Fungal-Based Biogenic Silver Nanoparticles: Synthesis, Mathematical Modeling and Application

Abstract

Biogenic synthesis based on microorganisms and plants utilizes biomolecules and metabolites that act as reducing and stabilizing agents. Furthermore, these biomolecules confer enhanced properties, increasing their effectiveness as antibacterial agents, even against multidrug-resistant bacteria. However, there is currently a lack of knowledge regarding the characteristics of the biomolecules involved and their mechanisms during synthesis, which limits their wider application. This research study focused on the synthesis of AgNPs using extracellularly secreted biomolecules from the filamentous fungus R. stolonifer. The results indicated that the concentration of the biomolecules depends on the response of the fungus to specific operational conditions. Furthermore, a relationship was found between the concentrations of total proteins and polyphenols, an optimal protein-to-polyphenol ratio, and the formation of AgNPs. Characterization of the fungus extracts revealed a complex mixture of over 100 metabolites, primarily phenolic compounds, proteins, and methoxylated compounds, thereby identifying a synergistic mechanism that enables the reduction and stabilization of AgNPs. Disinfection tests confirmed the antibacterial efficacy of all biosynthesized AgNPs, achieving 100% elimination of Escherichia coli and Staphylococcus aureus. The study outcomes highlight the important role of fungal biomolecules for creating a sustainable and efficient method for synthesizing antimicrobial nanoparticles with high efficacy for disinfectant applications.

1. Introduction

In recent years, the use of nanoparticles (NPs) in water treatment has emerged as an attractive alternative due to their physical and chemical properties, particularly those with antibacterial, antifungal, and antiviral capabilities [1,2,3], such as silver nanoparticles (AgNPs), which have demonstrated potent antimicrobial properties, including superior efficacy against pathogenic bacterial strains such as Escherichia coli and Staphylococcus aureus compared to other metal-based NPs [4,5,6,7]. Their use as a disinfectant is emerging as a promising alternative, offering a more sustainable, efficient, and environmentally friendly approach, particularly when AgNPs are synthesized using biogenic methods that employ microbially derived and plant-derived byproducts as reducing agents and stabilizers [4,8,9,10,11]. Biogenic synthesis based on unicellular and multicellular microorganisms (e.g., bacteria, fungi, yeasts, algae) from diverse environments, including soil, water, and plants [3,4,8], offers clear advantages due to the non-toxic nature and the enhanced properties conferred on the synthesized nanoparticles, such as biocompatibility and increased antibacterial and antifungal activity [1,2], including high efficacy against multidrug-resistant pathogenic bacteria [12,13]. The synthesis process using microorganisms involves the use of biomolecules and metabolites produced and secreted under specific conditions applied during their cultivation [10,14,15]. Common inducers rely on nutrient availability, temperature variation, or pH changes to stimulate the secretion of specific biomolecules [16]. The biomolecules available for NP formation depend on the inducer and the mechanism used: intracellular, where ions are reduced within microbial cells by enzymes, proteins, and other intracellular and extracellular biomolecules acting as mediators, or extracellular, which relies exclusively on metabolites secreted by the microorganism in the surrounding medium [1,9,17]. Extracellular pathways are often preferred due to their shorter processing times and simpler methodologies compared to intracellular routes, which require additional steps to extract nanoparticles from the cells [1,9]. Despite the advantages of microorganism-based synthesis, significant knowledge gaps remain regarding extraction methods for obtaining specific biomolecules, as these strongly influence nanoparticle physicochemical properties and functionality. Also, understanding the biomolecular species involved in NP is particularly important in microbial systems, where biological variability can affect reproducibility. Further, although biogenic synthesis offers environmental and economic advantages and opportunities for waste valorization, challenges remain regarding scalability, stability, biocompatibility, ecological impacts, and production costs. Further, integrating life-cycle assessment (LCA), computational modeling, and comprehensive management strategies is essential for establishing biogenic nanotechnology as a safe, sustainable, and scalable alternative [18,19]. However, these aspects are outside the scope of the current study and have not been investigated. This study explores the biogenic synthesis of AgNPs using extracts from the filamentous fungus Rhizopus stolonifer, evaluating different conditions and systematically characterizing the fungus-derived biomolecules present in a diverse range of fungal extracts, in addition to assessing the antibacterial capability of the biosynthesized AgNPs. The study outcomes are expected to provide knowledge to enhance the synthesis of more effective and sustainable disinfection products for water treatment, as the systematic understanding of the influence of extraction conditions and biomolecular composition can support the future optimization and scale-up of biogenic AgNP production.

2. Materials and Methods

2.1. Reagents

The reagents used in this study, including Potato dextrose agar (PDA, P2182 Millipore, Millipore, Burlington, MA, USA) solid medium, silver nitrate (AgNO₃, Merck, Naucalpan de Juárez, Mexico), Bradford solution (Quick Start, Bio-Rad, Hercules, CA, USA), Folin’s reagent (2790 HYCEL, Zapopan, Mexico), sodium carbonate (Na₂CO₃, 3598 JT BAKER, Aguascalientes, Mexico), bovine serum albumin (BSA, A8806 Sigma, Sigma, Burbank, CA, USA), and gallic acid (Merck, Naucalpan de Juárez, Mexico), were used as received.

2.2. Fungus Extract Preparation

A fungus isolated from Artocarpus heterophyllus (Jackfruit), identified as Rhizopus stolonifer, was used to study biomass production kinetics using the dry weight method, in which fungal samples are collected on filter paper at different growth stages and dried at 100 °C for 24 h. The radial growth kinetics were monitored by measuring the increase in diameter every 24 h until the fungus reached the maximum diameter of the Petri dish. Both kinetics were performed using a circular pellet of known diameter and weight cut from a previous fungal culture using potato dextrose agar (PDA) as the solid medium and an incubation temperature of 28 ± 1 °C. The extract was prepared by mixing the biomass with sterile distilled water and keeping it at room temperature (28 ± 1 °C) with constant stirring at 120 rpm. After filtration through a 0.45 µm Millipore membrane, the supernatant was retained and stored at 4 °C.

2.3. Biogenic Synthesis of Silver Nanoparticles (AgNPs)

Silver nitrate (AgNO₃) was added to the fungus extract to reach a final concentration of 1 mM and stirred at 120 rpm at 28 ± 1 °C for up to 24 h or until a visible orange color change occurred in a Thermo Scientific stirring plate with stirring and heating control (Mexico City, Mexico). The color change observed during the synthesis of silver nanoparticles (AgNPs) is a preliminary indicator of their formation. This phenomenon is because AgNPs present a coloration different from that of the metal on its macroscopic scale, resulting in the color change in the solution and the presence of surface plasmon resonance (SPR) characteristic of the formation of AgNPs. The solution was then stored at 4 °C two weeks before being completely used or characterized.

2.4. Characterization of the Fungus Extracts

The fungus extracts were characterized by Fourier Transform Infrared Spectroscopy (FT-IR) using a Thermo Scientific NICOLET IS10 instrument and by absorption scanning (200 to 700 nm) using a Thermo Scientific, evolution 300 UV-vis spectrophotometer (Thermo Scientific, Waltham, MA, USA). Colorimetric techniques were employed to quantify total proteins and polyphenols using methods adapted from Bradford [20,21] and Folin–Ciocalteu [22,23], respectively.

For the modified Bradford method, 10 µL of the sample was mixed with 200 µL of Bradford solution (i.e., 1:4 volume/volume rate). For the modified Folin–Ciocalteu method, 3 µL of the sample was mixed with 237 µL of distilled water, 15 µL of 10% Folin’s reagent, and 45 µL of 15% sodium carbonate (Na₂CO₃). A 96-well microplate was used for both methods. The reactions were allowed to proceed for five minutes in the dark, and the absorbance was measured using a UV-Vis spectrophotometer (Thermo Scientific, Waltham, MA, USA) at 595 nm for protein quantification and 760 nm for polyphenol quantification. Calibration curves for the protein and phenolic compounds were generated using bovine serum albumin (BSA) and gallic acid, respectively.

2.5. Screening of Fungus Extract by GC-MS Analysis

To profile the active components, the fungus extract was evaporated to dryness at 40 °C using a rotary vacuum evaporator under a nitrogen atmosphere. The resulting crude residue was resuspended in 1 mL of methanol and filtered through a 0.22 µm membrane. To ensure complete removal of water, the solution was dried again with anhydrous sodium sulfate.

