The S. birrea-mediated nanoparticles were produced in our previous work (Rama et al., 2025). Briefly, S. birrea shell powder was boiled for 30 min in deionized water (dH₂O; 25 g L⁻¹) with continuous stirring. The liquid extract was separated from the shell powder by centrifugation (10 min, 2,683 × g) using a Digital Centrifuge DSC-301SD (New Taipei City, Taiwan) and filtered using Whatman 52 filter paper (Rama et al., 2023). The S. birrea-mediated CaP NPs were prepared previously (Rama et al., 2025), whereby calcium nitrate (Ca(NO₃)₂· × H₂O, Merck, Darmstadt, Germany) and anhydrous disodium hydrogen orthophosphate (Na₂HPO₄, Merck, Darmstadt, Germany) salts were added to the extract (0.1 mol L⁻¹) and stirred for 30 min. The CaP NPs were washed thrice with dH₂O and centrifuged (10 min, 2,683 × g). The washed CaP NPs were oven-dried overnight at 100 °C. The S. birrea-mediated α-Fe₂O₃ NPs were also prepared previously (Rama et al., 2025), whereby iron nitrate (Fe(NO₃)₃·9H₂O, Merck, Darmstadt, Germany) precursor salt was added to S. birrea shell extract (0.1 mol L⁻¹). The mixture was adjusted to pH 10–11 and was stirred continuously for 30 min. The α-Fe₂O₃ NPs were washed thrice with dH₂O and centrifuged (10 min, 2,683 × g). Thereafter, α-Fe₂O₃ NPs were oven-dried overnight at 100 °C and annealed at 500 °C for 2 h. The characterization of the NPs was conducted previously (Rama et al., 2025). The characteristics of the NPs used in this study is summarized in Supplementary Table 1. Based on the elemental composition determined using energy-dispersive X-ray analysis, pure CaP and α-Fe₂O₃ NPs had formed and were capped with a negligible amount of carbon originating from the S. birrea extract (Rama et al., 2025).

A glycerol stock (1 mL) of S. marcescens strain 39_H1 obtained from a previous AD study (Obi et al., 2020), was added to 39 mL of nutrient broth (NB; Merck, Darmstadt, Germany) in a 100 mL borosilicate bottle, aseptically, and was cultured at 150 rpm under aerobic conditions using a low-temperature shaking incubator (IncoShake, Labotec, Johannesburg, South Africa). This was conducted initially at 30 °C (24 h), thereafter at 25 °C (24 h), 20 °C (48 h) and 15 °C (72 h) by subculturing 1 mL of each preceding culture into 49 mL of sterile NB. The resulting psychrotolerant S. marcescens (SM) that was cultured at 15 °C was further sub-cultured on sterile nutrient agar (NA) plates (Merck, Darmstadt, Germany) for 48 h to obtain isolated colonies of SM (Supplementary Figure 1). The concentration of colony forming units per milliliter (CFU mL⁻¹) of SM culture was determined using a standard concentration curve prepared as previously described (Campbell, 2010).

The FW, CM and cold-adapted inoculum were obtained and characterized in our previous work (Rama et al., 2024). Briefly, market-rejected FW (peppers, baby onions, beetroots, cucumbers, carrots, cabbage, broccoli and lettuce) obtained from Tshwane Market (Pretoria, South Africa) was homogenized using a blender, pasteurized at 70 °C for 1 h and stored at −20 °C until further use. Fresh cattle manure that was aseptically collected from the Agricultural Research Council—Animal Production (Pretoria, South Africa) was mixed and stored at −20 °C until further use. The cold-adapted inoculum was prepared by mixing 33.3 g CM and 16.7 g FW in a working volume of 500 mL in a high pressure resistant, airtight 1,180 mL borosilicate glass reactor closed with a polytetrafluoroethylene coated septum. The reactor was successively incubated at 30 °C (1 week), 25 °C (1 week), 20 °C (2 weeks) and 15 °C (10 weeks), with shaking at 100 rpm (Low temperature IncoShake Incubator, Labotec). Thereafter, the cold-adapted inoculum was stored at 4 °C prior to use on the same day for the batch culture trials. The inoculum was primarily composed of Methanosarcina, Methanobrevibacter, Methanobacterium, Macellibacteroides, Bifidobacterium, Clostridium sensu stricto, Bacteroides and Acinetobacter (Rama et al., 2024).

