Medicago sativa L. Plant Response against Possible Eustressors (Fe, Ag, Cu)-TiO₂: Evaluation of Physiological Parameters, Total Phenol Content, and Flavonoid Quantification

Abstract

The present study analyzed Medicago sativa L. crops irrigated by TiO₂ in the anatase phase and TiO₂ doped with Ag, Fe, and Cu ions at 0.1%w synthesized using the sol–gel method (SG) and the sol–gel method coupled with microwave (Mw-SG). The materials were added to the irrigation water at different concentrations (50, 100, and 500 ppm). Stress induction by nanomaterials was observed by measuring stem morphology, chlorophyll index, total phenols and flavonoids, and antioxidant activity through the DPPH (2,2-diphenyl-1-picrylhydrazy) radical inhibition assay. The nanomaterial treatments caused statistically significant reductions in parameters such as stem length, leaf size, and chlorophyll index and increases in total phenol content and DPPH inhibition percentage. However, the observed effects did not show clear evidence regarding the type of nanomaterial used, its synthesis methodology, or a concentration-dependent response. By generally grouping the results obtained to the type of dopant used and the synthesis method, the relationship between them was determined employing a two-way ANOVA. It was observed that the dopant factors, synthesis, and interaction were relevant for most treatments. Additionally, the addition of microwaves in the synthesis method resulted in the largest number of treatments with a significant increase in the total content of phenols and the % inhibition compared to the traditional sol–gel synthesis. In contrast, parameters such as stem size and chlorophyll index were affected under different treatments from both synthesis methods.

1. Introduction

When interacting with plants, nanomaterials (NMs), such as metals, metal oxides, polymers, or carbon structures, can generate different degrees of stress, which can lead to eustressic or distressic effects [1]. Within the area of stress induction by nanomaterials, the effects caused can be exploited for the enhancement of metabolic pathways that reflect on plant characteristics such as a larger size, improved nutritional content, and enhanced defense against biotic and abiotic stresses, among others [2,3].

Nanomaterials possess a variety of physicochemical features that lead the pathway to stress in plants and, therefore, to the positive or negative effects abovementioned [1]. Physicochemical characteristics such as size, surface area, dose, concentration, charge, and crystalline structure influence the generation of positive or negative effects on plant physiology and metabolic activity; however, the stress response will also be related to plant characteristics, such as age, tissue characteristics, route of exposure, molecules present in the media and additional stresses [1,4,5]. Because NPs’ characteristics influence plants’ responses, studies investigating the varied physicochemical characteristics and their influence on plants are adequate to recognize and relate how these characteristics relate to the observed effects in such a way that NPs features can be adjusted to obtain better results in plant development [6].

The alteration in secondary metabolite content is one of the main areas of NPs used in agriculture; secondary metabolites are molecules associated with defense mechanisms in plants and increase their production when they are affected by biotic and abiotic stress [7,8]. Nanomaterials, through multiple mechanisms such as reactive oxygen species (ROS) generation, have proven to be sources of stress induction [9], which under appropriate conditions, can cause an increase in the content of secondary metabolites without having more significant effects on other characteristics of the plants, leading to the generation of value-added products [10].

Nanomaterials such as metal oxides have proven to be suitable for producing plants with better characteristics [2]. Among these materials, TiO₂ has varied effects on various crops, offering positive aspects on plant development and increasing characteristics such as size, mass, and secondary metabolite content [11,12]. The benefits of using TiO₂ in plants are reflected in multiple investigations proving to be efficient products for developing crops with higher nutrient content and increased valuable compounds [13,14,15]. Plants such as Stevia rebaudiana Bertoni, Mentha piperita L., Saponaria officinalis L., and Triticum aestivum L. have improved features when interacting with TiO₂ NPs, increasing growth, germination, soluble sugar and proteins content, photosynthetic pigments, and phenolic contents, even if the plant was submitted to additional stresses such as salinity [16,17,18,19]. Although, some results indicate that TiO₂ can lead to adverse effects when increasing the concentrations, affecting germination and yield [19].

To our knowledge, the effect of TiO₂ on alfalfa and how different physicochemical characteristics alter its properties and characteristics has not been investigated. This work aimed to evaluate possible eustressic effects caused by TiO₂ materials synthesized by two different pathways and with different doping characteristics (Cu, Fe, Ag), showing how they relate to alfalfa morphology, chlorophyll index, total phenol, flavonoid content, antioxidant activity, and how factors such as synthesis and dopant relate to the effects observed.

2. Results

2.1. Physicochemical Characterization of TiO₂ Materials

Figure 1 shows a structural comparison between the materials synthesized by SG and Mw-SG. The synthesized materials by Mw-SG corresponding to Figure 1b,d,f,h show aggregates of lesser size than the pure sol–gel method (Figure 1a,c,e,g). The internal heat generated through this process helps form NMs with high crystallinity and small and uniform size.

EDS shows the elemental composition of the M-TiO₂, M = Ag, Fe, and Cu NPs, synthesized using the sol–gel method, where the presence of the Ti and O elements can be seen in Figure 2a for the undoped TiO₂. In Figure 2b–d, the elemental mapping for the doped materials is observed, where the presence of the elements Cu (Figure 2b), Fe (Figure 2c), and Ag (Figure 2d) are observed. Identical results were obtained for the Mw-SG synthesis method.

