Kefir as a Mixed Inoculum for Microbial Fuel Cells: Longitudinal Performance and Sustainability Implications

Abstract

Microbial fuel cells (MFCs) are promising bioelectrochemical systems for converting organic matter into electrical energy, but their practical relevance depends on both functional performance and sustainability-oriented viability. This study evaluated the bioelectrochemical behavior of double-chamber MFCs inoculated with kefir, comparing graphene and graphite anodes under fed-batch operation. A total of 33 MFC series were monitored longitudinally through voltage, current, power output, substrate consumption, and oxidation-reduction potential. Under the LED-connected closed-circuit configuration used here, kefir-inoculated reactors exhibited a reproducible electrical response together with near-complete substrate depletion. These findings support kefir as a workable mixed inoculum for comparative reactor operation under the tested conditions, although direct extracellular electron transfer and exclusive microbial causation of the measured signal were not demonstrated. Graphene showed higher early and mean electrical performance than graphite, particularly in power-related metrics, although this advantage decreased over time and did not result in a categorical separation of final batch-level outcomes. In contrast, substrate consumption remained highly similar between anode materials, indicating that the main material effect was expressed in electrochemical translation rather than in overall substrate conversion. Taxonomic profiling supported the presence of a metabolically complementary consortium dominated by lactic acid bacteria, acetic acid bacteria, Gram-negative bacteria, and yeasts. Deterministic sensitivity analysis and Monte Carlo-based LCA/TEA screening further showed that the most sustainable scenario was not necessarily the one with the highest electrical response, highlighting the importance of integrating performance, material burden, and uncertainty in MFC assessment.

1. Introduction

Microbial fuel cells (MFCs) are bioelectrochemical systems capable of converting the chemical energy stored in organic substrates into electrical current through the metabolic activity of microorganisms and their interaction with solid electrodes [1,2,3,4]. Since the early demonstration that microorganisms can transfer electrons to insoluble acceptors and harvest electricity under controlled conditions, MFCs have evolved into a relevant research platform at the interface of environmental biotechnology, electrochemistry, and renewable energy [1,2,3,4,5,6,7]. Their importance lies not only in electricity generation itself, but also in their broader potential for wastewater treatment, resource recovery, biosensing, and low-energy valorization of organic streams within circular bioeconomy frameworks [3,7,8,9].

The operation of MFCs depends on extracellular electron transfer (EET), a process through which electroactive microorganisms exchange electrons with conductive materials either directly or indirectly [5,6,10]. Within this framework, the anode plays a central role because its conductivity, surface chemistry, roughness, porosity, and biocompatibility influence microbial adhesion, biofilm establishment, interfacial electron transfer, and overall electrical response [6,7,11,12]. For this reason, anode material selection remains a critical design variable in MFC research. Carbon-based materials have been widely used because of their relative stability and conductivity, yet different forms of carbon do not necessarily perform equivalently. In particular, graphene-based materials have attracted attention due to their high conductivity and surface properties, whereas graphite remains a more conventional and economically accessible option [7,11,12]. However, superior electrochemical behavior of a given anode material does not automatically translate into superior system-level viability, especially when batch dynamics, material burden, and sustainability considerations are incorporated.

In parallel with material optimization, the microbial component of MFCs has become an equally important area of investigation. While many studies have focused on model electroactive taxa or wastewater-derived mixed inocula, there is increasing interest in unconventional microbial consortia capable of combining fermentation, acidification, metabolite exchange, and biofilm development [5,6]. Kefir is particularly interesting in this regard because it is a naturally structured symbiotic consortium composed mainly of lactic acid bacteria, acetic acid bacteria, and yeasts, embedded in a polysaccharide-rich matrix and characterized by strong ecological interactions [13,14,15,16,17,18]. Previous studies have shown that kefir grains harbor taxonomically diverse and metabolically complementary microbial populations, including organisms involved in carbohydrate breakdown, fermentation, acid production, and cross-feeding [13,14,15,16,17,18]. These characteristics make kefir a potentially attractive low-cost inoculum for bioelectrochemical systems, especially when the objective is not only to obtain electrical output but also to explore robust mixed-community functionality under simple operational conditions.

Another key dimension of current MFC research is the transition from proof-of-concept performance to sustainability-oriented evaluation. The interest in MFCs is increasingly tied to their ability to operate with low-value or waste-derived substrates and to contribute to circular resource use [8,9]. In this context, sugarcane by-products such as molasses are especially relevant because they are abundant carbon-rich streams with recognized potential as microbial feedstocks in biorefinery and valorization pathways [19]. Yet experimental studies frequently emphasize electrical metrics alone, without sufficiently linking reactor performance to broader questions of cost, material choice, environmental burden, or uncertainty. This limitation is increasingly important because integrated techno-economic assessment (TEA) and life cycle assessment (LCA) frameworks are now recognized as necessary tools for judging whether emerging biotechnologies are truly scalable and sustainable beyond laboratory performance claims [20].

Despite the progress in MFC materials and inoculum research, important gaps remain. First, the behavior of kefir-driven MFCs is still underexplored compared with more conventional inocula. Second, although graphene and graphite differ in physicochemical properties, it is not clear to what extent these differences translate into meaningful divergence in the temporal behavior of kefir-inoculated systems. Third, the practical relevance of any electrochemical advantage remains uncertain if it is not interpreted in relation to substrate use and, ultimately, to sustainability-oriented decision criteria. Therefore, the central research question of this study was: Do kefir-inoculated reactors operated as double-chamber MFCs exhibit a reproducible closed-circuit electrical response under the tested configuration, and to what extent does anode material (graphene vs. graphite) modify that temporal behavior?

Based on this question, the objective of the present study was to evaluate the functional bioelectrochemical behavior of double-chamber microbial fuel cells inoculated with kefir, comparing graphene and graphite anodes through longitudinal monitoring of voltage, current, power output, substrate consumption, and redox conditions, and to interpret the observed response in light of its relevance for low-cost and sustainability-oriented bioelectrochemical applications. By focusing on a mixed kefir consortium and two carbon-based anodes with different technological profiles, this study contributes to the understanding of how inoculum complexity and electrode material interact to shape MFC behavior, while also providing a more realistic basis for subsequent sustainability and viability assessment [6,7,13,14,15,16,17,18,19,20].

2. Materials and Methods

2.1. Study Design

2.1.1. Experimental Framework

This study was designed as a comparative longitudinal reactor experiment to evaluate the behavior of kefir-driven double-chamber microbial fuel cells (MFCs) operated under fed-batch conditions with two anodic materials, namely graphene and graphite. The conceptual architecture of the system is shown in Figure 1, in which the anaerobic anode chamber contained the kefir consortium and fermentable substrate, the cathode chamber contained an air-exposed zinc cathode operated under passive oxygen access, and both compartments were separated by a salt bridge. The experimental workflow integrated reactor operation, time-resolved electrochemical and physicochemical monitoring, molecular profiling of the kefir inoculum, longitudinal mixed-effects modeling of repeated measurements, series-level endpoint comparison, and sustainability-oriented deterministic sensitivity and Monte Carlo uncertainty analyses. This exploratory comparative design did not include abiotic, sterile, or no-inoculum controls. Accordingly, the study was structured to compare reactor behavior between graphene- and graphite-based kefir-inoculated units under identical operating conditions, rather than to isolate the exclusive microbial contribution to the measured electrical signal. The overall methodological logic followed established microbial fuel cell practice for reactor operation and electrical monitoring, together with standard statistical treatment for repeated-measures data and uncertainty propagation in scenario-based techno-economic and life-cycle-oriented assessment [3,21,22,23,24].