Batch cultures were prepared in triplicate in airtight 366 mL borosilicate bottles with PTFE coated septa and a working volume of 150 mL. The control batch cultures contained 10% (w/v) CM, FW and cold-adapted inoculum (2:1:1). The experimental batch cultures contained the same components as the control, however with the addition of SM, 50 mg L⁻¹ CaP NPs and/or 25 mg L⁻¹ α-Fe₂O₃ NPs, in different combinations. The compositions of the batch cultures are shown in Table 1. The batch cultures were conducted using an inoculum to substrate ratio (ISR) of 0.09 g VS inoculum g⁻¹ VS substrate. While this ratio is lower than recommended ratios for standardized tests (Holliger et al., 2016), stable and uninhibited digestion was observed throughout the experimental period. The concentrations of the NPs were selected based on the findings of previous research (Li et al., 2021; Akar et al., 2025), and were added to the batch cultures in powder form. Additionally, SM treated batch cultures contained 6% (v/v) SM (5 × 10⁹ CFU mL⁻¹) based on concentrations applied in a previous study (Zhang et al., 2018). Briefly, SM cultured at 15 °C in NB, as per Section 2.2, was diluted with sterile NB until the optical density at 600 nm corresponded to 5 × 10⁹ CFU mL⁻¹ using the standard concentration curve (Supplementary Figure 2). The final volume of the diluted culture was recorded as V_f. Then, the diluted culture was pelleted and rinsed thrice in sterile dH₂O (10 min, 2,683 × g). The cells were resuspended in sterile dH₂O, whereby the final volume was the same as V_f, and was used immediately for preparation of SM treated batch cultures. The final volume of SM in SM treated batch cultures was 4 mL. The batch cultures were run for 103 days at 15 °C with 100 rpm shaking speed in a low-temperature shaking incubator (IncoShake, Labotec, Johannesburg, South Africa).

The total solids (TS), volatile solids (VS), methane yield, pH, FOS/TAC (volatile fatty acids (VFAs) to total alkalinity) ratio, C/N (carbon/nitrogen) ratio as well as lipid, protein and carbohydrate content were determined according to methods described in our previous work (Rama et al., 2024). Headspace gas samples (2 mL) were collected from the high pressure-resistant batch bottles using gas-tight syringes and were analyzed for methane content by gas chromatography (GC) using an 8610C Gas Chromatograph (SRI Instruments, Torrance, CA, USA) equipped with a thermal conductivity detector and a HayeSep D packed column. Initially, gas samples were analyzed by GC once a week. Thereafter, samples were analyzed three times a week when methane was detected in the gas samples. The methane volume was calculated by multiplying the methane percentage obtained from the GC analysis by the headspace volume, and the methane yield was determined relative to the VS added as previously described (Nkuna et al., 2019). Methane volumes were estimated from the measured methane fraction and the known headspace volume under ambient pressure (1 atm) and standard temperature (15 °C). The pH, C/N, TS and VS (of TS) were 4.25, 17.11, 6.62% and 79.83% for FW; 8.11, 19.66, 16.11% and 80.99% for CM; and 7.05, 20.90, 3.19% and 90.70% for cold-adapted inoculum, respectively. The lipid, protein and carbohydrate content of CM and FW were 2.44%, 11.22% and 11.86%, and 1.08%, 13.95% and 7.71%, respectively (Rama et al., 2024). Analysis of variance (ANOVA) was conducted for the methane yields obtained using XLSTAT (standard) in Excel (Microsoft Inc., Paris, France), SAS9.4 for Windows (SAS Institute Inc., Cary, North Carolina, United States; SAS Institute, Inc., 2016). In addition, ANOVA with Tukey's post hoc test was conducted for the lag phases using R version 4.4.1 (R Core Team, 2021) in RStudio (RStudio Team, 2020).