X-ray diffraction patterns for the SG- and Mw-SG-synthesized materials are shown in Figure 3a,b. The diffraction peaks at 2θ angles of 25.1°, 37.7°, 47.8°, 53.6°, 54.8°, 62.5°, 68.7°, 70.1°, and 75.1° are related to the anatase phase of TiO₂, no presence of rutile or brookite phase was observed based on the diffraction signals. No diffraction peaks related to the dopants due to their low concentration were also observed.

The crystallite size for both synthesis methods is compiled in

Table 1. Crystallite size by Scherrer and Williamson-Hall method and degree of crystallinity for the SG- and Mw-SG-synthesized materials.

Material	Scherrer (nm)		Williamson-Hall (nm)
	SG	Mw-SG	SG	Mw-SG
TiO2	8.65	8.04	19.80	12.49
Ag-TiO2	9.62	7.42	14.00	12.27
Fe-TiO2	9.28	6.81	18.73	11.45
Cu-TiO2	8.75	7.69	16.31	12.27

, where the Scherrer equation gave an average of 9 nm crystallite size for the SG NMs and 7.49 nm for the Mw-SG. On the other hand, the Williamson–Hall equation shows an average crystallite size of 17.2 nm for the SG NMs and 12.12 nm for the Mw-SG NMs.

The crystal phase was also confirmed by Raman spectroscopy. The spectra of sol–gel NMs are shown in Figure 4a. Mw-SG- (Figure 4b) and SG-synthesized materials show four signals at 142.7 (E_g), 396.8 (B_1g), 517 (B_1g/A_1g), and 637.7 (E_g) cm⁻¹, which are indicative of the presence of the anatase crystalline phase, without the presence of the brookite and rutile phases of the TiO₂. No band shifting or new signals are observed due to the low concentration of dopants.

2.2. Morphological Data

The stem length of the alfalfa crops was measured in the first and second appearing stems. Figure 5a contains the measurements obtained from the length (cm) of the central stem for plants treated with SG NMs. In general, it is observed that the treatments meant a minimum reduction of 10% and a maximum of 30% compared to the control. The pairwise analysis to control (Dunnett) indicates that the reductions observed for the three concentrations of TiO₂ and Fe-TiO₂ are significantly statistical; however, in the Ag-TiO₂ materials, the concentration of 500 ppm, as well as 50 and 100 ppm for Cu-TiO₂, do not indicate the significant difference with the control.

The measurements obtained from the second appearing stem (Figure 5b) also showed a size reduction. In this case, all the types of NMs in their three concentrations meant a statistically significant reduction in the average size of the second stem, reaching a general decrease of approximately 35%. On the other hand, the leaf length (Figure 5c) was also affected by the different NPs treatments, where materials such as TiO₂ and Ag-TiO₂ did not mean a statistically significant reduction, compared to Fe-TiO₂ were at its three concentrations caused an average decrease of 25%. On the other hand, the maximum reduction was obtained at 500 ppm with Cu-TiO₂, whose first two concentrations did not generate a significant change.

In the same way as the plants treated with NMs SG, the Mw-SG materials caused a reduction in parameters, such as the length of the first stem, to appear, as shown in Figure 6a. In general, the treatments of the different types of NMs caused an average reduction of 12% with a maximum reduction of 28%. A pairwise comparison to the control reveals that the materials used exerted a statistically significant abatement at different concentrations, such as 50 ppm for TiO₂, 100 ppm for Fe-TiO₂ and Cu-TiO₂, and 500 ppm for Fe-TiO₂. TiO₂ and Ag-TiO₂.

Data on the length of the second stem to appear (Figure 6b) indicated that materials such as TiO₂ and Cu-TiO₂ caused a non-statistically significant reduction in its three concentrations. In contrast, Fe-TiO₂ and Ag-TiO₂ only caused a statistically significant reduction of approximately 30% in 100 and 500 ppm concentrations, respectively. Finally, the effect observed in leaf length (Figure 6c) indicates a general lowering, which is significant for the Ag-TiO₂ treatments in all concentrations. At the same time, TiO₂ exerted a significant impact at 50 ppm and 100 ppm. On the other hand, Fe-TiO₂ and Cu-TiO₂ exerted a statistically significant reduction at 100 and 500 ppm.

2.3. Total Phenol and Flavonoid Quantification

To evaluate the effect of NMs on crop growth, the secondary metabolite content in leaves, stems, and roots was carried out and presented in

Table 2. Total phenol content in plants treated with SG NMs.