2.1.2. Treatment Definition and Response Variables

Two anodic treatments were defined: (i) graphene-anode MFCs and (ii) graphite-anode MFCs. All remaining operational conditions were kept constant across treatments, including reactor geometry, cathodic configuration, separator type, substrate loading regime, external LED-connected load, passive oxygen access at the cathode, and sampling schedule. A total of 33 independent MFC units were included in the experiment and distributed across the two anodic treatments under the same operating conditions. Each reactor was treated as an experimental unit for longitudinal analysis, allowing repeated observation of within-unit temporal dynamics while preserving between-unit heterogeneity across the full study design.

The primary directly monitored electrical response was closed-circuit voltage (V). Current (mA) was recorded under the same circuit condition and power (mW) was calculated from voltage-current data. Secondary measured variables were oxidation-reduction potential (ORP, mV), reducing sugar concentration expressed as glucose equivalents (g/L), and temperature (°C). In addition to observation-level variables, derived analytical variables were computed to support series-level interpretation, including cumulative energy, final glucose consumption fraction, total ΔORP, peak power, and mean power (

Table 1. Core design of the kefir-driven MFC experiment.

Item	Specification
Experimental design	Comparative, parallel-treatment longitudinal reactor study
Reactor type	Double-chamber microbial fuel cell
Anode treatments	Graphene; Graphite
Cathode configuration	Air-exposed zinc cathode under passive oxygen access
Salt Bridge	Low-cost salt bridge fabricated in-house
Operating mode	Fed-batch
Anode condition	Anaerobic
Monitoring period	20 days
Main response	Voltage (V)
Secondary measured responses	ORP

). The taxonomic composition of the kefir inoculum was characterized by amplicon sequencing and later summarized into broad functional guilds for ecological interpretation.

2.2. Double-Chamber MFC Construction and Operation

All microbial fuel cells used in this study were fabricated in-house. The complete reactor architecture, including the reactor body, chamber coupling system, electrode holders, and separator housing, was custom-built to maintain uniform geometry and operating conditions across all experimental units. The system was configured as a double-chamber MFC, with a biologically active anodic compartment and a physically separated cathodic compartment. Interconnections were made using 16 mm polyvinyl chloride (PVC) tubing (Plastigama Wavin, Durán, Guayas, Ecuador), selected for low cost, mechanical stability, and compatibility with the working solutions.

The anodic electrode was prepared in two treatment-specific versions: graphene and graphite. In both cases, the projected geometric area was kept constant across treatments to preserve comparable exposure conditions. The cathodic electrode consisted of a zinc plate operated under passive oxygen access, as represented in Figure 1. Each MFC was connected to one LED lamp acting as a non-ohmic external load, and the reactors were maintained under closed-circuit operation during monitoring. Before inoculation, all reactor components in contact with liquid phases were washed, rinsed with deionized water, and sanitized according to material compatibility.

Electrode, Membrane, and Circuit Configuration

The salt bridge used in this study was a low-cost option fabricated in-house for proton-conductive separation between chambers. The anode was mounted vertically within the anodic chamber to maximize exposure to the inoculated medium and to favor biofilm attachment. The cathode was installed in the opposite chamber and operated under passive exposure to atmospheric oxygen, without external air supply or forced aeration [25]. Voltage was monitored under closed-circuit conditions using a digital multimeter (Pro’sKit MT-1225; Prokit’s Industries Co., Ltd., New Taipei City, Taiwan) while each MFC remained connected to an LED-based non-ohmic external load. Because the external circuit was not based on a fixed resistor, voltage was treated as the primary directly interpreted electrical response, whereas current was recorded under the same circuit condition and power was calculated from the simultaneous electrical measurements. Because the external load was an LED rather than a fixed resistor, the electrical measurements represent operating-condition-specific circuit response and should not be interpreted as polarization data, coulombic-efficiency estimates, or standardized electrochemical benchmarks (

Table 2. Reactor hardware, materials, and assembly notes.

Component	Specification	Brand/Supplier	Notes
Reactor body	Double-chamber MFC reactor	In-house fabricated	Uniform design for all units
Salt Bridge	Low-cost Salt Bridge	In-house fabricated	Hydrated before installation
Graphene anode	Conductive graphene sheet/plate	Commercially purchased	Treatment-specific anodic material
Graphite anode	Graphite plate/sheet	Commercially purchased	Comparative anodic material
Cathode	Zinc plate/sheet	Integrated into custom reactor	Air-exposed, not actively aerated
Tubing and fittings	PVC tubing, 16 mm	Commercial	Reactor connection element
External load	One LED per MFC	Commercial electronic component	Non-ohmic external load

). The general assembly and operating logic were consistent with established double-chamber MFC methodology and with previous studies showing that oxygen-reduction performance can be sustained under passive, non-aerated cathodic operation [3,26].

2.3. Kefir Inoculum, Medium Composition, and Fed-Batch Operation

The biological inoculum consisted of an active kefir-derived consortium obtained from the main bioreactor and used as the anodic microbial source. Prior to reactor inoculation, biomass was transferred from the main bioreactor to the individual MFC units at a high inoculum loading that was kept qualitatively consistent across all reactors to promote rapid electrochemical establishment of the anodic consortium. The anodic medium was formulated as a fermentable substrate under oxygen-restricted conditions.

The anolyte was operated in fed-batch mode. As carbon source, 10 mL of molasses adjusted to 12° Brix were administered every 2 days to each reactor throughout operation. The catholyte was refreshed or checked as required to maintain cathodic oxygen-reduction performance. After inoculation, the anodic chamber headspace was minimized and the anodic compartment was maintained under oxygen-restricted conditions without additional nitrogen flushing. The cathode chamber operated without active aeration and remained under passive oxygen access from ambient air.

2.4. Physicochemical and Electrical Monitoring

Voltage, current, ORP, temperature, and reducing sugar concentration were monitored repeatedly over the 20-day experimental period. Measurements were acquired at predefined time points selected to capture the early adaptation stage, intermediate operation, and late-stage decline or recovery behavior. Physicochemical measurements were made directly on liquid samples or in situ when compatible with sensor design. Voltage and current were recorded under closed-circuit conditions while each MFC remained connected to one LED as external load. Power was then calculated from the corresponding electrical readings. Because the external circuit was non-ohmic, the electrical response was interpreted comparatively under the same operating condition across all reactors rather than as a standard fixed-resistance polarization assay.

Instruments, Calibration, and Sampling Schedule

ORP was measured using a portable multiparameter meter (BLE-C600, YIERYI, Shenzhen, China) according to the manufacturer’s operating instructions. Voltage and current were recorded using a digital multimeter (Pro’sKit MT-1225; Prokit’s Industries Co., Ltd., New Taipei City, Taiwan) under closed-circuit operation (

Table 3. Measurement workflow, analytical principle, and reporting units.