A 2 µL sample was injected into an Agilent 7890 GC coupled to a 5975C MSD (Agilent Technologies, Santa Clara, CA, USA) with a 20:1 split ratio. Separation was performed using an HP-5ms Ultra Inert column (30 m × 0.25 mm, 0.25 µm; Agilent J&W). The flow rate of the helium carrier was maintained at 1.2 mL min⁻¹. The column oven program was set at 35 °C, then the temperature was increased to 325 °C (5 °C min⁻¹) and maintained till the end of the chromatographic run (60 min). The transfer line and ion source temperature were maintained at 300 °C and 230 °C, respectively. Electron ionization (EI) was performed at 70 eV. Chromatograms were acquired in full-scan mode over the m/z range of 29–550 using MassHunter Workstation Software (Software Version B.07.00/Build 7.0.457.0.). For identification, raw data were exported and processed using Agilent Unknowns analysis software (The NIST Mass Spectral Search Program for de NIST/EPA/NIH Mass Spectral Library Version 2.0 Buil May 19 2011), and compounds were identified by comparing mass spectra and retention times against the National Institute of Standards and Technology (NIST version 2.0) standard library database.

2.6. Characterization of Biogenically Synthesized AgNPs

The extracts were characterized before and after the addition of AgNO₃. After confirming the formation of AgNPs, 3 mL of each extract was lyophilized to obtain a fine powder. The powder was then characterized using an FT-IR and ultraviolet-visible (UV-Vis) spectrophotometer. FT-IT spectra were acquired using the total reflection attenuation technique (TAR) with 16 scans and a resolution of 2 cm⁻¹.

For AgNP production, the color change in the solution was evaluated along with a UV-Vis spectrophotometry reading with an absorption sweep from 200 to 700 nm, considering their respective characteristic SPR (around 400 nm to confirm the formation of AgNPs) [2,14,24,25,26].

The elemental composition of the AgNPs was determined by electron microscopy on a JSM-7610F microanalysis JEOL instrument operating at 2 Kv (JEOL Ltd., Tokyo, Japan), coupled with energy-dispersive X-ray spectroscopy (EDS). Morphology and particle size were analyzed by transmission electron microscopy (TEM, Hitachi 7700, Tokyo, Japan) operated at 100 kV. The samples were dispersed in water and applied to a lacey formvar-carbon supported copper grid (200 mesh size), and images were captured at 100, 50, and 20 nm scales.

2.7. Statistical Analysis

The relationships between the variables were initially explored via scatterplots, and pairwise correlations were evaluated. Gaussian process models were then constructed to predict outcomes $y\left(\mathit{x}\right)$ based on a $p$-dimensional vector of explanatory variables $\mathit{x}\in \mathrm{X}$. The model takes the following form (Equation (1)):

$${\left(y\left({\mathit{x}}_1\right),\dots ,y\left({\mathit{x}}_n\right)\right)}^T|\mathit{\mu },{\sigma }^2,\mathit{R}~MVN({\left(\mu \left({\mathit{x}}_1\right),\dots ,\mu \left({\mathit{x}}_n\right)\right)}^T,{\sigma }^2\mathit{R}),$$ (1)

where $MVN$ denotes the multivariate normal distribution, $\mu \left(.\right)$ represents the mean function, ${\sigma }^2$ is an unknown variance parameter, and $\mathit{R}$ is a correlation matrix. The scenarios considered were:

• (1)Predicting “poly” (polyphenols), “proteins” (proteins), and “rate” (proteins/polyphenols) based on “day” (fungus age) and “hours” (hours of stress exposure).
• (2)Predicting “wave” (wavelength), “area” (the area under the curve), and “absorb” (absorbance) based on “poly”, “proteins”, and “rate”.
• (3)As in scenario (2), with additional explanatory variables “day” and “hours”.

Predictive performance was assessed in all cases by comparing the model predictions with the observed data. This was done by considering all available training data and using leave-one-out cross-validation. For numerical purposes, all variables were scaled between 0 and 1.

2.8. Assessing the Antibacterial Activity of AgNPs

The antimicrobial activity of the synthesized AgNPs was evaluated using the microdilution technique [27], with E. coli ATCC 8739 (Gram-negative) and S. aureus ATCC 6538 (Gram-positive) as model bacteria. Serial dilutions of the AgNPs were prepared (dilution factors of 2, 4, 8, 16, 32, 64, 128, 256, and 512) and added to a microplate. A bacterial inoculum, adjusted to a concentration of 0.5 on the McFarland scale (1.5 × 10⁸ bacterial cells mL⁻¹), was added to each well from columns 1 to 11 of the microplate, with column 11 serving as the negative control (well with microbial growth) and column 12 as the positive control (well without microbial growth). The microplates were then incubated at 24 °C for 24 h.

As the McFarland scale relates turbidity to bacterial cell concentration, the antimicrobial activity was evaluated based on the difference in absorbance of each sample and its dilutions against the absorbance of the negative control after 24 h of incubation, assuming that an absorbance value of zero or lower than the value of the negative control represents the absence or low microbial cell growth. Finally, the Minimum Inhibitory Concentration (MIC) was defined as the highest dilution of AgNPs that inhibits bacterial growth. This was identified as the well with the lowest absorbance relative to the controls, following the M27-A3 protocol of the Clinical and Laboratory Standards Institute [28].

3. Results and Discussion

3.1. Growth Kinetics and Biomass Production of the Fungus R. stolonifer

Figure S1 in the Supporting Information shows the biomass production kinetics of the fungus R. stolonifer grown on PDA. The data shows non-linear behavior over time, with the maximum biomass recorded on day 3 at 0.180 g. Biomass ranged between 0.10 and 0.17 g, with no significant increase after day 3. This plateau in biomass production is attributed to the finite surface area and limited nutrients available on the Petri dish with PDA, leading to a maximum growth and biomass production point between days 2 and 3 [29].

The biomass obtained after day 3 corresponded with the appearance of sporangia, which is linked to the complete formation of their structure. Sporangia are the spore-bearing parts of the fungus, and their generation marks the transition to the exponential growth phase. The maximum biomass generation observed on day 3 was also related to rapid mycelial expansion and growth [30,31,32]. Consequently, it was determined that after day 3, spore production begins, signaling the start of a new growth phase.

Figure S1 also shows that the biomass production values exhibit larger error bars on some days. This behavior is primarily attributed to the individual responses of the fungus to incubation conditions, media, and growth phase [32,33]. The exponential phase of a filamentous fungus typically lasts from day 3 to day 5, entering the stationary phase during days 6 to 11. During the stationary phase, a fine black powder was observed on the surface, corresponding to the maturity or saturation of the sporangia. The sporangia usually break open, releasing spores with the black powder [29]. For the preparation of the extracts, the fungus was harvested on day 3, corresponding to the beginning of the exponential growth phase, when both structure and morphology are nearly complete. Additionally, day 11 was selected to mark the end of the stationary phase, when the sporangia are fully saturated with spores. These two stages enabled the evaluation of the effects of fungal maturity and spore influence on AgNP production and on the response of the extract to the stress conditions involved in the preparation.

3.2. Characterization of Fungus Extract

3.2.1. Identification of Polyphenols in Fungus Extracts

Figure S2a in the Supporting Information shows the UV spectra of the fungus extracts after 3 days of growth. A signal around 260 nm increases in intensity from 2 to 48 h. Two additional signals are observed from 72 h onwards, increasing in intensity until 96 h. The single signal at approximately 260 nm is attributed to simple phenolic compounds, which typically present a single peak between 220 and 280 nm [34]. These compounds contain π-conjugated systems with hydroxy phenolic groups. After 72 h, the signal changes, suggesting that the phenolic groups are undergoing modifications, resulting in compounds derived from the simple phenolic structures.

Similar to the extracts after 3 days of growth (Figure S2a), Figure S2b shows that all spectra of the fungus extract after 11 days of growth exhibit a unique signal around 260 nm, which is attributed to simple phenolic compounds. According to Geană et al. [34], these compounds show a single peak between 220 and 280 nm due to π-conjugated systems with hydroxy phenolic groups. However, unlike the extracts from 3 days of growth, the signal in the 11-day extracts remains constant over time, only increasing in intensity. This suggests that the simple phenolic groups in the 11-day extracts are more stable [35].