	Leaves			Stem			Root
	Gallic Acid 1 (mg/g)	SD (±)	%	Gallic Acid 1 (mg/g)	SD (±)	%	Gallic Acid 1 (mg/g)	SD (±)	%
50 ppm
Control	49.21 A	6.82	-	25.40 A	6.73	-	21.52 A	4.10	-
TiO2	58.21 A	6.26	18.28	35.13 A	2.46	38.30	36.78 B	2.22	70.91
Ag-TiO2	64.75 A	6.31	31.57	29.9 A	2.78	17.78	26.39 A	4.70	22.63
Fe-TiO2	62.00 A	6.56	25.99	31.42 A	3.37	23.70	25.94 A	1.58	20.53
Cu-TiO2	59.18 A	7.59	20.26	35.90 A	6.40	41.33	24.91 A	1.29	15.75
100 ppm
TiO2	84.08 B	5.80	70.85	49.01 B	2.83	92.95	34.65 B	8.12	61.01
Ag-TiO2	59.36 A	5.50	20.62	43.72 B	7.87	72.12	34.05 B	6.37	58.22
Fe-TiO2	49.99 A	1.87	1.58	34.77 A	6.24	36.88	27.43 A	1.69	27.46
Cu-TiO2	63.20 A	9.24	28.42	42.98 B	5.72	69.21	33.21 B	3.72	54.32
500 ppm
TiO2	67.03 B	11.27	36.21	49.86 B	9.08	96.29	32.47 B	2.73	50.88
Ag-TiO2	52.26 A	4.49	6.19	37.72 A	3.95	48.50	24.65 A	5.91	14.54
Fe-TiO2	59.02 A	1.16	19.93	37.59 A	5.72	47.99	25.14 A	3.14	16.82
Cu-TiO2	53.62 A	8.24	8.96	38.24 A	3.42	50.55	34.44 B	1.66	60.03

¹ mg GAE/sample g (mg gallic acid equivalents/sample g). The average represents the value of 3 repetitions. Comparison between means (Dunnett p ≤ 0.05). Means with different letters in the same column are statistically different. The percentage columns (%) represent the increase (+) or decrease (−) of the quantified data concerning the control group.

. The effect of the MNs on the gallic acid content in the three organs analyzed was treated by 1-way ANOVA and treated with a Dunnett test, comparing the treatment with the control. The leaves of the plants treated with NMs SG generally show that the plants treated with NMs have a higher gallic acid content. However, statistical analysis reveals that none of the 50 ppm treatments caused a significant effect at the lowest concentration, with TiO₂ being the only material to generate a relevant increase of approximately 70 and 36% for the 100 and 500 ppm concentrations, respectively.

On the other hand, the gallic acid content suffered an increase for the plants treated with NMs; however, at 50 ppm, none of the treatments were considered statistically significant. At 100 ppm, materials such as TiO₂, Ag-TiO₂, and Cu-TiO₂ caused an average increase of 40%, while at 500 ppm, only TiO₂ caused a significant increase of 50%. Root analysis shows a significant 70% increase at 50 ppm for TiO₂. At 100 ppm, the TiO₂, Ag-TiO₂, and Cu-TiO₂ materials reflected a substantial increase of more than 50%. Finally, at 500 ppm, TiO₂ and Cu-TiO₂ were the only statistically significant treatments, with an increase greater than 50%.

The content of total flavonoids measured, expressed as mg eq. rutin, showed atypical results where the content in mg was higher than the total phenolic content, which can be considered a misinterpretation as it is a subcategory of phenolic compounds. This higher response is associated with the standard, which may overestimate the quantified content. Despite this, statistical analysis revealed that these were not statistically significant despite showing an increase in total flavonoid content in most treatments compared to the control (Table S1).

The gallic acid content in the leaves of the plants treated with Mw-SG NMs (

Table 3. Total phenol content in plants treated with Mw-SG NMs.

	Leaves			Stem			Root
	Gallic Acid 1 (mg/g)	SD (±)	%	Gallic Acid 1 (mg/g)	SD (±)	%	Gallic Acid 1 (mg/g)	SD (±)	%
50 ppm
Control	45.72 A	4.01	-	20.48 A	2.38	-	21.71 A	7.76	-
TiO2	60.71 B	7.34	32.78	28.27 A	1.92	38.05	29.92 A	5.02	37.81
Ag-TiO2	64.08 B	9.87	40.16	24.57 A	2.57	19.97	22.28 A	1.14	2.62
Fe-TiO2	59.83 B	3.45	30.86	30.57 B	9.08	49.26	18.09 A	3.87	6.33
Cu-TiO2	54.96 A	13.89	20.20	32.46 B	5.85	58.49	29.08 A	3.54	33.94
100 ppm
TiO2	67.55 B	5.11	47.74	38.26 B	7.02	86.84	21.80 A	5.39	0.41
Ag-TiO2	58.83 B	7.19	28.68	28.09 A	3.14	37.16	27.28 A	4.90	25.65
Fe-TiO2	65.81 B	4.58	43.95	34.31 B	6.51	67.53	22.32 A	6.18	2.80
Cu-TiO2	72.76 B	5.93	59.14	28.68 A	7.47	40.03	37.00 B	8.77	70.42
500 ppm
TiO2	69.45 B	3.10	51.90	26.99 A	1.71	31.78	25.82 A	2.08	18.93
Ag-TiO2	58.31 B	4.88	27.53	30.01 B	3.98	46.53	25.64 A	4.60	18.10
Fe-TiO2	73.85 B	8.84	61.53	35.12 B	3.16	71.48	29.94 A	6.61	37.89
Cu-TiO2	65.67 B	5.88	43.63	46.09 B	8.34	125.04	37.77 B	6.44	73.97

¹ mg GAE/sample g (mg gallic acid equivalents/sample g). The average represents the value of 3 repetitions. Comparison between means (Dunnett p ≤ 0.05). Means with different letters in the same column are statistically different. The percentage columns (%) represent the increase (+) or decrease (−) of the quantified data concerning the control group.