Variable	Type	Measurement Principle	Instrument/Method	Unit
Voltage	Directly measured	Closed-circuit voltage under LED-connected load	Proskit 1225, Taiwan	V
Current	Directly measured	Closed-circuit current under LED-connected load	Proskit 1225, Taiwan	mA
Temperature	Directly measured	Anode temperature	BLE-C600 (YIERYI, China)	°C
ORP	Directly measured	Redox electrode	BLE-C600 (YIERYI, China)	mV
Reducing sugars	Directly measured	UV–Vis colorimetry	DNS assay with external calibration	g/L glucose equivalents

). Reducing sugars were quantified by UV–Vis colorimetry using the 3,5-dinitrosalicylic acid (DNS) method with external calibration and expression as glucose equivalents [26]. Current was recorded directly with the multimeter under the same LED-connected closed-circuit configuration used for voltage monitoring; because the load was non-ohmic, these current values are specific to that operating circuit and were not derived from a fixed external resistance. The DNS assay was selected because it remains one of the most established colorimetric procedures for reducing-sugar determination in fermentation and bioprocess matrices. Measurements were collected on days 0, 1, 2, 3, 14, 15, 17, and 20 of reactor operation, covering the early, intermediate, and late stages of batch operation.

2.5. Molecular Characterization of the Kefir Consortium

Amplicon sequencing was used to characterize the microbial composition of the kefir consortium employed as inoculum for the anodic bioprocess. Because the taxonomic fingerprint included both bacterial taxa and a yeast signal, a dual-marker strategy was used: 16S rRNA gene amplicons for bacteria and ITS amplicons for fungi and yeasts. DNA was extracted from one homogenized composite kefir inoculum sample collected prior to reactor inoculation.

2.5.1. DNA Extraction, Library Preparation, and Sequencing

Total genomic DNA was extracted using the DNeasy PowerSoil Pro Kit (Qiagen, Hilden, Germany), following the manufacturer’s protocol, with bead-beating included to improve lysis of mixed Gram-positive, Gram-negative, and yeast-containing matrices. DNA concentration and purity were assessed prior to library preparation. For bacterial profiling, the V3–V4 region of the 16S rRNA gene was amplified using broadly validated primer combinations suitable for amplicon-based community analysis [27]. For fungal and yeast profiling, the ITS region was amplified using an ITS primer pair with documented performance for mixed fungal communities [28]. Indexed libraries were purified, quantified, pooled equimolarly, and sequenced on an Illumina MiSeq platform using paired-end chemistry and a dual-index strategy appropriate for multiplexed amplicon datasets [29].

2.5.2. Bioinformatic Processing and Guild Aggregation

Raw reads were demultiplexed, primer-trimmed, quality filtered, denoised, merged, and checked for chimeras using a QIIME 2 workflow [30] incorporating DADA2 for amplicon sequence variant inference [31]. Taxonomic classification of bacterial reads was performed against the SILVA database [32], whereas fungal and yeast reads were classified against the UNITE database [33] through marker-gene classification procedures implemented in the QIIME 2 environment [34]. The resulting amplicon sequence variants were collapsed to the genus or species level when assignment confidence allowed. These taxonomic assignments were interpreted as sequence-based classifications rather than direct culture-confirmed identification of viable strains. For ecological interpretation, the abundance table was subsequently reorganized into broad guilds relevant to kefir bioelectrochemistry, namely lactic acid bacteria, mixed Gram-negative bacteria, acetic acid bacteria, and yeasts, and relative abundances were normalized to 100% within sample prior to graphical display.

2.6. Data Structure and Derived Variables

All repeated measurements were organized in long format, with each observation linked to a unique reactor identifier, anodic treatment, and sampling time. In addition to directly measured variables, derived analytical variables were computed to support longitudinal and endpoint analyses. Power was calculated from voltage-current data, cumulative energy was used as an integrated batch-level descriptor of electrical output, and glucose consumption fraction was calculated relative to the corresponding initial substrate level. At the series level, the main derived endpoints included peak power, final cumulative energy, final glucose consumption fraction, total change in ORP, mean power, and maximum power per gram of consumed substrate. This structure allowed the experimental response to be evaluated both dynamically over time and comparatively at the reactor-series level.

2.7. Statistical Analysis of Longitudinal Performance and Series-Level Endpoints

Statistical analyses were conducted in R (v. 4.5.3). Descriptive statistics were calculated separately for graphene and graphite configurations. To evaluate longitudinal behavior, linear mixed-effects models were fitted for selected repeated-measures outcomes, using time, anode material, and their interaction as fixed effects, while reactor identity was included as a random effect to account for within-unit dependence [21]. This framework was selected because it is appropriate for repeated observations collected from the same experimental unit over time and permits simultaneous estimation of treatment effects, temporal trends, and treatment-by-time interactions.

Complementarily, reactor-series summaries were generated to compare endpoint performance across materials (

Table 4. Statistical analysis framework used in the study.

Analysis Block	Outcome(s)	Data Level	Method	Purpose
Descriptive analysis	Voltage, current, power, consumed glucose, ORP, cumulative energy	Observation	Summary statistics	To characterize central tendency and dispersion by anode material
Longitudinal modeling	Power_mW; Consumed_Glucose_g_L	Repeated measures	Linear mixed-effects models	To estimate time effects, material effects, and time × material interaction
Series-level comparison	Peak power, final cumulative energy, final consumption fraction, total ΔORP, mean power, max power per g consumed	Reactor series	Nonparametric comparison	To assess endpoint differences between graphene and graphite
Deterministic sensitivity	Cost per mWh; GWP per mWh	Scenario	One-at-a-time sensitivity analysis	To identify dominant economic and environmental drivers
Probabilistic sustainability screening	Viability, best-cost probability, best-GWP probability	Scenario	Monte Carlo simulation	To propagate uncertainty across LCA/TEA inputs

). Because the number of experimental units per material was moderate and the distributions of several summary variables were not assumed to be Gaussian, nonparametric comparisons were used for selected series-level endpoints. Results were interpreted at both the longitudinal and endpoint levels to distinguish between differences in temporal response trajectories and differences in final batch outcomes.

2.8. Deterministic Sensitivity Analysis and Monte Carlo Uncertainty Propagation for LCA/TEA Scenarios

To extend the interpretation of the experimental results toward sustainability-oriented decision support, a scenario-based LCA/TEA screening framework was constructed. Four scenarios were evaluated by combining anodic material (graphene or graphite) and substrate type (glucose or molasses). For each scenario, deterministic base-case values were assigned for key economic and environmental parameters, including substrate cost, inoculum cost, anode cost, cathode cost, reactor cost, expected lifetime cycles, water use, anode-related global warming potential (GWP), cathode-related GWP, reactor-related GWP, substrate GWP, end-of-life burden, and circularity-related credits when applicable.

One-at-a-time deterministic sensitivity analysis was first used to identify the parameters exerting the largest influence on cost per unit of electrical output and GWP per unit of electrical output. Subsequently, uncertainty propagation was performed by Monte Carlo simulation using bounded low-mode-high input ranges for the main economic and environmental parameters. This probabilistic approach was adopted because integrated TEA/LCA interpretation at early technology stages is highly sensitive to uncertainty in material cost, lifetime, and impact assumptions, and Monte Carlo propagation is widely used to characterize such uncertainty in economic and life-cycle assessment contexts [20,22,23,24]. In addition, recent MFC-oriented environmental and techno-economic studies support the relevance of incorporating material burden and scenario uncertainty when interpreting sustainability beyond laboratory electrical performance alone [35,36]. The resulting outputs were summarized as scenario-wise probability of viability, probability of being the lowest-cost scenario, and probability of being the lowest-GWP scenario (

Table 5. Scenario structure and uncertainty inputs for the integrated LCA/TEA analysis.