The methane production potentials for all the treated batch cultures were evaluated by applying the following modified Gompertz model (Equation 1) to fit the experimental methane yields:

where Y is the methane yields (mL CH₄ g⁻¹ VS), t is the trial duration in days, P is the maximum methane production potential (mL CH₄ g⁻¹ VS), R is the maximum methane production rate (mL CH₄ g⁻¹ VS day⁻¹) and λ is the lag phase period in days (Rama et al., 2024).

Triplicate DNA extraction of the batch culture samples from before and after the trials was conducted using the ZymoBIOMICS DNA Miniprep Kit, according to manufacturer instructions (Zymo Research, Irvine, United States). Thereafter, 16S rRNA V4-V5 region amplification, amplicon sequencing and analyses were conducted as previously described (Rama et al., 2024). Briefly, DNA extracts were normalized to 2 ng μL⁻¹ and the replicate extracts were pooled. Then, PCR amplification of the 16S rRNA V4-V5 gene region using the 515F-Y and 915R primers (Fadeev et al., 2021), containing Illumina adapters, and OneTaq^® 2× Master Mix with Standard Buffer (New England Biolabs Inc., Ipswich, MA, USA) was conducted with the T100 Thermal Cycler (Bio-Rad Laboratories, Hercules, CA, USA). The PCR conditions were as follows: 94 °C for 30 s (initial denaturation), 94 °C for 30 s (denaturation); 55 °C for 30 s (annealing) and 68 °C for 1 min (extension) for 35 cycles; and 68 °C for 5 min (final extension). The PCR products were sent to the Agricultural Research Council – Biotechnology Platform (Onderstepoort, South Africa), where they were cleaned, indexed and sequenced using the Illumina MiSeq benchtop system (San Diego, CA, USA). The raw sequences were trimmed and assessed for quality using Trimmomatic (Bolger et al., 2014) and FastQC (Andrews, 2010), respectively. Thereafter, feature tables of amplicon sequence variants, taxonomic classification, and alpha and beta diversities of the samples were obtained using the QIIME2™ (Bolyen et al., 2019) and DADA2 (Callahan et al., 2016) pipelines, and the silva-138-99-nb-classifier. R (version 3.5.3; R Core Team, 2021), RStudio Team (2020), Tax4Fun2 (Wemheuer et al., 2020) and Kyoto Encyclopedia of Genes and Genomes (KEGG; Kanehisa and Goto, 2000; Kanehisa, 2019; Kanehisa et al., 2023) were used to further analyze the data and predict the functional capabilities of the microbial communities.

The pH and FOS/TAC ratios obtained for all treatments after the batch culture trials were 6.79–6.91 and 0.4, respectively, which demonstrates that stable PAD occurred with respect to alkalinity and acidity (Nkuna et al., 2021). The inoculum to substrate ratio (ISR) applied in this study was lower than standard recommendations (Holliger et al., 2016). Although this may affect the absolute methane yields, the stable PAD observed suggests that microbial activity was sufficient to support anaerobic conversion. Consequently, the reported methane potentials should be interpreted as indicative values, particularly suitable for relative comparison among treatments. Methane production during the treated and untreated PAD batch culture trials are shown in Figure 1. On average, methane production rapidly increased within the first 70 days of PAD, with the addition of only nanoparticles, compared to the control and SM treated batch cultures. The observed methane production results from the experimental trials that were evaluated using the modified Gompertz model were fitted with the predicted data (R² ≥ 0.98), as shown in Table 2. The lag phase results indicated that the nano-additives induced an augmentative effect on methane production by significantly reducing the lag phase compared to the control and SM treated batch cultures. The SM treated batch cultures had significantly longer lag phases compared to the control and treatment with nano-additives alone. Extended lag phase using bioaugmentation with SM may have occurred due to competition with the indigenous AD microbial community or insufficient inoculum size (Akindolire et al., 2022). Nevertheless, higher methane yields were produced by all treated batch cultures after 70 days in comparison to the control. The PAD microorganisms possibly adapted to the new environment and rapidly increased in activity after 70 days (Jákói et al., 2022), as reflected by the exponential increase in methane yield (Figure 1). Furthermore, a reduced lag phase was observed when SM was applied with the combination of both nano-additives compared to the other SM treated batch cultures. Moreover, after 70 days and until the end of the trial, the highest methane yields were produced from digestion with the addition of SM in combination with both nano-additives. These results suggest that the augmentative effect of SM with the nano-additives on methane production possibly occurred only after the establishment of SM during the PAD process.