) suffered an increase where at 50 ppm, the TiO₂, Ag-TiO₂, and Fe-TiO₂ treatments caused a statistically significant increase of more than 30%. On the other hand, at 100 ppm, all treatments caused a significant increase of at least 28%. Finally, at 500 ppm, the four NMs treatments resulted in a statistically significant increase to the control, increasing the gallic acid content between 27 and 51%. In the case of the stem, materials such as Fe-TiO₂ and Cu-TiO₂ did not cause a significant increase in gallic acid content. At 100 ppm, only the TiO₂ and Fe-TiO₂ treatments resulted in a statistically significant increase of at least 67%. Finally, at 500 ppm, only the TiO₂ treatment was not statistically significant, whereas the other materials caused a rise of at least 40%. Finally, the observed effect of the treatments on the roots indicates that none of the treatments caused a statistically significant impact at 50 ppm. In comparison, at 100 and 500 ppm, only the Cu-TiO₂ material caused a significant increase of approximately 70% for both concentrations.

For Mw-SG NMs, the flavonoid content suffered a general increase in all the treatments of up to 80%; however (Table S2), most treatments were not statistically different, maintaining their total content in mg higher than the total content of phenols. On roots, the content of total flavonoids was statistically different for the Cu-doped materials at 500 ppm, causing an increase of 41.99%.

2.4. Antioxidant Activity

The statistical analysis of the leaf parameters (

Table 4. DPPH inhibition (IHB) % in plants treated with SG NMs.

	Leaves			Stem			Root
	IHB 1 %	SD (±)	%	IHB 1 %	SD (±)	%	IHB 1 %	SD (±)	%
50 ppm
Control	30.66 A	4.08	-	25.65 A	2.19		12.63 A	0.92	-
TiO2	43.47 B	1.54	41.64	39.28 B	2.02	53.31	19.85 B	3.33	57.16
Ag-TiO2	43.88 A	2.69	42.97	41.15 B	4.10	60.61	17.41 A	3.19	37.84
Fe-TiO2	39.86 A	4.27	29.87	29.65 A	1.04	15.72	23.97 B	1.58	89.78
Cu-TiO2	38.19 A	1.97	24.43	33.68 A	4.17	31.45	17.75 A	5.32	40.53
100 ppm
TiO2	46.28 B	11.44	50.79	43.76 B	1.26	70.80	22.60 B	0.45	78.93
Ag-TiO2	40.47 A	4.34	31.86	32.07 A	2.67	25.17	21.13 B	2.36	67.30
Fe-TiO2	35.32 A	0.54	15.08	28.01 A	1.50	9.32	25.78 B	1.37	104.11
Cu-TiO2	37.93 A	1.70	23.59	30.15 A	5.69	17.68	15.73 A	2.56	24.54
500 ppm
TiO2	40.80 A	6.19	32.94	40.41 B	9.60	57.72	20.84 B	2.14	65.00
Ag-TiO2	41.06 A	6.27	33.79	32.50 A	6.51	26.85	21.20 B	4.04	67.85
Fe-TiO2	39.10 A	1.52	27.40	32.21 A	2.59	25.72	24.54 B	2.99	94.29
Cu-TiO2	34.98 A	4.58	13.97	28.33 A	2.42	10.57	21.38 B	2.53	69.27

¹ DPPH radical inhibition percentage. The average represents the value of 3 repetitions. Comparison between means (Dunnett p ≤ 0.05). Means with different letters in the same column are statistically different. The percentage columns (%) represent the increase (+) or decrease (−) of the quantified data concerning the control group.

) reveals that at a concentration of 50 and 100 ppm, the NM TiO₂ was the only one that caused a significant increase of approximately 40 and 50%, respectively. At the same time, the other materials did not generate relevant values along the three connections. On the other hand, the % of IHB in the stem was affected by significant multiple treatments caused at 50 ppm by TiO₂ and Ag-TiO₂, displaying a minimum increase of 50%; at concentrations of 100 ppm, only the treatment with TiO₂ resulted in a significant rise of 70%, while at 500 pm, said material caused a 57% increase. In contrast, the rest of the treatment was not statistically relevant. Finally, the effect observed in the roots indicates a modification in its antioxidant capacity. At 50 ppm, TiO₂ and Fe-TiO₂ generated an increase greater than 50%; at 100 ppm, the TiO₂, Ag’TiO₂, and Fe-TiO₂ treatments caused a significant increase of more than 60%. Finally, at 50 ppm, all the materials significantly increased the antioxidant capacity of the roots by at least 65%.

For the SG-Mw-treated plant, IHB% was also increased (

Table 5. DPPH inhibition (IHB)% in plants treated with SG-Mw NMs.