Scenario ID	Anode	Substrate	Main Varying Inputs
S1	Graphene	Glucose	Higher anode cost and anode GWP; conventional substrate cost and burden
S2	Graphite	Glucose	Lower anode cost and anode GWP; conventional substrate cost and burden
S3	Graphene	Molasses	Higher anode cost and anode GWP; lower substrate cost and lower substrate GWP; circularity credits
S4	Graphite	Molasses	Lower anode cost and anode GWP; lower substrate cost and lower substrate GWP; circularity credits

).

3. Results

3.1. Functional Bioelectrochemical Response of the Kefir-Inoculated MFC System

A total of 693 longitudinal observations from 33 MFC series were analyzed, including 17 graphene-anode series and 16 graphite-anode series, all inoculated with kefir and operated with zinc as cathode material. Overall, the kefir-inoculated reactors exhibited a coherent closed-circuit electrical response under the tested configuration, characterized by measurable voltage, current, and power output, together with progressive substrate depletion and moderate changes in oxidation-reduction conditions.

Descriptive statistics indicated that the graphene configuration showed higher average electrical performance than the graphite configuration. Mean current was 0.369 mA in graphene and 0.334 mA in graphite, while mean voltage was 0.729 V and 0.701 V, respectively. Consequently, mean power output was higher in graphene (0.296 mW) than in graphite (0.251 mW). Final cumulative energy also favored graphene (91.49 mWh) relative to graphite (73.98 mWh). In contrast, glucose consumption was nearly identical between configurations, with mean consumption fractions of 0.826 for graphene and 0.824 for graphite, indicating that substrate conversion proceeded to a very similar extent in both anode materials. ORP values were slightly lower in graphene (145.87 mV) than in graphite (150.29 mV), whereas temperature remained practically constant across both groups, close to 24.36 °C. Taken together, these descriptive results show that the main difference between anode materials was concentrated in the electrical response, not in global substrate depletion or thermal conditions (

Table 6. Descriptive statistics of electrochemical, substrate-consumption, redox, and operational variables in kefir-inoculated MFCs stratified by anode material. The table reports the number of observations, mean, standard deviation, median, interquartile range, minimum, and maximum for current, voltage, power, consumed glucose, consumption fraction, ORP, temperature, and cumulative energy.

Anode	Variable	N	Mean	SD	Median	IQR	Min	Max
GRAPHENE	Current_mA	357	0.369419	0.470394	0.195	0.309697	0.01	4.2
GRAPHENE	Voltage_V	357	0.728501	0.227232	0.805455	0.341818	0.11	1.22
GRAPHENE	Power_mW	357	0.295808	0.426992	0.144007	0.258337	0.002	4.452
GRAPHENE	Consumed_Glucose_g_L	357	4.522655	2.230661	4.658949	3.867013	0	8.048692
GRAPHENE	Glucose_Consumption_Fraction	357	0.826307	0.241914	0.90249	0.215066	0	1
GRAPHENE	ORP_mV	357	145.8683	33.8315	147.0909	44	7	222
GRAPHENE	Temperature_C	357	24.35987	0.40678	24.48	0.421818	23.03	25.1
GRAPHENE	Cumulative_Energy_mWh	357	91.49495	91.51432	60.63873	91.86122	0	425.3795
GRAPHITE	Current_mA	336	0.333568	0.285476	0.2275	0.356705	0.01	1.52
GRAPHITE	Voltage_V	336	0.700818	0.2067	0.679091	0.270909	0.02	1.35
GRAPHITE	Power_mW	336	0.251456	0.258193	0.145169	0.264276	0.0008	2.052
GRAPHITE	Consumed_Glucose_g_L	336	4.234736	2.39838	4.086114	4.195823	0	8.224868
GRAPHITE	Glucose_Consumption_Fraction	336	0.823873	0.241745	0.903879	0.211689	0	1
GRAPHITE	ORP_mV	336	150.2872	40.60899	145.9545	46.63636	44	252
GRAPHITE	Temperature_C	336	24.35658	0.310642	24.40955	0.37375	23.21	24.94
GRAPHITE	Cumulative_Energy_mWh	336	73.97782	53.93576	58.99781	68.46814	0	212.7504

).

The temporal trajectories confirmed that both materials followed the same general pattern: a relatively high initial response followed by a progressive decline over time. Voltage, current, and power were all highest during the early stage of the experiment and then gradually decreased, indicating that the MFC system was most active during the initial phase of substrate availability and then weakened as the batch progressed. Graphene maintained a higher mean voltage and power during the first half of the run, whereas graphite remained consistently lower but followed a similar temporal shape. By the final sampling points, both materials converged toward low power values, suggesting that the major difference between the anodes was the magnitude of the early response rather than a sustained divergence throughout the whole experiment (Figure 2).

Longitudinal mixed-effects modeling supported this interpretation. For Power_mW, time had a significant and negative effect (β = −0.03298, p < 0.001), confirming that power output decreased systematically during the experimental period. In addition, graphite started from a significantly lower baseline than graphene (β = −0.15635, p = 0.0279). However, the interaction between time and anode material was positive and significant (β = 0.01120, p < 0.001), indicating that the initial performance gap between materials became narrower over time. Therefore, graphene displayed a stronger early electrochemical response, but this advantage diminished as the system evolved. For Consumed_Glucose_g_L, time had a strong positive effect (β = 0.17503, p < 0.001), confirming progressive substrate consumption. In contrast, neither the main effect of anode material (p = 0.786) nor the time × material interaction (p = 0.360) was significant, indicating that the anode did not meaningfully alter the overall kinetics of glucose depletion (

Table 7. Longitudinal mixed-effects models for power output and glucose consumption as a function of time and anode material. Graphene was the reference category; time was modeled in days; reactor ID was included as a random intercept. Fixed-effect estimates, standard errors, z statistics, p values, and 95% confidence intervals are reported. Random-intercept variance is reported separately.

Outcome	Term	Estimate	Std. Error	z	p Value	95% CI
Power_mW	Intercept	0.625569	0.049506	12.636	1.33 × 10−36	0.528539 to 0.722599
Power_mW	Anode material (Graphite vs. Graphene)	−0.156350	0.071098	−2.199	0.027872	−0.295699 to −0.017001
Power_mW	Time (days)	−0.032976	0.002265	−14.557	5.28 × 10−48	−0.037416 to −0.028536
Power_mW	Time × anode material	0.011200	0.003253	3.443	0.000576	0.004823 to 0.017576
Power_mW	Random-intercept variance (reactor ID)	0.029742	—	—	—	—
Consumed_Glucose_g_L	Intercept	2.772343	0.454941	6.094	1.10 × 10−9	1.880675 to 3.664011
Consumed_Glucose_g_L	Anode material (Graphite vs. Graphene)	−0.177357	0.653359	−0.271	0.786042	−1.457917 to 1.103204
Consumed_Glucose_g_L	Time (days)	0.175031	0.008411	20.811	3.48 × 10−96	0.158547 to 0.191516
Consumed_Glucose_g_L	Time × anode material	−0.011056	0.012079	−0.915	0.360017	−0.034731 to 0.012618
Consumed_Glucose_g_L	Random-intercept variance (reactor ID)	3.354160	—	—	—	—

).