The simulated methane production rates had improved in the nano-additive treated batch cultures and more so with the addition of SM in combination with the nano-additives, compared to the control. Moreover, maximum methane yields from the SM and/or nano-additive treated batch cultures were significantly higher compared to the control. The addition of SM with the respective nano-additives improved maximum methane yields compared to batch cultures treated with nano-additives alone (i.e. CSC>CC; CSF>CF; CSCF>CCF). The biomass of SM added was negligible compared to the total substrate, thus its effect was primarily metabolic. Batch cultures treated with nano-additives alone resulted in higher maximum methane yields compared to treatment with SM alone. Furthermore, the highest maximum methane yields were obtained from CSCF and CCF, rendering a 133.5% and 105.0% increase, compared to the control. These results suggest that the combined stimulating and conductive attributes of the nano-additives possibly contributed to the enhancement of methane production through synergistic interaction with SM and/or the indigenous PAD microbial communities. Zhu et al. (2022) reported a similar augmentative effect of iron oxide (magnetite), calcium peroxide and the combined use of iron oxide and calcium peroxide on mesophilic AD of food waste and sludge, whereby a 5.95, 15.3 and 26.8% increase in methane production was observed, respectively, compared to their control (Zhu et al., 2022).

The maximum methane yield results of the batch cultures in this study were comparable to results from previous batch PAD studies and some mesophilic AD studies conducted under non-augmented conditions (i.e., without the addition of nanoparticles, nutrients, or engineered microbes), however augmented mesophilic AD systems resulted in higher maximum methane yields (Table 3), as expected. Nonetheless, bioaugmentation with SM resulted in a methane yield of 124.6 ± 20.3 mL CH₄ g⁻¹ VS at 15 °C, which was comparable to and higher than previously reported methane yields obtained at 20 °C from bioaugmentation with mesophilic/psychrotolerant consortia (Table 3). The superior performance of SM may be attributed to its ability to produce cold-active enzymes involved in the fermentative stages, thus contributing to the availability of methanogenic precursors. Despite the enhanced maximum methane yields from augmentation with the additives, the lag phase period in this study (35–56 days) was longer compared to other PAD studies that have reported shorter lag phases (0.51–16 days) (Martí-Herrero et al., 2022; Xu et al., 2023). In the present study, the batch bottles were not flushed with nitrogen gas initially and a low ISR was applied, which may have contributed to the overall prolonged lag phase. Moreover, this study utilized a cold-adapted mesophilic inoculum to start-up the batch culture. To reduce the lag phase by improving the affinity between the substrate and inoculum, it is recommended to further investigate the application of a stabilized psychrophilic inoculum (Martí-Herrero et al., 2022) and an ISR between 1 and 4 (Holliger et al., 2016; Koch et al., 2020). Overall, the results indicate that the combined application of the nano-additives, with or without SM, has the potential to enhance methane production at low temperatures (15 °C).