	Leaves			Stem			Root
	IHB 1 %	SD (±)	%	IHB 1 %	SD (±)	%	IHB 1 %	SD (±)	%
50 ppm
Control	33.93 A	1.78	-	23.24 A	2.58	-	20.40 A	1.71	-
TiO2	55.88 B	1.80	64.69	36.94 B	1.39	58.95	45.47 B	6.22	122.89
Ag-TiO2	49.05 B	4.24	44.55	42.09 B	1.46	81.11	26.20 A	2.72	28.43
Fe-TiO2	44.55 B	1.63	31.29	42.80 B	1.53	84.16	33.80 B	1.36	65.68
Cu-TiO2	55.91 B	3.43	64.77	42.75 B	4.54	83.95	38.80 B	1.68	90.19
100 ppm
TiO2	47.49 B	2.96	40.84	37.36 B	1.29	60.76	44.06 B	3.86	115.98
Ag-TiO2	54.14 B	4.31	59.56	44.35 B	4.08	90.83	39.14 B	2.40	91.86
Fe-TiO2	43.78 B	2.87	29.03	41.83 B	2.20	79.99	45.12 B	1.98	121.13
Cu-TiO2	46.74 B	5.51	37.75	31.96 B	2.43	37.55	35.48 B	7.06	73.92
500 ppm
TiO2	46.67 B	0.91	37.54	34.05 B	1.36	46.51	42.52 B	5.22	108.43
Ag-TiO2	47.65 B	2.19	40.42	42.17 B	1.41	81.45	38.17 B	3.79	87.10
Fe-TiO2	46.55 B	3.56	37.20	42.07 B	2.65	81.04	37.28 B	5.50	83.82
Cu-TiO2	53.19 B	2.64	56.76	47.25 B	2.11	103.31	37.66 B	4.32	84.60

¹ DPPH radical inhibition percentage. The average represents the value of 3 repetitions. Comparison between means (Dunnett p ≤ 0.05). Means with different letters in the same column are statistically different. The percentage columns (%) represent the increase (+) or decrease (−) of the quantified data concerning the control group.

). However, no relation between an increasing NP concentration and an increase in radical inhibition was observed. The leaf analysis shows that at the three concentrations used, all the NMs were capable of increasing IHB% in a statistically significant matter, whereas at 50 and 100 ppm, the treatments such as Fe-TiO₂ caused an increase of 30%, the highest increase being of 64% obtained at 50 ppm by TiO₂. All the treatments in the stem proved significant again, achieving an increase of between 30 and 100%. Finally, the analysis in the roots shows that the NMs used at the three concentrations caused a significant increase of between 28 and 120%, except for the treatment at 50 ppm by Ag-TiO₂.

2.5. Chlorophyll Index

In general, an apparent decrease in the chlorophyll index was observed (Figure 7a); however, the reduction was not statistically significant to the control. TiO₂ material at a concentration of 50 and 100 ppm achieved a statistically significant decrease of approximately 30%. On the other hand, for the Fe-TiO₂ material, only 100 ppm significantly reduced the chlorophyll index, achieving a 27% reduction.

The chlorophyll indices of the plants treated with the materials obtained by the Mw-SG synthesis method are shown in Figure 7b. It was observed that the chlorophyll content was significantly reduced at concentrations of 50 and 100 for the TiO₂ and Cu-TiO₂ treatments, generating a decrease more significant than 14%. The Fe-TiO₂ material also caused a statistically significant decrease reflected at 50 and 100 ppm, causing a 15% decrease. Finally, the Ag-TiO₂ material caused a behavior not previously observed where the chlorophyll index increased significantly to the control by 13%, while at 100 and 500 ppm, there was a similar reduction of 15%.

2.6. Two-Way ANOVA

Based on the previously shown results, the use of NMs meant a decrease in parameters such as stem size, leaf length, and chlorophyll index, in turn, an increase in the phenol content and the antioxidant activity present in the plant. The behavior of the observed results is not related to the rise in the concentration of NMs. Although some treatments show statistical significance compared to the control, a statistical comparison by pairs reveals that the effect observed between NMs treatment does not represent a significant difference. The results obtained in each concentration (50, 100, and 500 ppm) were considered mere replicates and grouped on their respective NMs. Under these considerations, two-way ANOVA analyzes were performed, normalizing data to control, taking as factors the dopant of each NM (undoped, Fe, Cu, and Ag), taking as repetitions the values collected from each concentration used, and the synthesis method (SG and MW). Keeping the control data makes it possible to corroborate whether the treatments have a significant effect, regardless of the concentration used. On the other hand, comparing the factors without using control data would indicate if the factors had a substantial difference between them, helping to observe if the presence of dopants influences the measured parameters compared to the undoped sample.

Two-way ANOVA analyses with and without control over the data obtained for the length of the first stem at appearance (

Table 6. Two-way ANOVA test for the first appearing stem size.

Factors	ANOVA	SS	DF	MS	F	p Value
Synthesis–Dopant (Control)	Synthesis:dopant	0.3898	4	0.09745	2.980	0.0191 *
	Synthesis	0.1639	1	0.1639	5.012	0.0257 *
	Dopant	1.736	4	0.4341	13.28	0.0001 ***
	Residual	12.82	392	0.03270
Synthesis–Dopant (No control)	Synthesis:dopant	0.3703	3	0.1234	3.648	0.0129 *
	Synthesis	0.2614	1	0.2614	7.726	0.0057 **
	Dopant	0.2686	3	0.08955	2.647	0.0489 *
	Residual	12.11	358	0.03383

* = significant at p ≤ 0.05, ** = significant at p ≤ 0.01, *** = significant at p ≤ 0.001

) indicate that synthesis, doping, and the interaction of both factors are significant, suggesting a change in the nanoparticle because of the synthesis method and its respective doping. A Tukey pairwise test was performed to differentiate the effects of synthesis and dopant (Table S3). The results indicate that the four types of nanoparticles significantly reduced stem length for both synthesis methods. Nevertheless, there was a difference in the effect of doping-related growth inhibition where copper-SG caused a significant impact compared to TiO₂. For the Mw-SG materials, a significant impact was observed with the Fe-TiO₂, the other NMs having a non-significant response concerning TiO₂.