To provide transparent evaluation of model behavior, diagnostic plots for both mixed-effects models are shown in Figure 3. The power model displayed mild heteroscedasticity and upper-tail deviation from normality, but still supported comparative interpretation of the decline in electrical output and attenuation of the initial graphene–graphite gap. By contrast, the glucose-consumption model showed more structured residual behavior and stronger deviation in the Q–Q plot, consistent with a bounded batch-depletion process approaching an upper limit over time.

Accordingly, the power model is interpreted as a comparative longitudinal model, whereas the glucose-consumption model should be read mainly as a descriptive summary of temporal depletion rather than as a strict distributional model. The positive time × material coefficient for power indicates attenuation of the initial graphene–graphite gap over time, not reversal of the ranking.

3.2. Substrate Consumption and ORP Behavior During Batch Operation

The temporal behavior of substrate depletion further supports the functional interpretation of the system. Glucose consumption fraction increased rapidly during the early days of operation and approached near-complete depletion in both materials by the end of the experiment. The trajectories were almost superimposed, indicating that graphene and graphite supported very similar substrate transformation profiles. This is consistent with the mixed-model results, where time strongly explained glucose consumption but anode type did not.

ORP followed a different pattern. Rather than showing a strong monotonic trend, it displayed moderate fluctuations over time and a broader dispersion than the substrate signal. Graphite tended to remain slightly above graphene at several time points, but the trajectories overlapped broadly and did not show a stable or structurally separated pattern. The absence of a clear ORP divergence between materials suggests that redox changes reflected the evolving internal reactor environment rather than a categorical difference imposed by the anode.

The combined reading of glucose depletion and ORP indicates substantial substrate conversion under both materials, whereas the redox environment changed only moderately and without a pronounced material-specific structure. Under the tested LED-connected configuration, the anode effect was more evident in circuit-level electrical response than in overall substrate depletion or general redox progression (Figure 4). This pattern is consistent with substantial fermentative substrate conversion occurring in parallel with only partial translation of reducing equivalents into the external circuit. Accordingly, substrate depletion in the present study should not be interpreted as evidence of efficient electron capture at the anode.

3.3. Taxonomic and Functional Composition of the Kefir Consortium

The sequencing-based taxonomic fingerprint of the kefir consortium revealed a mixed microbial assemblage with substantial representation of lactic acid bacteria (LAB), Gram-negative taxa, acetic acid bacteria (AAB), and yeasts (Figure 5A). At the genus/species level, Lactococcus lactis was the most abundant taxon (22.00%), followed by Pseudomonas (13.09%), Acinetobacter baumannii (12.39%), Citrobacter (10.19%), Acetobacter (9.52%), and Lactobacillus helveticus (9.19%). Additional contributors included Lactobacillus kefiranofaciens (6.48%), unclassified Enterobacteriaceae (6.19%), Saccharomyces cerevisiae (4.71%), Enterococcus (4.56%), and Lactobacillus kefiri (1.68%). Thus, the inoculum cannot be described as a narrowly LAB-restricted consortium; rather, it constitutes a functionally heterogeneous community with both fermentative and redox-active potential. Because these assignments were obtained from amplicon-based profiling, they should be interpreted as sequence-based community signatures rather than definitive culture-confirmed identifications of viable taxa.

When the same community was collapsed into broad functional guilds, LAB accounted for 43.9% of the total relative abundance, followed closely by mixed Gram-negative bacteria at 41.9%, while AAB and yeasts contributed 9.5% and 4.7%, respectively (Figure 5B). This distribution is relevant because it suggests that the reactor-level electrical response observed in the MFCs likely emerged from a consortium architecture based on metabolic complementarity rather than on dominance of a single taxonomic lineage. In particular, the large LAB fraction is consistent with rapid carbohydrate turnover and acidogenic primary fermentation, whereas the Gram-negative fraction plausibly contributes broader respiratory flexibility, redox turnover, and ecological bridging among metabolic niches. The presence of Acetobacter adds an oxidative layer capable of transforming intermediate metabolites, and the detection of S. cerevisiae is compatible with a supporting fermentative role and structural contribution to consortium dynamics.

From a bioelectrochemical standpoint, the community profile is compatible with a distributed metabolic network capable of sustaining substrate conversion, metabolite cross-feeding, and redox coupling under anaerobic anodic conditions. The taxonomic pattern also provides an explanatory framework for the temporal results described above: rapid reducing sugar depletion is congruent with strong fermentative pressure, while the progressive improvement in electrochemical descriptors suggests that metabolic turnover was accompanied by establishment of a more stable electrode-associated reaction interface. Accordingly, the sequencing data support an ecological interpretation in which the inoculum had sufficient compositional complexity to plausibly sustain mixed-community substrate conversion and redox-active interactions. However, because the sequencing was performed on the inoculum rather than on anode-attached biofilm, these data should not be interpreted as direct evidence of which taxa were enriched at the electrode or directly involved in electron transfer. These functional interpretations should therefore be considered ecologically plausible but inferential, since amplicon abundance alone does not demonstrate direct electroactivity or metabolic dominance under reactor operation.

3.4. Series-Level Comparison of Key Performance Endpoints

When the analysis was shifted from individual longitudinal observations to the level of entire MFC series, the same general trend remained visible, although the differences between anode materials became less pronounced. Graphene reached a higher mean peak power (1.143 mW) than graphite (0.884 mW), and it also achieved a higher mean final cumulative energy (135.89 mWh vs. 117.26 mWh). Mean power at the series level was likewise higher in graphene (0.296 mW) than in graphite (0.251 mW). However, final glucose consumption fraction was nearly identical in both groups, with values of 0.9989 for graphene and 0.9964 for graphite, confirming that both materials supported almost complete substrate exhaustion. Total ORP change also remained comparable between groups, with mean values of 12.59 mV for graphene and 9.38 mV for graphite (

Table 8. Series-level comparison of key experimental endpoints according to anode material. Values are reported at the MFC-series level as mean ± SD, together with median (IQR) to support nonparametric interpretation. Between-group comparisons were performed using Mann–Whitney U tests.

Endpoint	Graphene (N = 17), Mean ± SD	Graphite (N = 16), Mean ± SD	Graphene Median (IQR)	Graphite Median (IQR)	Mann–Whitney U	p Value
Peak power (mW)	1.143 ± 0.987	0.884 ± 0.470	0.896 (0.705–1.050)	0.791 (0.599–0.962)	155.5	0.494
Final cumulative energy (mWh)	135.89 ± 105.61	117.26 ± 56.50	95.94 (65.76–186.58)	87.53 (73.86–161.24)	138.0	0.957
Final glucose consumption fraction	0.9989 ± 0.0028	0.9964 ± 0.0057	1.0000 (1.0000–1.0000)	1.0000 (0.9942–1.0000)	164.0	0.250
Total ΔORP (mV)	12.59 ± 36.18	9.38 ± 46.89	13.00 (3.00–30.00)	3.50 (−11.00–37.00)	150.5	0.614
Mean power (mW)	0.296 ± 0.233	0.251 ± 0.121	0.213 (0.140–0.397)	0.179 (0.157–0.347)	141.0	0.871
Maximum power per g consumed (mW per g consumed)	0.425 ± 0.250	0.407 ± 0.265	0.419 (0.277–0.543)	0.293 (0.212–0.542)	149.0	0.653

).