The bacterial and archaeal community composition, before and after PAD of CM and FW that was untreated and treated with various combinations of SM and/or nano-additives at 15 °C, is shown in Figure 2. The bacterial and archaeal community composition was similar between all samples, including the control, before PAD (day 0), as expected. However, a major shift occurred in the community composition across all samples after PAD (day 103). This shift resulted from the depletion of oxygen during PAD, which eliminates or diminishes aerobic microorganisms and promotes growth of anaerobic microorganisms that are able to thrive with little to no oxygen (Zakem et al., 2020). The dominant bacterial genera present before PAD were Carnobacterium (6.5–33.9%), Bifidobacterium (9.5–16.3%), Trichococcus (8.5–23.7%) and Bacteroides (4.7–7.6%; Figure 2A). These bacteria are typically anaerobic and fermentative (Leisner et al., 2007; Strepis et al., 2020; Kelly et al., 2021), however their diminished presence following PAD suggests that these bacteria were either outcompeted by other dominant bacterial genera or unable to adapt to the low temperatures.

Acinetobacter (21.2–30.9%), Bacteroides (33.7–41.7%), Macellibacteroides (4.2–6.6%), Clostridium sensu stricto (2.5–5.8%) and Christensenellaceae_R-7_group (1.4–3.6%) were the dominant bacterial genera after PAD. These bacterial genera play a role in fermentation and acetogenesis (Akindolire et al., 2022; Wei et al., 2022). The bacterial genera that were present in all the samples before and after PAD were Bacteroides and Christensenellaceae_R-7_group. This demonstrates their ability to persist and adapt to changing environments (i.e., in terms of low temperature, oxygen depletion as well as in the presence of SM and/or nano-additives). Besides Acinetobacter, the observed dominant and persistent bacterial genera can produce various volatile/short chain fatty acids such as acetate, butyrate, propionate, succinate and/or lactate through fermentation and acetogenesis (Nishiyama et al., 2009; Ueki et al., 2011; Jabari et al., 2012; Wei et al., 2022). On the other hand, Acinetobacter possibly played a role in fermentation and acetogenesis by utilizing acetate and various other carbon sources for growth (Kim et al., 1997). Furthermore, Bacteroides and Acinetobacter are electroactive bacteria that are capable of enhancing methane production via DIET (Harirchi et al., 2022; Zheng et al., 2023). Other PAD studies have also reported the increased relative abundance of Bacteroides, Clostridium and Acinetobacter, which suggests that these bacteria play an important role in methane production under psychrophilic conditions (Xu et al., 2023; Yan et al., 2023; Zheng et al., 2023).

The archaeal community was primarily composed of Methanobrevibacter (89.0-92.5%), Methanosphaera (3.9–6.2%) and Methanosarcina (1.2–3.2%) before PAD across all samples, however by the end of the trial all samples were dominated by Methanosarcina (46.4–67.0%), thereafter Methanobrevibacter (18.3–36.9%) and Methanobacterium (5.3–27.6%; Figure 2B). Methanobrevibacter and Methanobacterium utilize hydrogen (H₂) and carbon dioxide (CO₂) as electron acceptors during hydrogenotrophic methanogenesis, while electrotrophic Methanosarcina can utilize H₂/CO₂, acetate, methanol and/or methylamines during hydrogenotrophic, acetoclastic and methylotrophic methanogenesis, respectively (Akindolire et al., 2022). Previous reports have indicated that the acetoclastic pathway for methanogenesis is more thermodynamically favorable under psychrophilic conditions (Tiwari et al., 2021; Akindolire et al., 2022; Yan et al., 2023). Hence, the increased presence of Methanosarcina possibly stimulated the acetoclastic methanogenesis pathway under psychrophilic conditions.