The case of the second stem corresponding to the ANOVA analysis (Table S4), comparing the nanoparticles without control, shows that only the synthesis factor was significant. Furthermore, the Tukey analysis (Table S5) demonstrated the most significant influence on the length of the second stem for the SG materials. All the treatments were significantly statistically compared to Mw-SG, where only Ag-TiO₂ and Cu-TiO₂ modified the size. In turn, the null difference between the effect of the dopant on TiO₂ for both synthesis methods.

The leaf phenol content showed that the analysis with control and without control significantly affected the synthesis and doping factors (

Table 7. Two-way ANOVA test for the total gallic acid content in leaves.

Attribute	ANOVA	SS	DF	MS	F	p Value
Synthesis–Dopant (Control)	Synthesis:dopant	0.2995	4	0.07489	2.190	0.0750 ns
	Synthesis	0.3779	1	0.3779	11.05	0.0012 **
	Dopant	1.167	4	0.2917	8.529	0.0001 ***
	Residual	3.659	107	0.03419
Attribute	ANOVA table	SS	DF	MS	F	pvalue
Synthesis–Dopant (No control)	Synthesis:dopant	0.2487	3	0.08289	2.314	0.0805 ns
	Synthesis	0.6614	1	0.6614	18.47	0.0001 ***
	Dopante	0.3924	3	0.1308	3.652	0.0151 *
	Residual	3.582	100	0.03582

ns = not significant; * = significant at p ≤ 0.05, ** = significant at p ≤ 0.01, *** = significant at p ≤ 0.001

). The comparative table with the Tukey analysis (Table S6) indicates a more significant effect for the Mw-SG materials, where all the treatments with dopants significantly increased the phenol content. However, there was no statistical difference between TiO₂ and the rest of the dopants, which reveals the same effect on phenol content regardless of the used dopant. A lower influence was observed in the SG materials, where only the TiO₂ material indicated a significant effect compared to the control. On the other hand, the comparative analysis showed a statistical difference between the Fe-TiO₂ material and TiO₂.

In the case of the phenolic content in the stem, there was no significant influence between the synthesis and dopant factors for the comparison without using the control (Table S7). The Tukey pairwise comparison (Table S8) shows no significant effect related to the SG NMs, while the Mw-SG materials, such as TiO₂, Cu-TiO₂, and Fe-TiO₂, caused a significant change. The analysis of the phenolic content in roots was influenced by dopant and the interaction between factors (Table S9). In contrast, the pair analysis between materials significantly affected both parameters’ synthesis, dopant, and interaction (Table S10). The comparison by Tukey indicates that the NMs SG only have a significant effect due to TiO₂. At the same time, for the Mw-SG, the Cu-TiO₂ meant the greatest effect, which was statistically significant compared to TiO₂.

The ANOVA analysis for the inhibition % in leaves shows that the doping factors, synthesis, and their interaction are significant; this is shown in the pairwise comparison (

Table 8. Two-way ANOVA test for the inhibition % of DPPH in leaves.

Attribute	ANOVA Table	SS	DF	MS	F	p Value
Synthesis–Dopant (Control)	Synthesis:dopant	0.3042	4	0.07605	4.092	0.0040 **
	Synthesis	0.2926	1	0.2926	15.75	0.0001 ***
	Dopant	1.422	4	0.3555	19.13	<0.0001 ****
	Residual	1.970	106	0.01858
Attribute	ANOVA table	SS	DF	MS	F	pvalue
Synthesis–Dopant (No control)	Synthesis:dopant	0.2642	3	0.08805	4.539	0.0050 **
	Synthesis	0.5076	1	0.5076	26.17	0.0001 ***
	Dopant	0.3641	3	0.1214	6.256	0.0006 ***
	Residual	1.921	99	0.01940

** = significant at p ≤ 0.01, *** = significant at p ≤ 0.001, **** = significant at p ≤ 0.0001.

), where the most significant effect on this parameter is reflected in the Mw-SG materials being all the significant treatments, especially the Fe-TiO₂. In the case of the SG materials, it can be seen how the TiO₂ and Ag-TiO₂ significantly changed concerning the control (Table S11).

These factors and their interaction remained statistically relevant for the other sections measured (stem and roots) (Tables S12 and S14), where it was also observed that the microwave synthesis method generated a more significant effect than the SG route.

The dopant and synthesis factors and their interaction remained significant for the effect observed in the chlorophyll index (

Table 9. Two-way ANOVA test for the chlorophyll index.