Figure 1 show the results of quantifying protein and polyphenol concentrations in the fungus extracts from 3 days of growth (E3) and 11 days of growth (E11). For the reported polyphenol concentrations of different extracts (Figure 1a), the error bars are as large as the average values, sometimes even overlapping, indicating no significant differences between the concentrations obtained at different stress times. This variability between replicates is attributed to the individual responses of the fungus to the stress conditions. Polyphenols are generally produced in filamentous fungi as a response to environmental stress [35,36]. Furthermore, polyphenols provide adaptive advantages, helping fungi to adjust to specific environmental conditions and offering protection [37,38,39,40]. Based on the results obtained, it is hypothesized that the duration of stress does not directly determine the polyphenol concentration produced. The higher polyphenol concentration in extracts from E3 (3 days of growth) compared to E11 (11 days of growth) suggests that the fungus is more affected by stress in its early stages. In contrast, the lower polyphenol concentration in the E11 extract indicates that the fungus has adapted better to stress over time. Thus, the age of R. stolonifer plays a role in determining the polyphenol concentrations produced.

Figure 1.

(a) Polyphenol concentration in extracts of the R. stolonifer fungus with 3 days-growth (E3) and 11 days-growth (E11); (b) polyphenol production in the fungus extract with 3 days of growth (E3) and 11 days of growth (E11) with respect to time; (c) protein concentration in the extracts of R. stolonifer fungus with 3 days-grown (E3) and 11 days-grown (E11); and (d) protein production in the fungus extract with 3 days of growth (E3) and 11 days of growth (E11) with respect to time. Note: E3: green; E11: blue.

Figure 1b show the polyphenol concentrations produced, which were monitored at different time intervals (from 2 to 96 h), where it is evident that the production by the E3 fungus extracts is higher than that by E11 extracts. This trend can also be observed in the previous individual responses (Figure 1a), confirming that there is greater stress in young fungi. Furthermore, under both conditions, maximum production occurs between 24 and 48 h, suggesting that production decreases due to resource depletion and increased oxidative stress, which increases as time increases [38].

Statistical analysis of the reported results showed that, in the first case (Figure 1a), E3 shows a potential difference in average production (p = 0.054), while E11 exhibits no significant variation (p = 0.1454). In the second case (Figure 1b), E3 demonstrates a significant difference over time (p = 4.62e⁻⁵) compared to a less pronounced change in E11 (p = 0.093). Variance analysis of both conditions revealed more significant variability in E3 (p = 3.552e⁻⁷ and p = 0.00066), underscoring the dynamic stress response of younger fungi versus the stability of older fungi.

3.2.2. Identification of Proteins in Fungus Extracts

Figure S3 presents the FT-IR spectra of the freeze-dried samples of R. stolonifer fungus extracts, where 9 distinct signals were identified across all spectra. Around 3280 cm⁻¹ (line a in the figure), a broadband can be observed, which can be attributed to amine N-H bonds or peptide bonds. Lines b, c, and d, ranging from 2960 to 2850 cm⁻¹, show several signals typically associated with asymmetric and symmetric C-H bond vibrations in methyl and methylene groups. The line e at approximately 1580 cm⁻¹ corresponds to the symmetric deformation vibrations of the carboxylate groups (COO⁻). However, after 24, 48, and 72 h, this signal disappears, and two signals appear at 1636 and 1541 cm⁻¹, corresponding to amide I and amide II, which are primarily due to the C–O and C–N stretching vibrations of the peptide’s main chain; this may be due to a rearrangement of the chains [41,42].

The f line, around 1400 cm⁻¹, corresponds to the deformation vibrations of the C-H bonds in the methyl and methylene groups, as well as to the vibrations of the C=O bonds in the asymmetric carbonyl and carboxylate groups, which do not show significant differences in most of the extracts. The g line, located around 1250 cm⁻¹, is attributed to the stretching of C-O bonds in phosphates or esters and exhibits greater intensity at 48 and 72 h. This may be due to the stress to which the fungus is subjected, causing it to begin producing a greater amount of these compounds during this time.

Line h, around 1070 cm⁻¹, is typically linked to both C-O and C-C bond stretching and C-N bond vibrations [43,44] (which can be confirmed due to the sp3 hybridization of these bonds). Finally, line i, located around 650 cm⁻¹, is related to out-of-plane vibrations of N-H and C=O bonds. The collective presence of these signals, particularly those indicated by lines a, b, c, e, g, h, and i, suggests the presence of functional groups common in proteins, fatty acids, and lipids [45]. Therefore, it can be inferred that the extracts contain substantial concentrations of these biomolecules.

The fungus R. stolonifer, like many fungi, produces stress-response proteins that aid cells in surviving under adverse conditions. For example, these proteins neutralize free radicals and prevent oxidative damage [46]. Heat shock proteins (HSPs) are also generated to protect other proteins from denaturation and assist in proper protein folding under heat stress conditions [47]. Moreover, proteins involved in producing secondary metabolites may have defensive or signaling functions [37]. These findings suggest that protein production varies significantly depending on the duration or exposure to stress.

Figure 1c illustrates the protein concentration in the different extracts from E3 and E11 (2, 24, 72, and 96 h), showing no significant differences between these time points, as was the case in the results for polyphenols. This suggests that stress exposure duration may influence protein and polyphenol production in R. stolonifer, as the stress response is highly variable and depends on both the specific stress condition and the fungal maturity stage [35]. For example, protein production in E11 is generally lower than in E3 at most stress time points, indicating that E3 exhibits a higher protein production response to stress with greater variability than E11. This difference is attributed to the distinct growth and maturity stages of the fungus. More mature stages, such as E11, tend to respond more consistently to stress, while less mature stages, such as E3, show greater variability in their metabolic responses, reflecting higher vulnerability to stress [29,30].

When monitoring protein production over time for extracts E3 and E11 (Figure 1d), both conditions exhibit similar trends over time, with a peak in production between 48 and 72 h. Regarding polyphenol and protein production under stress conditions caused by nutrient scarcity and constant agitation, filamentous fungi adjust their metabolic processes to optimize survival [35]. Proteins, essential for critical cellular functions such as repair and maintenance of cell structure, tend to remain stable as the fungus prioritizes their production to cope with stress [36]. In contrast, polyphenols, which serve as antioxidants and provide protection against oxidative damage, may be reduced. Under these stress conditions, polyphenol synthesis is diverted as a secondary metabolic pathway, redirecting resources towards protein production and maintenance [30,39]. This adjustment enables the fungus to concentrate its limited resources on the essential components required for short-term adaptation and survival.

Regarding our statistical analysis of protein quantification for the first case (Figure 1c), E3 exhibits significant differences in average production over time (p = 0.0025), indicating dynamic fluctuations, while E11 shows less pronounced changes (p = 0.019). In the second condition (Figure 1d), neither E3 (p = 0.10) nor E11 (p = 0.3382) shows significant temporal differences in average protein production. However, variance analysis revealed consistently higher variability in E3 compared to E11, with significant p-values (4.24 × 10⁻⁵ and 2.25 × 10⁻⁹, respectively). These findings underscore the dynamic and variable stress response in younger fungi, in contrast to the stable and consistent protein production in older fungi across different conditions.

3.2.3. Characterization of Compounds in the Fungus Extracts

The chemical profile of the R. stolonifer extract (E3) was characterized using GC-MS to identify the specific metabolites involved in the biogenic synthesis of AgNPs (Figure S4). The chromatogram revealed a complex, multicomponent system consisting of approximately 100 different metabolites (match score > 80, Figures S5–S7). This mixture of distinct chemical families, summarized in Table 1, includes phenolic compounds, fatty acids and organic esters, terpenoids, and heterocyclic species. These results provide a characterization of the fungal secretome and offer essential insights into the dual-action mechanism of reduction and stabilization.

Table 1.

Characterization of R. stolonifer extracts with a total of 130 compounds detected with a criterion of match score ≥ 80.