Despite these numerical differences, the between-series comparisons did not reveal statistically significant separation. Peak power showed broad overlap between the two groups (p = 0.494), and final cumulative energy was even less differentiated (p = 0.957). Similarly, final glucose consumption fraction (p = 0.250), total ΔORP (p = 0.614), mean power (p = 0.871), and maximum power per gram of consumed substrate (p = 0.652) did not differ significantly between materials. These results indicate that the electrical advantage of graphene was real at the level of mean tendencies, but it was not strong enough to generate an unequivocal endpoint separation once variability between experimental series was taken into account.

The boxplots reinforce this interpretation. Graphene tended to occupy higher positions for peak power and cumulative energy and included some high-performing series, whereas graphite appeared somewhat more compact and less dispersed. However, the ranges overlapped markedly, and no variable showed complete segregation between the two materials. Final consumption fraction was very close to the upper limit in both groups, and total ΔORP displayed broad dispersion with both positive and negative values. Accordingly, the material effect should be interpreted as moderate rather than categorical: graphene tended to provide better electrical output, but graphite remained functionally comparable in terms of substrate depletion and final experimental behavior (Figure 6).

In relation to the research question, this is a key result. The data do not support the statement that one anode material was unequivocally superior in all dimensions. Instead, they show that graphene generally improved early and mean electrical performance, whereas graphite remained broadly comparable at the level of batch completion and final substrate use.

3.5. Deterministic One-at-a-Time Sensitivity of the LCA/TEA Base Case

Because the experimental differences between materials were moderate rather than absolute, the sustainability-oriented interpretation of the system was examined through deterministic sensitivity analysis. The one-at-a-time sensitivity analysis revealed that the economic and environmental outcomes of the deterministic base case were dominated by a relatively small set of input parameters, rather than by electrical output alone.

For the economic dimension, the most influential variables were lifetime cycles, reactor cost per unit, inoculum cost per batch, and anode cost per unit, indicating that cost viability depended strongly on durability and capital-related inputs. In practical terms, this means that even when a configuration showed somewhat better electrical performance, its economic competitiveness could still be undermined by short service life, expensive materials, or high reactor and inoculum costs. Substrate cost had comparatively little effect in the selected base case, suggesting that the cost structure was more strongly driven by infrastructure and component replacement than by feedstock alone.

For the environmental dimension, the strongest drivers were end-of-life GWP, lifetime cycles, and anode GWP, showing that environmental feasibility was especially sensitive to durability and material burden. The contribution of substrate GWP remained comparatively small, whereas the role of end-of-life assumptions was large enough to alter the overall environmental ranking of the system. This indicates that environmental performance was not simply a function of instantaneous electrical output, but rather of how long the system lasts and how burdensome its materials are over the full life cycle.

These deterministic results are important because they shift the interpretation of viability away from a narrow focus on peak power. Even if graphene performed somewhat better electrically, the sustainability ranking of the system could still be governed by reactor cost, inoculum cost, anode footprint, and service life. Thus, the deterministic sensitivity analysis already suggested that technical performance alone was insufficient to define the most viable configuration (Figure 7).

3.6. Probabilistic Economic and Environmental Performance Under Monte Carlo Simulation

The probabilistic Monte Carlo framework confirmed and extended the deterministic interpretation. Rather than relying on a single point estimate, the simulation propagated uncertainty across the main economic and environmental inputs and generated performance distributions for the four evaluated scenarios. This allowed the analysis to move from mean comparisons toward probability-based viability.

The cost distributions showed clear separation among scenarios. The graphite-based molasses scenario (S4) occupied the lowest cost range and demonstrated the strongest probability of economic competitiveness, whereas the graphene-based scenarios were shifted toward higher cost values. The environmental distributions followed a similar pattern. Again, S4 concentrated more heavily in the lower GWP range, while the graphene configurations tended to remain at higher burdens. These patterns indicate that the modest electrical advantage of graphene was not sufficient to offset the combined effects of cost and environmental burden when uncertainty was explicitly incorporated (Figure 8).

The probabilistic summary confirmed this conclusion. The overall probability of viability was highest for S4 (0.75965), followed by S2 (0.67070), whereas S1 (0.03860) and S3 (0.05610) showed much weaker viability profiles (

Table 9. Monte Carlo probabilistic summary of economic and environmental viability across scenarios. The table reports scenario-wise viability probabilities, probability of being the lowest-cost scenario, and probability of being the lowest-GWP scenario under uncertainty propagation.

Scenario ID	Probability Viable	Probability Best Cost	Probability Best GWP
S1	0.0386	0.00435	0.02465
S2	0.6707	0.47785	0.38995
S3	0.0561	0.00475	0.0385
S4	0.75965	0.51305	0.5469

). In addition, S4 had the highest probability of being the best scenario in terms of cost (0.51305) and also the highest probability of being the best in terms of GWP (0.54690). Therefore, once uncertainty in costs, durability, and environmental burdens was propagated through the system, the configuration with the strongest sustainability profile was not the one with the highest average electrical output, but rather the one with the best overall balance between performance, material burden, and life-cycle assumptions.

This finding substantially strengthens the interpretation of the experimental results. The data show that the kefir-inoculated MFC system was functionally active and that graphene tended to improve electrical performance. However, the Monte Carlo results show that electrical improvement alone did not guarantee higher viability. Under uncertainty, the more sustainable outcome was associated with the scenario that minimized economic and environmental burden rather than maximizing electrical response alone.

3.7. Overall Answer to the Research Objective

Taken together, the results show that kefir-inoculated reactors generated a reproducible circuit-level electrical response under the tested configuration, and that anode material modulated mainly the magnitude of the early response rather than final batch completion. When uncertainty-sensitive sustainability analysis was incorporated, the graphite–molasses scenario emerged as the most robust option. Thus, the evidence supports a comparative conclusion rather than a mechanistic one: the kefir-based system functioned under the tested setup, anode material influenced electrical performance, and overall viability depended on the combined interaction of performance, material burden, durability, and uncertainty.

4. Discussion

4.1. Functional Significance of the Kefir-Driven Bioelectrochemical Response

The present study shows that kefir can sustain a coherent reactor-level electrical response in double-chamber MFCs operated under fed-batch conditions, as evidenced by measurable voltage, current, and power generation together with near-complete substrate depletion. This result is relevant because it moves kefir from the category of an unconventional fermentative consortium to that of a biologically active mixed inoculum capable of supporting comparative reactor-level electrical response under the present operating conditions. The finding is consistent with the emerging view that mixed eukaryotic–prokaryotic consortia can support distributed reactor-level electrical behavior through complementary metabolic roles rather than through the dominance of a single classical exoelectrogen [37,38,39,40].