A visible difference between the samples after PAD, based on the taxonomic classification, includes the dominant presence of Serratia in the samples that were initially inoculated with SM. These results indicate that by the end of the PAD trial, SM had established itself within the PAD microbial community without being outcompeted by other microorganisms. A negligible difference (0.18%) was observed between CSM and CSCF after the PAD trial in terms of the relative abundance of Serratia. The results suggest that both nano-additives improved the efficiency of SM and possibly other microorganisms despite the relative abundance of SM during PAD. Other studies that were conducted under mesophilic and thermophilic temperatures have reported similar findings, whereby the combination of bioaugmentation and nano-additives enhanced methane production compared to bioaugmentation alone (Xiao et al., 2019; Laikova et al., 2023). These results further support the proposition that the establishment of SM within the PAD microbiome was crucial for its augmentative effect with the nano-additives to occur on methane production. Moreover, significant positive correlations were observed between Serratia, Acinetobacter, Bacteroides, Clostridium, Dysgonomonas, Methanobacterium, Methanosarcina and the maximum methane potentials from this study (Supplementary Figure 3). This key finding suggests that syntrophic relationships possibly existed between these diverse microbial groups, thus contributing to methane production.

A distinct change in microbial community diversity was observed before and after PAD. This can be attributed to the transition from aerobic to anaerobic conditions (Rama et al., 2024). The NMDS plot of the microbial community beta diversity, based on Bray-Curtis metrics, indicates that the microbial communities before PAD clustered together, as expected (Figure 3A). Interestingly, after PAD, the microbial communities remained clustered together following treatment with the nano-additives alone (Figure 3B), while treatment with SM and nano-additives significantly altered the core diversity of the microbial communities, despite the augmentative effect of the treatments on methane production. Low doses of calcium (20 mg L⁻¹) were found to enhance methane production from digestion of long-chain fatty acids with little impact on microbial community diversity compared to high doses (2,500 mg L⁻¹; Liu et al., 2021). A previous study reported that calcium phosphate can influence the microbiome and promote production of VFAs by members of the Firmicutes and Bacteroides bacterial taxa (Fuhren et al., 2021). The findings of those studies support the augmentative effect of the CaP NPs during PAD observed in this study and its minimal impact on diversity. Other contrasting studies have reported a shift in microbial community from augmentation of mesophilic anaerobic digesters with α-Fe₂O₃ NPs and/or calcium peroxide nanoparticles as well as bioaugmentation with microbial consortia (Wang et al., 2016; Xu et al., 2022; Zhu et al., 2022). A possible syntrophic relationship may have been established via DIET between some of the fermentative/acetogenic bacteria and methanogens with the aid of the α-Fe₂O₃ NPs. Previous studies have reported on similar findings whereby α-Fe₂O₃ NPs promoted methane production via DIET between syntrophic microorganisms at mesophilic temperatures (Wang et al., 2016; Aguilar-Moreno et al., 2020; Rana et al., 2020; Zhu et al., 2022). As such, the CaP NPs and α-Fe₂O₃ NPs may have provided a stimulating benefit to the microorganisms during enhanced PAD with minimal effect on the microbial community compared to the control. In contrast, the addition of SM (with or without the nano-additives) resulted in its establishment in the microbial community, thus altering the community structure and diversity. Nonetheless, enhanced methane production was observed despite the altered microbial diversity due to the presence of SM.

The alpha diversity indices of the microbial communities before and after batch PAD are shown in Figure 3C. The species richness (Chao1), diversity (Shannon) and evenness (Simpson) of the microbial communities mostly decreased after PAD, most likely due to the decrease in temperature and anaerobic conditions, which provides a niche environment for certain microorganisms (Zakem et al., 2020). Although treatment with SM and CaP NPs or α-Fe₂O₃ NPs enhanced species richness after PAD in the microbial communities of CSC and CSF, treatment with all three additives (CSCF), which gave the highest methane production potential, resulted in the lowest microbial diversity after PAD. These results suggest that a stable and co-operative microbial community with lower diversity was prevalent during enhanced methane production with the combined treatment.