No.	Retention Time	Compound Name	CAS#	Formula	Molecular Weight (Da)	Match Factor	Area
1	4.95	furfural	9998-01-1	C5H4O2	96.08	98.6	5.7 × 105
2	7.21	1,2-cyclopentanedione	003008-40-0	C5H6O2	98.1	97.9	9.9 × 105
3	8.28	5-methylfurfural	99 620-02-0	C6H6O2	110.11	97.9	7.8 × 105
4	1.91	acetic acid	000064-19-7	C2H4O2	60.05	97.6	5.2 × 106
5	6.85	gamma-butyrolactone	7331-52-4	C4H6O2	86.09	97.6	3.0 × 106
6	1.78	acetaldehyde, hydroxy-	000141-46-8	C2H4O2	60.05	97.3	9.1 × 106
7	1.58	formic acid	000064-18-6	CH2O2	46.03	97.2	2.0 × 106
8	4.78	2-methylpyrazine	109-08-0	C5H6N2	94.11	96.9	2.2 × 105
9	16.09	5-hydroxymethylfurfural	000067-47-0	C6H6O3	126.11	96.9	9.3 × 106
10	2.36	1-hydroxy-2-propanone	116-09-6	C3H6O2	74.08	96.8	1.9 × 106
11	8.93	phenol	108-95-2	C6H6O	94.11	96.2	2.1 × 106
12	18.36	4-vinylguaiacol	7786-61-0	C9H10O2	150.17	94.9	5.5 × 105
13	1.71	isobutyraldehyde	78-84-2	C4H8O	72.11	94.8	1.7 × 105
14	8.83	2,4-dihydroxy-2,5-dimethyl-3(2H)-furan-3-one	010230-62-3	C6H8O4	144.13	94.7	1.3 × 106
15	19.46	p-hydroxybenzyl alcohol	623-05-2	C7H8O2	124.14	94.5	1.4 × 106
16	3.73	1-hydroxy-2-propanone	116-09-6	C3H6O2	74.08	93.6	2.5 × 106
17	25.41	uracil	66-22-8	C4H4N2O2	112.09	93.5	3.6 × 106
18	11.94	o-methoxyphenol	90-05-1	C7H8O2	124.14	93.3	2.7 × 105
19	2.29	isovaleraldehyde	590-86-3	C5H10O	86.13	92.9	2.3 × 105
20	2.08	oxolane	000000-00-0	C4H8O	72.11	92.7	4.2 × 104
21	11.70	para-cresol	000000-00-0	C7H8O	108.14	92.2	8.3 × 105
22	9.16	2H-pyran-2,6(3H)-dione	005926-95-4	C5H4O3	112.08	92.1	3.7 × 105
23	13.66	4H-pyran-4-one, 2,3-dihydro-3,5-dihydroxy-6-methyl-	028564-83-2	C6H8O4	144.12	92.1	3.5 × 106
24	15.44	1,2-benzenediol	120-80-9	C6H6O2	110.11	92	1.5 × 106
25	2.68	2,3-pentanedione	000000-00-0	C5H8O2	100.12	91.3	1.1 × 105
26	11.10	4-hydroxy-2,5-dimethyl-3(2h)-furanone	000000-00-0	C6H8O3	128.13	91.3	3.9 × 105
27	3.01	methacrylic anhydride	000760-93-0	C8H10O3	154.16	91.1	4.3 × 104
28	23.35	beta-d-glucopyranose, 1,6-anhydro-	000498-07-7	C6H10O5	162.14	91	4.0 × 106
29	10.33	2,5-furandione, 3,4-dimethyl-	000766-39-2	C6H6O3	126.11	90.6	8.9 × 104
30	15.88	dihydrobenzofuran	496-16-2	C8H8O	120.15	90.6	1.7 × 106
31	28.84	4-hydroxyphenylacetamide	017194-82-0	C8H9NO2	151.16	90.5	2.9 × 106
32	2.37	butanal, 2-methyl-	000096-17-3	C5H10O	86.13	90.4	2.5 × 105
33	7.74	2,5-furandione, 3-methyl-	000616-02-4	C5H4O3	112.08	90.4	2.7 × 105
34	14.13	2-cyclopenten-1-one	000930-30-3	C5H6O	82.10	90.1	1.0 × 105
35	3.42	2,2′-bioxirane	001464-53-5	C4H6O2	86.09	88.4	1.5 × 106
36	4.72	2(5h)-furanone, 3-methyl-	022122-36-7	C5H6O2	98.1	88.3	1.4 × 105
37	57.10	dilauryl thiodipropionate	123-28-4	C30H58O4S	514.84	87.9	8.7 × 105
38	3.18	pyrazine	000290-37-9	C4H4N2	80.09	87.5	1.9 × 104
39	12.54	furan	110-00-9	C4H4O	68.07	87.4	2.6 × 105
40	4.06	propanoic acid, 2-oxo-, methyl ester	000600-22-6	C4H6O3	102.09	87.3	1.0 × 106
41	5.45	2-furanmethanol	000098-00-0	C5H6O2	98.1	87.3	2.3 × 106
42	10.52	5-methylfurfural	620-02-0	C6H6O2	110.11	87.1	1.3 × 105
43	17.62	1,4-dihydroxybenzene	123-31-9	C6H6O2	110.11	87	2.0 × 106
44	20.90	1, 1, 5-trimethyl-1, 2-dihydronaphthalene	30364-38-6	C13H16	172.27	86.9	9.4 × 104
45	20.52	toluene	108-88-3	C7H8	92.14	86.7	4.4 × 104
46	6.11	4-cyclopentene-1,3-dione	000930-60-9	C5H4O2	96.08	86.6	6.1 × 105
47	10.63	1,3-dioxol-2-one,4,5-dimethyl-	037830-90-3	C5H6O3	114.1	86.6	9.2 × 105
48	5.78	acetol acetate	000000-00-0	C5H8O3	116.12	86.4	8.4 × 104
49	19.86	propanoic acid, 3-chloro-, 4-formylphenyl ester	000000-00-0	C10H9ClO3	212.63	86.3	1.7 × 105
50	22.65	4-tolyl isocyanate	622-58-2	C8H7NO	133.05	86.3	5.2 × 105
51	11.55	2-furancarboxylic acid	000088-14-2	C5H4O3	112.08	86	3.6 × 105
52	16.65	acetic anhydride	000108-24-7	C4H6O3	102.09	85.9	2.8 × 106
53	10.72	ethylene glycol diacetate	000000-00-0	C6H10O4	146.14	85.6	6.5 × 105
54	29.63	adenine	000073-24-5	C5H5N5	135.13	85.6	1.3 × 106
55	2.69	propenoic acid	79-10-7	C3H4O2	72.06	85.4	3.0 × 105
56	6.70	2,2′-bifuran, 2,2′,5,5′-tetrahydro-	098869-92-2	C8H10O2	138.16	84.9	2.1 × 105
57	20.77	4-pyridinecarboxamide	001453-82-3	C6H6N2O	122.13	84.6	1.9 × 105
58	2.60	propionic acid	000000-00-0	C3H6O2	74.08	84.5	6.8 × 104
59	33.74	norharmane, n-acetyl-	1011-37-6	C13H10N2O	210.23	84.5	3.2 × 105
60	5.76	proline, 2-methyl-5-oxo-, methyl ester	056145-24-5	C7H11NO3	157.17	84.4	4.8 × 104
61	23.69	butyl para-hydroxybenzoate	94-26-8	C11H14O3	194.23	84.3	3.0 × 105
62	14.24	heptanediamide, n,n′-di-benzoyloxy-	10250-13-2	C21H22N2O6	398.41	84.1	9.6 × 104
63	3.66	3-methylpyridazine	1632-76-4	C5H6N2	94.11	84	1.1 × 105
64	14.72	4h-pyran-4-one, 3,5-dihydroxy-2-methyl-	1073-96-7	C6H6O4	142.11	84	4.8 × 105
65	11.90	4-hydroxy-2,5-dimethyl-3(2h)-furanone	3658-77-3	C6H8O3	128.13	83.3	9.9 × 105
66	8.05	2-furanmethanol, 5-methyl-	3857-25-8	C6H8O2	112.13	83	2.4 × 105
67	19.47	benzenemethanol, 3-hydroxy-	620-24-6	C7H8O2	124.14	82.5	2.4 × 106
68	16.64	phenylacetic acid	103-82-2	C8H8O2	136.15	82.3	2.6 × 105