Attribute	ANOVA Table	SS	DF	MS	F	p Value
Synthesis–Dopant (Control)	Synthesis:dopant	1.023	4	0.2558	5.729	0.0002 *
	Synthesis	0.1865	1	0.1865	4.177	0.0416 *
	Dopant	1.521	4	0.3801	8.514	<0.0001 *
	Residual	17.90	401	0.04465
Attribute	ANOVA table	SS	DF	MS	F	pvalue
Synthesis–Dopant (No control)	Synthesis:dopant	0.9994	3	0.3331	7.326	<0.0001 *
	Synthesis	0.3385	1	0.3385	7.445	0.0067 *
	Dopant	0.6999	3	0.2333	5.131	0.0017 *
	Residual	16.96	373	0.04547

* = significant at p ≤ 0.05

). However, a clear difference concerning the general effect due to the synthesis method was not observed. In this case, it is shown in Table S15, the comparison by pairs, that for the SG materials, the most significant effect in the reduction in the chlorophyll index was by TiO₂ and Fe-TiO₂, while in the Mw-SG, it was by the materials Cu-TiO₂ and Fe-TiO₂.

3. Discussion

As mentioned above, NPs’ physicochemical characteristics guide the stress generation mechanisms, leading to plants’ biostimulation or inhibitory activity [1]. Through sol-gel synthesis and sol-gel microwave assisted, it was obtained TiO₂ nanomaterials with different doping elements are present in their structure, as seen in EDS analysis. Furthermore, it was revealed that the followed route of synthesis as well as post-treatment resulted in structural modifications, as presented in Raman spectroscopy. A more detailed analysis of XRD patterns revealed that the addition of energy through microwaves leads to a smaller crystallite size [20] among all types of doped NPs, making their crystallite size, as seen through Williamson–Hall, more even as compared to sol–gel synthesis.

Dunnett’s analysis showed that specific treatments could significantly raise or lower the measured parameters; however, said treatments did not show a significant dependence on the concentration of nanomaterial supplied. In summary, two-way ANOVA analysis confirmed the influence of factors such as dopant, synthesis method, and their interaction, which were relevant in parameters such as chlorophyll index, stem size, and inhibition % in the three organs measured. Figure 8 summarizes the effects observed in the plant due to the treatments and indicates which of these seen by the pair test generated a significant impact concerning their normalized controls.

Parameters such as stem size indicated that both synthesis methods had an adverse effect under all applied treatments. On the other hand, the chlorophyll index was affected by individual treatments, which differed according to the synthesis method. The total content of phenols reflected a more significant difference depending on the properties of the NMs acquired by the synthesis method. Only the TiO₂ SG caused a significant increase in this parameter in the three organs measured. By modifying the synthesis method, the NMs’ characteristics were changed, resulting in a more significant number of effective treatments, as in the content of phenols in leaves and stems. Said effect was also observed in the inhibition % of DPPH, where plants suffered an increase by the TiO₂ and Ag-TiO₂ SG treatments in the three organs measured, while the four types NMs synthesized through microwaves had a significant response, indicating a greater interaction of these due to the modification of their synthesis routes. Dunnett’s analysis showed that specific treatments could significantly raise or lower the measured parameters; however, said treatments did not show a significant dependence on the concentration of nanomaterial supplied.

Studies of TiO₂ interaction with plants have mixed effects where the presence of TiO₂ enhances some species’ growth of multiple organs such as roots or stems, while other cases show detrimental effects on the growth of these organs [21,22,23,24]. Many mechanisms associated with growth inhibition involve root hair blockage by NMs and damage by ROS, reducing plants’ ability to absorb nutrients, causing reduced plant growth, and multiple morphological traits alteration [25,26,27]. An increased secondary metabolite content could be mainly due to reactive oxygen species (ROS), one of the primary mechanisms described for NMs’ plant interaction and stress induction [9]. The excess of these oxidating molecules affects the vegetable cell disrupting the membrane and unbalancing the plant’s cellular development affecting molecules such as proteins, DNA, and lipids [28]. To mitigate the effect caused by the ROS, the plant synthesizes metabolites that can scavenge the molecules reducing the stress factor caused by the cellular damage, thus increasing its antioxidant activity [29,30], as observed in the results. On the other hand, a study of Phaseolus vulgaris L. exposed to CeO₂ on a solid medium revealed a modification of flavonoid content in roots and leaves. However, polyphenol compounds suffered a pronounced increase in leaves compared to roots, implying that NPs’ effect on secondary metabolisms could have different responses depending on plant tissue [31], as seen before.

In the case of the % inhibition of DPPH, it was observed that some treatments at different concentrations caused a significant increase in the antioxidant activity measured in the three organs. Seen in the two-way ANOVA, the synthesis and dopant factors and the interaction of both were significant, indicating that the effect on the oxidant activity was highly related to the dopant type and the employed synthesis. Although some treatments appear to increase IHB% while having no significant augmentation of the phenol content at several concentrations, this effect could be related to the biostimulation of other secondary metabolism antioxidant molecules that are not measured with the techniques used, such as saponins, which are highly present in alfalfa [32].

The chlorophyll content also suffered a decrease, where it can be observed that the microwave synthesis resulted in a significant reduction due to Cu-TiO₂, which in the SG synthesis was not the cause of the said effect. On the contrary, the Mw-SG synthesis derived in a TiO₂ treatment did not generate a significant impact compared to that synthesized by SG. Both synthesis methods coincided in a reduction due to Fe-TiO₂. Results indicating the reduction or augmentation of photosynthetic pigments in plants treated with TiO₂ have also been determined [33,34,35,36,37].