This interpretation is also aligned with the only directly comparable kefir-focused MFC study presently available, in which kefir was shown to act as a viable anodic consortium capable of generating measurable power under two-chamber operation [38]. However, the present work extends that line of evidence in two important ways. First, it evaluates kefir under a longer longitudinal window, which makes it possible to interpret not only peak behavior but also temporal decline and batch completion. Second, it embeds reactor performance within a broader framework that includes substrate depletion, ORP evolution, taxonomic composition, and sustainability-oriented uncertainty analysis. In that sense, the contribution of the study is not merely to confirm that kefir “works”, but to show that kefir-driven MFCs can be interpreted as ecologically complex but analytically tractable systems.

The broader significance of this result lies in the consortium logic itself. Recent literature increasingly emphasizes that mixed-culture MFCs may outperform or outlast monoculture systems not because each individual member is strongly exoelectrogenic, but because microbial synergy improves substrate turnover, metabolite exchange, endogenous mediator availability, and ecological resilience [39,40]. This is especially relevant for kefir, whose microbial organization is naturally based on co-existence among lactic acid bacteria, acetic acid bacteria, and yeasts. Thus, the present results support the view that kefir is not simply a low-cost inoculum, but a biologically structured consortium with sufficient metabolic breadth to sustain comparative reactor-level electrical response under non-sterile and low-complexity operating conditions.

4.2. Ecological Interpretation of the Kefir Consortium and Plausible Metabolic Coupling

The taxonomic fingerprint observed in this study supports a discussion centered on metabolic complementarity rather than single-organism causality. The dominance of LAB, together with the substantial presence of Gram-negative bacteria, Acetobacter, and Saccharomyces cerevisiae, suggests that the observed reactor behavior likely emerged from a layered metabolic sequence involving primary sugar fermentation, intermediate metabolite oxidation, and indirect or distributed redox coupling. Such a structure is consistent with current understanding of interspecies interactions in bioelectrochemical systems, where cooperative substrate use and metabolite-enabled mutualism are increasingly recognized as important drivers of MFC performance [39,40].

Within that framework, the detection of Acetobacter and S. cerevisiae is particularly meaningful. Recent work on integrated biocatalysis in MFCs has shown that coupling fermentative yeast metabolism with oxidative acetic acid bacterial activity can support biomass valorization and functional current generation through interlinked biotransformations [37]. Earlier studies also demonstrated that Acetobacter aceti can participate directly in current-generating systems, and that mixed oxidative bacterial cultures may outperform their corresponding pure-culture configurations [41]. Similarly, yeast-centered MFC research continues to show that S. cerevisiae can serve as a relevant bioelectrocatalytic component, although its electron-transfer efficiency is typically context-dependent and often enhanced by the surrounding biochemical environment [42]. Taken together, these precedents strengthen the ecological plausibility of the present consortium-level interpretation.

Importantly, the present data do not prove direct electroactivity for each detected taxon, and the discussion should not overstate that point. Amplicon abundance is not equivalent to functional dominance during reactor operation. Nonetheless, the observed taxonomic structure is fully compatible with a distributed network in which LAB accelerate carbohydrate turnover, yeasts contribute fermentative conversion and cross-feeding potential, and acetic/Gram-negative fractions contribute oxidative transformation and redox flexibility. From that perspective, the kefir consortium appears less like a random inoculum and more like a metabolically tiered community capable of plausibly supporting the observed reactor behavior through cooperative ecological architecture.

4.3. The Anode Effect Was Real, but Not Decisive

One of the clearest outcomes of the study is that graphene improved early and mean electrical performance, yet this advantage did not translate into unequivocal superiority at the level of final reactor-series endpoints. This pattern is fully consistent with the broader literature on anode materials, which shows that the effect of the anode is often strongest at the interface level—through conductivity, surface area, roughness, defect structure, and microbial attachment behavior—rather than as a deterministic driver of final batch completion [43,44]. In other words, anode material can shape the kinetics and intensity of the electrochemical response without necessarily redefining the global trajectory of substrate conversion.

This distinction is important. Graphene-based materials are often discussed as intrinsically superior because of their high conductivity and favorable surface properties, and reviews of graphene-modified electrodes have repeatedly highlighted their capacity to improve electron-transfer efficiency and microbe–electrode interaction [44]. Yet the experimental literature also shows that comparisons among graphite, graphene, and graphene-derived materials do not always produce categorical winners. In dual-chamber systems, performance differences can be sensitive to catholyte conditions, hydrophilicity, surface treatment, and reactor architecture, sometimes yielding moderate rather than transformative gains [45]. The present study fits that second pattern: graphene had a measurable advantage, especially in the early response, but the wide overlap between materials at the series level indicates that the effect was not large enough to define a universally superior configuration.

Mechanistically, this result suggests that the rate-limiting step in the present system was not governed by anode material alone. The near-identical glucose depletion profiles imply that both materials supported similar overall substrate conversion, while the stronger divergence in power indicates that graphene improved the electrical expression of that metabolism rather than the metabolism itself. This is a subtle but important point for the manuscript: the study does not show that graphene altered the fundamental ability of kefir to transform substrate, but rather that it enhanced how effectively that underlying bioconversion was translated into measurable electrical output. As a result, the anode effect should be discussed as interface optimization, not as wholesale metabolic reprogramming.

4.4. Why Substrate Depletion and Electrical Output Did Not Move in Parallel

Another important interpretive result is the partial decoupling between substrate depletion and electrical performance. Glucose was consumed almost completely under both anode materials, but power declined progressively during operation and differences in substrate consumption were not statistically significant. This indicates that total substrate use cannot be interpreted as a proxy for electrical efficiency in the present system. Such a decoupling is common in mixed-culture MFCs, where substrate conversion can continue through fermentative and acidogenic routes even when electron capture at the anode becomes progressively less efficient [39,40].

The explanation is likely multifactorial. Under batch conditions, the early phase of operation typically concentrates the highest availability of fermentable substrate and the strongest redox gradient, favoring higher electrical output. As fermentation proceeds, intermediate metabolites accumulate or are redistributed, biofilm architecture evolves, and the effective fraction of electrons captured by the electrode may decline even while substrate transformation remains active. In mixed consortia, part of the carbon and reducing equivalents can be diverted toward biomass growth, soluble metabolites, internal maintenance, or non-electrode electron sinks. Accordingly, high substrate removal does not necessarily imply sustained electrical harvesting. This framework also helps explain why ORP changed only moderately and without clear material-dependent separation: the reactor environment remained functionally active, but not all of that activity translated into the external circuit.

From a discussion standpoint, this distinction strengthens the manuscript rather than weakens it. It allows the study to move away from an overly simplistic “more substrate consumed equals better MFC” narrative and toward a more nuanced interpretation in which bioelectrochemical performance is one expression of a broader microbial conversion system. That position is more consistent with current research on mixed-culture MFCs, where synergy can improve robustness and substrate flexibility even when electrical outputs remain modest compared with highly engineered or strongly optimized systems [39,40].

4.5. Sustainability Interpretation Beyond Peak Power

The sustainability-oriented analyses represent one of the most valuable parts of the study because they prevent the discussion from equating electrical advantage with technological superiority. Recent reviews on microbial electrochemical systems have emphasized that techno-economic and life-cycle interpretation is essential if laboratory MFC results are to be evaluated meaningfully in relation to scale-up, materials burden, and circular-economy relevance [46]. More broadly, wastewater and bioresource technologies are increasingly being judged through integrated TEA/LCA frameworks that compare not only technical output but also material demand, durability, end-of-life behavior, and uncertainty [47].