69	22.23	naphthalene, 1,2-dihydro-4,5,7-trimethyl-	53156-11-9	C13H16	172.27	82.3	4.7 × 104
70	11.56	3h-1,2,4-triazol-3-one, 1,2-dihydro-	930-33-6	C2H3N3O	85.06	82.2	2.4 × 105
71	29.92	1-undecene, 9-methyl-	74630-41-4	C12H24	168.32	82.1	8.6 × 104
72	1.30	(2-aziridinylethyl)amine	4025-37-0	C4H10N2	86.14	82	9.3 × 106
73	17.07	2-fluoroimidazole	57212-34-7	C3H3FN2	86.07	82	7.0 × 104
74	14.13	2-cyclopenten-1-one	930-30-3	C5H6O	82.1	81.7	7.6 × 104
75	7.74	furan	110-00-9	C4H4O	68.07	81.5	2.5 × 105
76	19.88	bis(2-ethoxyethyl) ether	112-36-7	C8H18O3	162.23	81.4	8.0 × 104
77	16.65	ketone, methyl 2-methyl-1,3-oxothiolan-2-yl	5684-35-1	C6H10O2S	146.21	81.3	4.3 × 106
78	19.86	2-(2-methoxyethoxy)ethanol	111-77-3	C5H12O3	120.15	81.2	1.3 × 105
79	9.42	1h-pyrrole-2,5-dione	541-59-3	C4H3NO2	97.07	81.1	7.4 × 104
80	25.18	pyrolo[3,2-d]pyrimidin-2,4(1h,3h)-dione	496-04-8	C6H5N3O2	151.12	80.7	1.1 × 105
81	1.53	cyclobutylamine	2516-34-9	C4H9N	71.12	80.1	3.6 × 106
82	12.87	.alpha.-l-galactopyranoside, methyl 6-deoxy-	14687-15-1	C7H14O5	178.18	80	1.2 × 106
83	33.74	9h-pyrido[3,4-b]indole	244-63-3	C11H8N2	168.2	79.9	2.9 × 105
84	15.00	5-oxotetrahydrofuran-2-carboxylic acid, ethyl ester	1129-30-2	C7H10O4	158.15	79.6	2.6 × 105
85	14.24	benzoic acid	65-85-0	C7H6O2	122.12	79.1	1.6 × 105
86	2.81	ethyl-1-propenyl ether	928-55-2	C5H10O	86.13	78.9	2.0 × 105
87	13.15	valeric anhydride	2082-59-9	C10H18O3	186.25	78.9	1.6 × 105
88	10.47	1,2-ethanediol, 1,2-diphenyl-	484-17-3	C14H14O2	214.26	78.8	1.0 × 105
89	25.76	1,1,4,5,6-pentamethyl-2,3-dihydro-1h-indene	91313-26-9	C14H20	188.31	78.7	3.7 × 105
90	20.63	2′,6′-dimethyl-4′-propoxyacetophenone	34443-69-1	C13H18O2	206.28	78.6	2.0 × 105
91	3.31	crotonic anhydride	623-68-7	C8H10O3	154.16	78.4	4.3 × 104
92	15.89	5-oxotetrahydrofuran-2-carboxylic acid, ethyl ester	001126-51-8	C7H10O4	158.15	78.4	1.4 × 106
93	6.10	propanoic acid, 3-nitro-, methyl ester	020497-95-4	C4H7NO4	133.1	78.2	6.9 × 106
94	2.85	acetamidoacetaldehyde	064790-08-5	C4H7NO2	101.1	78	2.4 × 105
95	12.63	maltol	000118-71-8	C6H6O3	126.11	78	1.4 × 105
96	20.50	naphthalene, 1,2-dihydro-4,5,7-trimethyl-	053156-11-9	C13H16	172.27	78	3.3 × 104
97	7.90	2,3-butanedione monoxime	57-71-6	C4H7NO2	101.1	77.9	7.2 × 104
98	22.66	1h-indol-3-ol, acetate	000608-08-2	C10H9NO2	175.18	77.9	7.1 × 105
99	22.66	4-(2,6,6-trimethylcyclohexa-1,3-dienyl)but-3-en-2-one	001203-08-3	C13H18O	190.28	77.9	3.3 × 105
100	8.57	2(3h)-furanone, 5-acetyldihydro-	029393-32-6	C6H8O3	128.13	77.8	6.1 × 104
101	13.15	propylhydrazone propionaldehyde	000000-00-0	C6H14N2	114.19	77.8	2.4 × 105
102	21.85	benzaldehyde, 2-hydroxy-6-methyl-	018362-36-2	C8H8O2	136.15	77.8	3.6 × 106
103	26.62	homovanillic acid	000306-08-1	C9H10O4	182.17	77.8	7.9 × 105
104	25.42	aminomaleimide	037770-94-8	C4H4N2O2	112.09	77.6	2.9 × 106
105	26.14	1-(4-tert-butylphenyl)propan-2-one	081561-77-5	C13H18O	190.28	77.5	2.0 × 105
106	6.70	crotonyl isothiocyanate	060034-28-8	C5H5NOS	127.16	77.4	5.0 × 105
107	13.38	methane, isocyanato-	000624-83-9	C2H3NO	57.05	77.3	2.8 × 105
108	12.19	cyclopropyl carbinol	002516-33-8	C4H8O	72.11	77.2	2.5 × 105
109	1.75	ethyl vinyl ether	000000-00-0	C4H8O	72.11	76.8	5.9 × 105
110	5.61	furan, 2-methoxy-	025414-22-6	C5H6O2	98.1	76.8	3.7 × 104
111	9.39	n-butyl-tert-butylamine	016486-74-1	C8H19N	129.24	76.8	1.3 × 105
112	28.63	4-((1e)-3-hydroxy-1-propenyl)-2-methoxyphenol	458-35-5	C6H8O3	128.13	76.7	8.0 × 105
113	9.28	.alpha.-acetobutyrolactone	000517-23-7	C6H8O3	128.13	76.4	4.0 × 105
114	3.08	n-propyl acetate	000109-60-4	C5H10O2	102.13	76.3	3.3 × 106
115	19.54	1, 1, 5-trimethyl-1, 2-dihydronaphthalene	30364-33-1	C13H16	172.27	76.2	4.0 × 104
116	1.47	acetic anhydride	000108-24-7	C4H6O3	102.09	76	1.1 × 105
117	17.77	1-[(1-oxo-2-propenyl)oxy]-2,5-pyrrolidinedione	038862-24-7	C7H7NO4	169.14	76	8.3 × 105
118	19.31	phenol, 2,6-dimethoxy-	000091-10-1	C8H10O3	154.16	76	1.2 × 105
119	29.92	cyclopentane, 1,1,3,3-tetramethyl-	4516-69-2	C9H18	126.24	75.9	6.7 × 104
120	40.07	toluene	108-88-3	C7H8	92.14	75.8	5.3 × 104
121	1.99	dimethyl ether	000115-10-6	C2H6O	46.07	75.7	1.8 × 105
122	20.50	naphthalene, 1,2-dihydro-4,5,7-trimethyl-	053156-11-9	C13H16	172.27	75.7	5.6 × 104
123	2.63	trimethyl orthoformate	000000-00-0	C4H10O3	106.12	75.6	2.0 × 104
124	10.72	p-dioxane, 2,5-dimethyl-3-methylene-	003984-21-2	C7H12O2	128.17	75.4	2.7 × 106
125	18.26	triethylene glycol diacetate	149-73-5	C10H18O6	234.25	75.4	2.4 × 105
126	31.44	methanol, [4-(1,1-dimethylethyl)phenoxy]-, acetate	054889-98-4	C13H18O3	222.28	75.4	2.6 × 105
127	4.26	2-pyrrolidinone, 5-(cyclohexylmethyl)-	014293-08-4	C11H19NO	181.27	75.3	2.5 × 105
128	6.81	2(3h)-furanone	020825-71-2	C4H4O2	84.07	75.2	1.3 × 105
129	4.79	pyrazine, methyl-	000109-08-0	C5H6N2	94.11	75.1	5.9 × 105
130	13.38	2-acetyl-2,3,5,6-tetrahydro-1,4-thiazine	656-42-8	C6H11NOS	145.22	75	5.1 × 105