In SM treated batch cultures, the relative gene abundance for predicted functions in fermentation were enriched compared to the other treatments (Figure 4). In another study, SM displayed hydrolytic and acidogenic capabilities during mesophilic AD (Obi et al., 2020). This suggests that SM likely played an important role in the initial stages of PAD. Moreover, high relative gene abundances for pyruvate decarboxylase (EC:4.1.1.1), alcohol dehydrogenase (EC:1.1.5.5) and aldehyde dehydrogenase (EC:1.2.1.3) were detected in SM treated batch cultures. These enzymes are responsible for the conversion of pyruvate and ethanol to acetaldehyde, and acetaldehyde to acetate, respectively (Liu et al., 2025). Furthermore, batch cultures treated with the individual and combined nano-additives alone had enriched predicted functional capabilities associated with propionate degradation. The results suggest that the additives may have contributed to the acetogenesis stage, hence it is recommended that future work validate this potential role of the additives through co-culture studies with cellulose, propionate or ethanol as substrates. The enhanced methane potentials from the treated batch cultures during methane production could possibly be attributed to its role in alleviating the rate-limiting stages, which are often the hydrolysis stages in mesophilic AD and VFA accumulation stages of PAD (Obi et al., 2020; Zheng et al., 2023).

Through the fermentation and acetogenesis stages, organic substrates are converted to acetate and acetyl-CoA (Zheng et al., 2023). All microbial communities from the treated and untreated batch cultures had higher relative abundance of genes associated with the homoacetogenesis and acetoclastic methanogenesis pathways compared to the hydrogenotrophic and methylotrophic pathways. These results support the proposition that acetoclastic methanogenesis was the dominant pathway leading to methane production during PAD. However, the SM treated batch cultures had relatively lower predicted functional capabilities in methanogenesis despite their higher methane yields compared to the batch cultures treated with nano-additives alone. Some strains of S. marcescens are capable of producing a red pigment called prodigiosin, which was previously found to form conductive films that enhanced electron flow in bioelectrical systems (Zani et al., 2024). In this study, only microbial communities from SM treated batch cultures had a high relative gene abundance for prodigiosin synthetase. Growth of SM on an NA plate confirmed that it was a pigmented strain (Supplementary Figure 1). These results suggest that SM and prodigiosin possibly played a role in stimulating EET during PAD, which may explain the higher methane yields observed from the SM treated batch cultures. However, further investigation of the individual effect of prodigiosin on methane yields is recommended to provide insight into its role in PAD.

In a previous study, coal slime containing conductive materials increased type IV pilus protein PilA gene abundance, and enhanced methane yields from mesophilic AD of chicken manure (Zhao et al., 2024). Besides their roles in microbial adhesion, biofilm formation and motility, the type IV pilus assembly protein PilA and its associated monomers can form electrically conductive pili (e-pili) that play an important role in long-distance EET (Lovley and Holmes, 2020). In this study, treatment with the nano-additives alone, especially in combination, predominantly increased the relative gene abundance of type IV pilus assembly proteins compared to the control and SM treated batch culture communities. Moreover, the nano-additives used in this study had elevated conductivity (Supplementary Table 1) compared to previously reported conductivity of e-pili (2-20 μS cm⁻¹; Kumar et al., 2021). These results suggest that the nano-additives augmented methane yields possibly due to DIET interactions between syntrophic, electron-donating bacteria (Acinetobacter, Clostridium and Sedimentibacter) and electron-accepting methanogens (Methanosarcina) by acting as electron shuttles (Wang et al., 2023). Taken together, these findings support the superior performance observed from the augmented batch cultures, which were possibly linked to enhancement of key AD functions and DIET interactions. However, it should be noted that predicted functional analysis provides indirect evidence for DIET (Khalid et al., 2025). Therefore, co-culture and mixed microbial community studies incorporating methods such as metatranscriptomics, metaproteomics, isotope tracing, electrochemical techniques or cellular characterization via electron microscopy are recommended to validate the direct role of the additives in DIET during PAD.