and apparent activation-energy, providing a direct kinetic proxy for past microbial S cycling and redox evolution. More broadly, the integration of oxygen consumption profiles with evolved gas signatures allows semi-quantitative partitioning of redox-active components among organic matter, sulfur-bearing phases, and Fe-bearing minerals, yielding a kinetic “redox fingerprint” of geological materials.</p>
      <p id="d2e3525">Overall, this integrated approach moves beyond static compositional measurements toward a mechanistic, high-resolution framework for tracing coupled C-O-S dynamics in Earth materials. The TGA/DSC-MicroGC platform provides a versatile tool for investigating redox conditions, paleoenvironmental change, and biogeochemical reactivity across diverse natural systems. Future applications to marine sediments, soils, and ancient rock archives may further refine our understanding of redox processes across environmental settings and their links to elemental cycling and Earth evolution.</p>
</sec>

      
      </body>
    <back><notes notes-type="codedataavailability"><title>Code and data availability</title>

      <p id="d2e3532">The analysis scripts generated for this study are highly customized to the specific data infrastructure and require specific explanation for use. The code is, however, available upon reasonable request from the corresponding author, who can provide the necessary support and documentation. All relevant data related to this study are available via Zenodo (<ext-link xlink:href="https://doi.org/10.5281/zenodo.20739985" ext-link-type="DOI">10.5281/zenodo.20739985</ext-link>).</p>
  </notes><app-group>
        <supplementary-material position="anchor"><p id="d2e3538">The supplement related to this article is available online at <inline-supplementary-material xlink:href="https://doi.org/10.5194/bg-23-4859-2026-supplement" xlink:title="pdf">https://doi.org/10.5194/bg-23-4859-2026-supplement</inline-supplementary-material>.</p></supplementary-material>
        </app-group><notes notes-type="authorcontribution"><title>Author contributions</title>

      <p id="d2e3547">Conceptualization: M.E.G.; Investigation: S.W., M.E.G.; Methodology: S.W., M.E.G. Writing – original draft: S.W.; Writing – review and editing: S.W., S.L.J., M.E.G.</p>
  </notes><notes notes-type="competinginterests"><title>Competing interests</title>

      <p id="d2e3553">The contact author has declared that none of the authors has any competing interests.</p>
  </notes><notes notes-type="disclaimer"><title>Disclaimer</title>

      <p id="d2e3559">Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. The authors bear the ultimate responsibility for providing appropriate place names. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.</p>
  </notes><ack><title>Acknowledgements</title><p id="d2e3565">This project was supported through Swiss National Science Foundation grant 215097 and a Branco Weiss Society in Science fellowship (M.E.G.). This study was also supported by the Chinese Academy of Sciences-Pioneer Hundred Talents Program (grant E5710403 awarded to S. W) and the Swiss National Science Foundation (grants 200021_163003 and 200020_192361 awarded to S.L.J.). We thank J. Dupeyron, J. Marin Carbonne and Y. Garcin for proving the sediment samples from the Lake Cadagno and the Congo Basin as well as for their helpful discussions.</p></ack><notes notes-type="financialsupport"><title>Financial support</title>

      <p id="d2e3570">This research has been supported through Swiss National Science Foundation (grant 215097, grants 200021_163003 and 200020_192361) and the Chinese Academy of Sciences-Pioneer Hundred Talents Program (grant E5710403).</p>
  </notes><notes notes-type="reviewstatement"><title>Review statement</title>

      <p id="d2e3576">This paper was edited by Sebastian Naeher and reviewed by Małgorzata Labus and one anonymous referee.</p>
  </notes><ref-list>
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