Unlike plant-mediated synthesis—where high concentrations of complex flavonoids or xanthones often act as both reducing and capping agents [48,49,50], fungal extracellular synthesis suggests a distinct synergistic mechanism [15,28,51]. The analysis undertaken identified a series of simple phenolic species such as phenol (8.9 min), p-cresol (11.7 min), guaiacol (11.9 min), and 4-vinylguaiaicol (18.4 min), alongside polysubstituted phenols such as catechol (1,2-benzenediol, 15.4 min), hydroquinone (1,4-dihydroxybenzene, 17.6 min), 4-hydroxybenzyl alcohol (19.5 min), 2,3-dihydroxybenzoate (26.4 min), resorcinol (1,3-dihydroxybenzenes, 10.5 min), 2,3-dihydroxybenzoate (24.6 min). These molecules feature π-conjugated systems with multiple hydroxyl groups, which correlate with the UV-Vis absorbance at 265 nm and serve as the primary reducing agents facilitating the electron transfer from Ag⁺ to Ag⁰ [50,52,53].

To validate the specific role of these compounds, standard gallic acid and ferulic acid as formers of silver NPs were tested. As reported elsewhere, these compounds can reduce silver ions but fail to produce stable NPs under controlled conditions [50,54]. This suggests that while simple phenols possess the redox potential to reduce silver, they lack the steric bulk and specific functional groups necessary to ensure long-term colloidal stability on their own. Therefore, the successful formation of AgNPs in an R. stolonifer system is attributed to the immediate reduction of silver ions by the phenolic mixture on contact. Once the silver atoms begin to nucleate into nanoparticles, the stabilization process is mediated by methyl and/or prenyl groups, primarily provided by the proteins and methoxylated phenols present in the fungal extract [15,40,50,54]. This highlights a critical functional synergy where phenolic hydroxyls drive the initial reduction, while the methyl-rich protein fraction provides the necessary capping properties to prevent metal aggregation [51,55,56]. This relationship between polyphenol concentration and protein structure is fundamental to understanding how R. stolonifer can achieve efficient biogenic synthesis [39,46].

3.3. Biogenic Synthesis of AgNPs

Table 2 presents data for selected E3 and E11 fungus extract samples, highlighting that the SPR occurs within the wavelength range of 430 to 490 nm. This variation in SPR is related to the interaction with organic matter. As previously reported, although the SPR for AgNPs typically occurs at 400 nm in biogenic synthesis, the UV-Vis signal falls within the range of 430–470 nm [55]. The SPR analysis partially confirms the formation of AgNPs but also reveals a lack of uniformity across the replicates. It was evident that the formation of nanoparticles was not influenced by the reaction time but by the characteristics of each extract, which in turn affected nanoparticle formation. Based on these findings, it was determined that there is a relationship between the extract characteristics under stress (i.e., polyphenols, proteins and amino acid concentrations) and their effect on the formation and properties of AgNPs [57,58,59].

Table 2.

AgNP synthesis with R. stolonifer fungus extracts after 3 and 11 days of growth for different stress times after 24 h of reaction. Note: Data that did not meet the three conditions stipulated for AgNP formation with R. stolonifer fungus extracts are shown in bold lettering.

Sample Code	Fungus Age, Days	Stress Time, h	Polyphenols, mg mL−1	Proteins, mg mL−1	Protein/Polyphenols Rate	Wavelength, nm
E3-2d	3	2	0.024	0.788	32.7	495
E3-48a	3	48	0.065	1.476	22.7	452
E3-2c	3	2	0.046	0.943	20.4	434
E3-48d	3	48	0.070	1.271	18.0	0
E11-48a	11	48	0.054	0.815	15.0	441
E11-96b	11	96	0.061	0.893	14.7	442
E11-24d	11	24	0.069	0.947	13.8	447
E11-846	11	46	0.035	0.414	11.8	450
E11-48c	11	48	0.073	0.854	11.7	448
E3-24d	3	24	0.074	0.863	11.6	0
E11-2a	11	2	0.106	1.181	11.2	0
E11-24c	11	24	0.081	0.899	11.1	451
E3-72b	3	72	0.117	1.270	10.8	455
E11-2c	11	2	0.087	0.906	10.4	0
E3-746	3	46	0.041	0.424	10.3	450
E11-946	11	46	0.041	0.421	10.3	450
E11-72a	11	72	0.084	0.833	9.9	0
E3-2b	3	2	0.109	1.069	9.8	0
E3-72a	3	72	0.087	0.856	9.8	453
E3-446	3	46	0.043	0.414	9.7	0
E11-48b	11	48	0.074	0.713	9.6	445
E11-2d	11	2	0.131	1.243	9.5	0
E3-24c	3	24	0.096	0.879	9.2	457
E3-96d	3	96	0.065	0.580	9.0	0
E3-230	3	0.5	0.047	0.410	8.6	434
E3-530	3	0.5	0.048	0.414	8.6	437
E3-727	3	27	0.048	0.410	8.5	451
E3-427	3	27	0.049	0.412	8.4	446
E11-24a	11	24	0.093	0.769	8.3	442
E11-827	11	27	0.051	0.418	8.2	437
E3-720	3	20	0.051	0.412	8.1	443
E3-546	3	46	0.053	0.422	8.0	450
E11-830	11	0.5	0.052	0.412	8.0	446
E11-930	11	0.5	0.052	0.412	8.0	443
E3-730	3	0.5	0.051	0.399	7.9	429
E11-1027	11	27	0.054	0.423	7.8	450
E11-2b	11	2	0.098	0.757	7.7	0
E3-724	3	24	0.054	0.417	7.7	449
E11-72b	11	72	0.114	0.868	7.6	450
E3-48c	3	48	0.181	1.375	7.6	0
E11-96d	11	96	0.101	0.754	7.4	441
E11-72d	11	72	0.097	0.718	7.4	456
E3-627	3	27	0.057	0.418	7.4	446
E11-820	11	20	0.055	0.405	7.3	435
E3-246	3	46	0.057	0.417	7.3	0
E3-130	3	0.5	0.058	0.412	7.1	434
E3-527	3	27	0.060	0.423	7.0	470
E3-24b	3	24	0.226	1.567	6.9	0
E3-72d	3	72	0.232	1.608	6.9	0
E11-1046	11	46	0.061	0.419	6.9	450
E3-420	3	20	0.059	0.403	6.8	446
E3-327	3	27	0.062	0.418	6.7	0
E3-646	3	46	0.064	0.427	6.7	0
E3-96a	3	96	0.172	1.133	6.6	0
E11-920	11	20	0.061	0.404	6.6	437
E3-48b	3	48	0.167	1.094	6.6	0
E11-927	11	27	0.061	0.400	6.5	450
E11-1020	11	20	0.063	0.409	6.5	434
E11-24b	11	24	0.113	0.728	6.4	443
E11-96a	11	96	0.108	0.688	6.4	444
E3-430	3	0.5	0.064	0.403	6.3	429
E3-220	3	20	0.065	0.406	6.2	446
E3-330	3	0.5	0.065	0.402	6.2	425
E11-72c	11	72	0.109	0.672	6.2	440
E11-48d	11	48	0.109	0.660	6.1	445
E3-630	3	0.5	0.068	0.407	6.0	437
E11-1024	11	24	0.074	0.432	5.9	435
E3-24a	3	24	0.150	0.863	5.8	454
E3-624	3	24	0.074	0.418	5.6	0
E3-224	3	24	0.077	0.419	5.4	451
E11-1030	11	0.5	0.074	0.402	5.4	446
E11-96c	11	96	0.134	0.720	5.4	442
E3-320	3	20	0.076	0.406	5.3	444
E3-520	3	20	0.077	0.411	5.3	451
E11-924	11	24	0.082	0.427	5.2	440
E3-524	3	24	0.083	0.429	5.1	459
E11-824	11	24	0.084	0.414	4.9	435
E3-424	3	24	0.083	0.406	4.9	446
E3-124	3	24	0.084	0.411	4.9	0
E3-324	3	24	0.086	0.412	4.8	464
E3-227	3	27	0.090	0.423	4.7	451
E3-146	3	46	0.090	0.412	4.6	0
E3-72c	3	72	0.259	1.048	4.0	0
E3-120	3	20	0.100	0.402	4.0	0
E3-620	3	20	0.104	0.410	4.0	443
E3-96c	3	96	0.160	0.627	3.9	0
E3-346	3	46	0.105	0.405	3.9	0
E3-2a	3	2	0.133	0.512	3.8	0
E3-96b	3	96	0.190	0.552	2.9	0
E3-127	3	27	0.155	0.426	2.7	0

After all quantification data and results from the synthesis of AgNPs were compiled and optimal ratio ranges were considered, the samples were ranked in descending order based on their protein/polyphenol ratio (Table 2). Table 2 indicates that, in some cases, despite the ratio being adequate, the results of nanoparticle production are negative.