In that context, the deterministic sensitivity and Monte Carlo analyses materially change the interpretation of the experimental data. Graphene tended to perform better electrically, but once cost structure, life-cycle burden, and uncertainty were included, the graphite-based molasses scenario emerged as the most robust option. This is methodologically and conceptually important. It shows that the practical ranking of MFC configurations can invert when viewed through a systems perspective. In other words, the best-performing electrical material is not necessarily the best-performing sustainability option. That conclusion is highly consistent with recent work showing that electrode design, capital intensity, and service life frequently dominate environmental and economic performance in microbial electrochemical technologies [46,47].

The broader literature on next-generation anodes also supports this interpretation. Highly engineered materials can substantially enhance biofilm formation and electron-transfer kinetics, as demonstrated in recent wastewater-focused reactor studies using advanced electrode architectures [48]. However, those gains do not automatically guarantee favorable scale-up economics or lower life-cycle burden. The present results therefore support a more disciplined discussion: advanced conductive materials remain valuable for understanding interface behavior and maximizing reactor output, but low-cost and lower-burden alternatives may become more attractive once uncertainty, replacement cycles, and circular substrate integration are considered.

4.6. Implications for Circular Bioeconomy and Future Hybridization Pathways

One of the most promising implications of this study is that kefir-driven MFCs can be discussed within a circular-bioeconomy frame rather than only as proof-of-concept electrochemical devices. The molasses-based scenarios showed that low-cost or waste-derived substrates can materially improve viability when combined with lower-burden materials, which supports a waste-to-energy or waste-to-value interpretation of the system. This is important because MFC technologies are unlikely to become broadly relevant if they depend on expensive substrates and high-impact materials while delivering modest electrical outputs. Their stronger niche may instead lie in integrated platforms that combine waste conversion, energy recovery, and bioprocess intensification.

That perspective is also consistent with emerging work on hybrid bioelectrochemical systems. Recent techno-economic and life-cycle studies of integrated dark fermentation–microbial electrolysis pathways show that hybridization across biological and electrochemical units can improve the value proposition of waste-derived conversion chains by redistributing functions across stages rather than demanding that one unit operation achieve all performance goals alone [49]. In a similar conceptual sense, kefir-driven MFCs may be more useful as components within broader valorization cascades—especially where fermentative conversion, low-cost inoculation, and partial electricity recovery can be combined with downstream upgrading—than as standalone high-power devices. The present data support that argument because the reactor clearly functioned, but its strongest contribution may lie in its compatibility with low-cost consortia and circular substrates rather than in absolute electrical output.

4.7. Study Limitations and Future Research Directions

The study also has limitations that should be acknowledged explicitly. First, the taxonomic profile was generated from the inoculum rather than from time-resolved biofilm samples, so functional interpretation remains ecological rather than directly mechanistic. Second, the external LED-connected configuration does not permit the same electrochemical standardization as a classic fixed-resistance or full polarization-based workflow, meaning that the electrical outputs should be interpreted comparatively within this experimental design rather than as universal benchmark values. Third, the sustainability block is intentionally a screening-level LCA/TEA framework and not a full industrial inventory. Its strength lies in uncertainty-aware comparison, not in definitive scale-up prediction. A further limitation is that the experimental design did not include abiotic, sterile, or no-kefir controls. Therefore, although kefir-inoculated reactors showed a reproducible electrical response under the tested configuration, the present dataset does not allow exclusive attribution of that signal to microbial metabolism alone. In addition, because the cathode consisted of air-exposed zinc, a galvanic contribution from zinc oxidation cannot be excluded. For that reason, absolute cell voltages, particularly the highest observed values should be interpreted cautiously and not as direct evidence of purely microbial anodic performance. An explicit voltage–current polarization relationship was not generated; therefore, power values should be interpreted only as operating-condition-specific comparative outputs under the LED-connected circuit. No cyclic voltammetry, electrochemical impedance spectroscopy, or polarization analyses were performed. Consequently, the present work supports comparative interpretation of reactor-level electrical behavior, but it does not provide direct mechanistic confirmation of extracellular electron transfer. In addition, the residual diagnostics associated with the mixed-effects models indicate that the glucose-consumption model departs more strongly from Gaussian assumptions than the power model. This pattern is consistent with a bounded longitudinal depletion process approaching an upper limit during batch operation, in which standard linear mixed modeling may serve mainly as an approximation and floor/ceiling effects can affect inference [50]. Accordingly, the glucose-consumption model should be interpreted primarily as a descriptive temporal summary, and future work should consider nonlinear mixed models or bounded longitudinal approaches.

These limitations point directly to future work. The next stage should include time-resolved biofilm sequencing or transcript-informed analysis to identify which members of the kefir consortium become functionally enriched at the anode during operation. It should also include reactor designs that enable direct comparison between batch depletion, coulombic efficiency, and resistance evolution. Finally, the sustainability logic introduced here should be expanded toward hybrid and staged valorization frameworks in which low-cost substrates, fermentative pretreatment, and downstream bioelectrochemical recovery are assessed as connected rather than isolated modules [46,49]. Such an approach would be especially valuable for determining whether kefir-driven systems are better positioned as stand-alone MFCs or as consortium-based units within broader biomass valorization platforms.

5. Conclusions

This study shows that kefir can serve as a workable mixed inoculum in double-chamber reactors operated under fed-batch conditions, supporting a reproducible circuit-level electrical response together with near-complete substrate depletion under the tested setup. These findings indicate that kefir-supported reactors combined active substrate conversion with measurable electrical output, but the present design does not isolate direct anodic electron transfer from other electrochemical contributions.

The comparison between graphene and graphite indicates that anode material influenced reactor behavior, mainly by increasing early and mean electrical output in graphene. However, this effect diminished over time and did not produce statistically decisive separation in final series-level endpoints. The practical implication is that graphene improved electrical expression under the tested configuration, but did not alter overall substrate depletion.

The sequencing-based inoculum profile was consistent with a metabolically diverse community that could plausibly support fermentative turnover, metabolite exchange, and redox-active interactions. Because sequencing was performed on the inoculum rather than on anode-attached biofilm, these data should be interpreted as ecological context rather than direct proof of which taxa mediated electron transfer.

A major contribution of the work lies in the fact that reactor interpretation was not restricted to electrical output alone. By incorporating deterministic sensitivity analysis and Monte Carlo-based uncertainty propagation within a scenario-oriented LCA/TEA framework, the study showed that the configuration with the strongest sustainability profile was not necessarily the one with the highest average electrical response. Once material burden, durability, substrate cost, and uncertainty were considered jointly, the graphite–molasses scenario emerged as the most robust option. This finding is particularly relevant because it shifts the evaluation of microbial fuel cells away from a narrow performance-maximization perspective and toward a systems-based understanding of technological viability. In practical terms, the results suggest that moderate electrical gains obtained through more advanced electrode materials may be outweighed by cost and environmental penalties when viewed through a broader sustainability lens. These conclusions should be interpreted within the limits of a control-free exploratory design and a zinc-based cathodic configuration, both of which constrain strict mechanistic attribution.

Overall, this study supports kefir-driven MFCs as a comparative low-cost research platform whose practical value is best interpreted through the combined lens of reactor performance, microbial ecology, material burden, and uncertainty-aware sustainability assessment.