A further analysis was conducted to determine optimal conditions. Three key conditions were stipulated for AgNP formation with R. stolonifer fungus extracts: (a) polyphenols concentration should range from 0.120 to 0.024 mg mL⁻¹; (b) protein concentration should range from 0.947 to 0.399 mg mL⁻¹; and (c) the protein/polyphenol ratio should range from 5 to 33. Table 1 highlights, in bold, all data that do not meet these three conditions, mostly representing samples that did not lead to the formation of AgNPs.

3.3.1. Characterization of the Biogenic AgNPs

The AgNPs produced using fungus extracts were classified according to the plasmon position in each sample. The use of UV-Vis spectroscopy is a fundamental technique for observing nanoparticle formation through SPR at a specific wavelength [24,60,61]. Additionally, the wavelength, absorbance, and area under the SPR curve provide insights into the size and shape of the AgNPs, as their size influences the intensity and position of the SPR due to the volume-to-surface area ratio. Similarly, the shape of the AgNPs affects how electrons respond to the incident electromagnetic field, resulting in different resonance modes depending on the geometry [49].

As shown in Figure S8, the signals share the same range with different absorbances and overlap, indicating that the NPs must be morphologically similar. For their characterization, several samples were selected from each range: range 1 (Figure S8), which spans from 435 to 425 nm, sample S3-524 was chosen; range 2 (Figure S9), covering the interval from 455 to 446 nm, samples S3-24a, S11-24c, S11-48c, and S3-72a were selected; range 3 (Figure S10), covering 445 to 437 nm, samples S3-320, S11-48d, E11-96d, and S11-827 were chosen; and, range 4 (Figure S11), which spans from 435 to 425 nm, samples S3-430 and S11-824 were selected. The selected samples were characterized using HRTEM and EDS to correlate their morphology and size with the reported plasmon shape.

Figure S12 presents the FI-TR spectrum for the fungus extract samples and the synthesized AgNPs. Notable signals denoted as a, b, and c were observed, where Signal a at 1580 cm⁻¹ is typically associated with the stretching vibrations of the C=C (sp2 hybridization) bond in aromatic rings or with the vibrations of the amide group (NH₂-C=O) [56]. Since fungus extracts contain proteins and aromatic compounds, this band suggests that these components may play a role in reducing and stabilizing AgNPs. Signal b at 1472 cm⁻¹ was attributed to the angular deformation of CH₂ or CH₃ bonds found in lipids or the vibrations of methyl and methylene groups in polysaccharides. These functional groups are common in fungal extracts, and their presence in the FT-IR spectrum suggests that lipids and polysaccharides are involved in the formation and stabilization of nanoparticles [26]. Finally, signal c at 1072 cm⁻¹ can be attributed to C-O stretching vibrations in alcohols, ethers, or possibly to C-N stretching vibrations in amines. Secondary metabolites in fungal extracts, such as carbohydrates and proteins, typically exhibit these bonds. The presence and enhancement of this signal suggest that these organic components of the fungal extract actively contribute to the synthesis of AgNPs [52]. The increase in the FT-IR spectrum peaks further confirms the involvement of organic compounds in the synthesis process.

Table S1 presents the elemental analysis of the synthesized AgNPs, revealing significant variability in the percentages of silver (%Ag) and oxygen (%O) among the different samples. The %Ag values ranged from 7% to 95%, indicating a wide concentration range likely influenced by the synthesis conditions and experimental parameters. On the other hand, an inverse relationship can be observed between the percentages of silver and oxygen. Specifically, samples with higher silver content tend to have lower oxygen concentrations.

The above-noted inverse relationship suggests that a high concentration of silver in the nanoparticles could be related to reduced oxide formation due to competition between silver and oxygen during synthesis [62]. As the amount of available silver increases, its reduction and stabilization in the form of nanoparticles is likely to be favored, which limits the interaction of oxygen with biomolecules and consequently reduces the formation of metal oxides. This inverse relationship between %Ag and %O provides an important insight for tuning nanoparticle composition and physicochemical properties. Understanding this balance is fundamental to designing nanoparticles with specific characteristics, such as increased stability, controlled reactivity, or enhanced properties for antimicrobial applications, and ultimately allowing their optimization. All samples resulted in spherical-shaped NPs with a wide distribution in size. Additionally, a biofilm of organic matter surrounding the particles was noted, which aligns with the observation from previous studies.

As shown in Table 3 and Figure S13, Range 1 exhibits a high variability in particle size and a high polyphenols/proteins ratio, particularly for the larger sizes. In this range, large-sized NPs correlate with high polyphenol/protein ratios, suggesting that NPs size may influence the relative concentration of these components. In contrast, Ranges 2 and 3 show relatively small and medium NP sizes, with significantly lower polyphenols/protein ratios compared to Range 1. Specifically, in Range 2, smaller NP sizes are associated with lower ratios, while in Range 3, intermediate sizes do not show a clear pattern in the polyphenols/protein ratio. These results suggest that NP size can significantly impact the polyphenols/protein ratio, particularly for the larger sizes. It can be concluded that a high protein/polyphenol ratio favors the growth of large particles. Therefore, range classification does not significantly correlate with size, indicating that other factors also influence the synthesis kinetics. These findings highlight the importance of considering multiple variables in optimizing the NP formation process and suggest that specific characterizations are crucial to evaluate their size, morphology, and dispersion as a function of the synthesis conditions.

Table 3.

Sizes of the biosynthesized AgNPs observed by HRTEM microscopy.

Range	Sample Code	Polyphenols, mg mL−1	Proteins, mg mL−1	Rate	Observed Size, nm
1	S3-524	0.083	0.429	5	21 ± 4
2	S3-24a	0.150	0.863	6	31 ± 27
	S11-24c	0.081	0.899	11	71 ± 15
	S11-48c	0.073	0.854	12	50 ± 25
3	S3-320	0.076	0.406	5	19 ±9
	S11-827	0.051	0.418	8	25 ± 10
4	S3-430	0.064	0.403	6	20 ± 4
	S11-824	0.084	0.414	5	30 ± 8

3.3.2. Process Optimization

As evident from the experimental results, the processes involve significant variability in microorganisms present, making process optimization complex. A feasible approach is to investigate correlations between the variables, as can be observed in Figure 2, which provides a scatter plot of all the variables. Explanatory variables can be useful for prediction and for optimization. For example, a linear relationship between hours of stress and polyphenol concentration can be seen, while a strong relationship between wavelength and any other variable is not evident. Although correlations exist, they should be interpreted cautiously, as they are only significant if the pairwise relationship is approximately linear.

Figure 2.

Correlations and scatterplots for pairwise relationships between variables. *** Highly significant correlation i.e., p < 0.001, ** Very significant correlation i.e., p < 0.01, * Statistically significant correlation i.e., p < 0.05. Marginally significant correlation p < 0.10, No symbol means correlation is not significant i.e., p >= 0.10.

Figure 3, Figure 4 and Figure 5 display the predictive performance of the developed Gaussian process models for the three scenarios described in Section 2.7. The first plot given in each figure represents predictions using all data for training, while the second plot shows predictions based on leave-one-out cross-validation. Notably, predictive performance in the latter case is generally expected to be lower, as predictions made from training data are typically more accurate than those used for validation.

Figure 3.

Relationships between observed data and model predictions for polyphenols (poly), proteins (proteins) and protein/polyphenol rate (rate): (a) for all data considered for training and (b) when leave-one-out cross-validation was used.

Figure 4.

Relationships between the observed data and the model predictions for wavelength (wave), area under the curve (area) and absorbance (absorb): (a) all data were considered for training and (b) when leave-one-out cross-validation was used.

Figure 5.

Relationships between the observed data and the model predictions for wavelength (wave), area under the curve (area) and absorbance (absorb): (a) when all data were considered for training and (b) when leave-one-out cross-validation was used.

In Figure 3, a potentially useful predictive model is presented for proteins, as there is strong agreement between the observed data and the model predictions (R² = 0.81). For polyphenol and protein/polyphenol ratio, some agreement can be observed between the data and predictions, though this relationship is more variable than that for proteins (R² = 0.57 and 0.65, respectively). Figure 4 shows generally poor predictive performance across outcomes. For wavelength, the model can predict whether the observed data are relatively large or not (resulting in a correlation of 0.78) but has limited predictive ability otherwise. A weak relationship exists between observed and predicted values (R² = 0.63 and 0.54, respectively) for the area under the curve and absorbance of the UV-Vis spectra. When additional explanatory variables are considered (Figure 5), the predictive models for the area under the curve and absorbance of the UV-Vis spectra improve substantially, with stronger relationships between observed and predicted values (R² = 0.81 and 0.87, respectively). This suggests that incorporating additional data, such as fungus age and stress hours, improves the predictability of the outcomes.

3.4. Antibacterial Efficacy of AgNPs

All disinfection tests showed a higher disinfection percentage than the microbial culture without any disinfectant (i.e., negative control). As shown in Figure S14, most AgNPs show 100% disinfection, which means the elimination of microbial growth after 24 h, indicating high disinfection activity up to a dilution factor of 4 for E. coli and up to a dilution factor of 8 for S. aureus. Above a dilution factor of 8, the disinfection values against E. coli increase sharply, which is typical due to the higher structural resistance of E. coli compared to S. aureus [63], meaning that the AgNPs have a minimum inhibitory concentration (MIC) at dilution factor 4 for Gram-negative model bacteria in all samples and a MIC at dilution factor 8 for Gram-positive model bacteria for all samples except sample S3-524.

This may be because Gram-positive bacteria have a thick layer of peptidoglycan and lack an outer membrane, while Gram-negative bacteria have a thin layer of peptidoglycan and possess a complex outer membrane containing lipopolysaccharide [64]. This makes Gram-positive bacteria more susceptible to inactivation than Gram-negative bacteria. On the other hand, this can also be observed through the mechanism of inactivation of Gram-positive bacteria, since during the diffusion phase, the nanoparticles concentrate largely in the extracellular space, infiltrating the cell wall as the concentration inside increases, causing them to distribute throughout the cell wall and the cytoplasmic membrane, resulting in a higher intracellular concentration [63]. In the diffusion phase of the mechanism for inactivating Gram-negative bacteria, the nanoparticles are highly concentrated in the extracellular space, infiltrating the outer membrane and exhibiting a decreasing concentration gradient from the outside towards the inside. They are distributed throughout the periplasmic space, indicating an equilibrium concentration between the extracellular and intracellular environments [63]. However, even at a dilution factor of 512, most NPs maintained a disinfection value above that of the negative control, indicating that bacterial growth was still prevented. In contrast, exceptions were observed for S. aureus, where some samples in range 2 and 3, namely S11-824, S3-320, S11-827, and S11-830, showed higher bacterial growth at dilutions of 128 and 512. This suggests that at low concentrations, certain AgNP formulations may act as a nutrient for S. aureus or may not generate sufficient oxidative stress to inhibit their growth, allowing the bacteria to adapt [64]. This phenomenon requires further investigation.

4. Conclusions

Based on the results obtained from the synthesis of silver nanoparticles with R. stolonifer fungus extracts, the following conclusions were reached:

Fungi act in a way that their stress response and biomolecule production are not directly dependent on time or specific stress conditions.

The differences in the structure and maturity of the fungi between day 3 and day 11 are significant. The fungus has greater stability on day 11 and better adaptation to stress conditions, allowing for the production of biomolecules with low variability between replicates.

No significant differences were observed between fungi during the synthesis of silver nanoparticles (AgNPs), suggesting that synthesis conditions can be standardized to obtain consistent results based on biomolecule production.

The optimal conditions for the synthesis of AgNPs with Rhizopus stolonifer fungus extracts require adequate concentrations of proteins and polyphenols and strong interactions between these components, with efficient synthesis when the proportion of proteins is 5 to 33 times higher than that of polyphenols.

All nanoparticles proved effective in the disinfection of E. coli and S. aureus regardless of their UV-vis range (different morphology and size), achieving 100% disinfection with dilution factors up to 4 and 8, respectively, irrespective of the differences between AgNPs.

Although the response of fungi to stress may vary depending on their maturity, the optimal conditions for the synthesis of AgNPs are the ratio and concentration of proteins and polyphenols, with the resulting nanoparticles being highly efficient in bacterial disinfection despite morphological differences present, underlining the feasibility of this methodology to produce effective antimicrobial agents.

Abbreviations

The following abbreviations are used in this manuscript:

AgNPs	Silver nanoparticles
NPs	Nanoparticles
PDA	Potato dextrose agar
BSA	Bovine serum albumin
SPR	Surface plasmon resonance
FT-IR	Fourier Transform Infrared Spectroscopy
GC-MS	Gas chromatography–mass spectrometry
EI	Electron ionization
NIST	National Institute of Standards and Technology
UV-Vis	Ultraviolet-visible
TAR	Total reflection attenuation technique
EDS	Energy-dispersive X-ray spectroscopy
TEM	Transmission electron microscopy
MIC	Minimum Inhibitory Concentration
E3	The fungus extracts from 3 days of growth
E11	The fungus extracts from 11 days of growth
HSPs	Heat shock proteins

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/molecules31173103/s1, Figure S1: Biomass production kinetics of the fungus R. stolonifer grown on PDA; Figure S2: UV spectra of the different extracts of the fungus R. stolonifer (a) for 3 days of growth and (b) for 11 days growth; Figure S3: FT-IR spectra of different R. stolonifer fungus extracts after 3 days of growth; Figure S4: Representative GC-MS total-ion chromatogram for R. stolonifer extract. Annotations of representative metabolites are displayed; Figure S5: Comparison of experimental and library mass spectra for phenolic compounds. Head-to-tail comparison of experimental mass fragmentation against NIST-MS library for (a) phenol, (b) 1,2-benzenediol, (c) p-cresol, (d) p-hydroxybenzyl alcohol, (e) 4-vinylguaiacol, (f) benzaldehyde, 2-hydroxy-6-methyl- Identifications are based on a match factor ≥ 80, as detailed in Table 1; Figure S6: Comparison of experimental and library mass spectra for methoxylated phenolic compounds. Head to tail comparison of experimental mass fragmentation against NIST-MS library for (a) o-methoxyphenol, (b) 2-(1-propenyl)-6methoxyphenol, (c) 2-(1-propenyl)-6-methoxyphenol, (d) homovanillic acid, (e) phenol, 2,6-dimethoxy-, (f) phenol, 2,6-dimethoxy-Identifications are based on a match factor ≥ 80 as detailed in Table 1; Figure S7: Comparison of experimental and library mass spectra for methoxylated phenolic compounds. Head-to-tail comparison of experimental mass fragmentation against NIST-MS library for (a) furfural, (b) 2,4-dihydroxy-2,5-dimethyl-3(2H)-furan-3-one, (c) 5-hydroxymethylfurfural, (d) 5-methylfurfural, (e) 2-methylpyrazine, (f) uracil. Identifications are based on a match factor ≥ 80 as detailed in Table 1; Figure S8: UV-vis spectra range of the synthesized AgNPs; Range 1: 435–425 nm; Figure S9. UV-vis spectra range of the synthesized AgNPs; Range 2: 455–446 nm. Figure S10. UV-vis spectra range of the synthesized AgNPs; Range 3: 445–437 nm. Figure S11. UV-vis spectra range of the synthesized AgNPs; Range 4: 435–425 nm. Figure S12: FT-IR spectra of R. stolonifer fungus extracts and biogenically synthesized AgNPs; Figure S13: HRTEM micrographs of the AgNPs in (a) Range 1, (b) Range 2, (c) Range 3, and (d) Range 4; Figure S14: Results of disinfection tests of AgNPs synthesized with R. stolonifer fungus extracts (a) E. coli y (b) S. aureus; Table S1: Elemental analysis of AgNPs biosynthesized with fungus extracts.

Author Contributions

Conceptualization, O.M.R.-N., V.H.R.-Z. and L.F.M.-D.; methodology, A.V.-R. and J.M.; software, J.M. and L.F.M.-D.; validation, O.M.R.-N. and A.S.C.-D.; formal analysis, L.F.M.-D. and M.P.-Á.; investigation, O.M.R.-N.; resources, A.V.-R.; data curation, A.V.-R., L.F.M.-D. and J.M.; writing—original draft preparation, A.O.-T. and A.S.C.-D.; writing—review and editing, O.M.R.-N., A.G. and E.R.B.; supervision, O.M.R.-N. and A.S.C.-D. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in this study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding authors.

Conflicts of Interest

Erick R. Bandala is an employee of NantBioRenewables LLC. Alain Salvador Conejo-Davila is an employee of Microprime. The other authors declare no conflicts of interest. NantBioRenewables LLC and Microprime had no role in the design of the study; in the collection, analysis, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Funding Statement

This research received no external funding.