Plants with long-term exposure to the NMs can adapt to the stress effects generated by the same NMs, which could alter plant response to NPs [9,38]. As shown in this research, several concentrations did not significantly affect the metabolite content modification. However, this does not imply that alfalfa crops can resist the effect at higher exposure times or NPs with different morphological characteristics. Lower and higher concentrations and comparing metabolite content in different development stages can provide insight into NMs’ plant interaction and secondary metabolism modification. The possibility of TiO₂ translocation into higher organs could mean different adversities caused by the interaction of NMs with molecules and biochemical processes in different organs. Different alterations in each section depended on NMs’ capability to move through plant tissues, as observed with the metabolic assays. NMs with structural differences appeared to have different degrees of metabolic stress at each organ measured [39].

4. Materials and Methods

4.1. Titanium Dioxide and Doped TiO₂ Materials Synthesis and Characterization

Titanium isopropoxide 97% (Sigma Aldrich, St. Louis, MO, USA) was dissolved in isopropanol 99% (J.T. Baker, Phillipsburg, NJ, USA). The solution was stirred for 20 min under a nitrogen atmosphere to prevent the oxidation of the titanium precursor. The hydrolysis process was then performed by adding water to the precursor/solvent solution, and this new solution was stirred for 1 h. For the Ag-modified TiO₂, the precursor AgNO₃ (J.T. Baker, Phillipsburg, NJ, USA) was used. For the Fe-TiO₂, the precursor was FeSO₄∙7H₂O (J.T. Baker, Phillipsburg, NJ, USA); for the Cu-TiO₂, the precursor was CuSO₄∙5H₂O (J.T. Baker, Phillipsburg, NJ, USA). These compounds were added by dissolving them into the water used for the hydrolysis in a 0.1%w. The obtained product was dried at room temperature and then calcined at 450 °C for 3 h to promote the anatase crystal phase. For this synthesis, the materials were identified as sol–gel (SG) materials. The TiO₂ samples synthesized with the microwave-assisted sol–gel method were prepared using the sol obtained after hydrolysis. It was transferred into a Teflon vessel and placed on a microwave reaction system (Flexiwave Milestone). The process was carried out at a temperature of 180 °C for 30 min. Once the product was obtained, it was filtered, dried, and calcined at 450 °C for 3 h [20]. For this synthesis, the materials were identified as microwave-assisted sol–gel (Mw-SG) materials.

Morphology and elemental analysis were carried out using a JEOL JSM-6060 LV scanning electron microscope (SEM) operating at a voltage of 15 keV. Elemental analysis was performed by Energy-Dispersive X-ray Spectroscopy (EDS Oxford Inca X-Sight coupled to a MT 1000, Hitachi). The crystallinity was determined by X-ray Diffraction analysis (XRD) using a Bruker D8 advanced diffractometer equipped with a Cu seal tube to generate Cu Kα radiation (λ = 1.5406 Å) with angles of 10 < 2θ < 80° in a pitch of 0.01°; Raman analysis was conducted using a LabRam HR Horiba Scientific with a NdYGa (λ = 532 nm).

4.2. Nanoparticle Crystallite Size

The crystallite size for both synthesis methods was calculated using the Scherrer equation shown in Equation (1), where (D) is the diameter of crystallite, (λ) is the X-ray wavelength, (k) is the Scherrer constant, and (β) is the full width at half maximum obtained from the diffraction signals in the XRD pattern and (θ) the peak position.

$$D=\frac{k\lambda }{\beta cos cos \theta }$$

(1)

The crystallite size was also determined using the Williamson–Hall equation (Equation (2)), which considers the structural stress of the crystallite. The equation represents a straight line where (ε) is the slope that provides the strain of the crystallite.

$$\beta cos cos \theta =\epsilon \left(4 sin sin \theta \right)+\frac{k\lambda }{D}$$

(2)

4.3. Plant Harvest and Growth Parameters

Alfalfa seeds (Medicago sativa L.) were purchased from a local distributor: Hortaflor, Mexico. Seeds were placed in seedbeds using peat moss substrate (Jiffy) with a pH of 5.8, electrical conductivity of 0.4 mS/cm, a moisture fraction of 15%, and particle size of <10 mm inside a plasticized greenhouse of 68 × 49 × 156 cm in length, breadth, and height, respectively. Three replicates with a population of six crops (one crop per container) were maintained during development for the experiment. Sprouts were kept in seedbeds (2.5 × 2.5 × 6.5 cm) for 15 days before being transferred to plastic containers of 500 mL (11.6 cm in height, and 9.5 cm and 6.8 cm in the top and bottom diameter, respectively) using a peat moss substrate. Sprouts were treated by direct soil irrigation with 5 mL solutions of 50, 100, and 500 ppm of TiO₂ and M-TiO₂ (M = Ag, Cu, Fe) with no nutritive solution. When transferred to the 500 mL containers, each plant was irrigated with 50 mL of their respective solution of NMs until completing 80 days of treatment.

Plants were randomly selected for morphological analysis; first and second-appearing stem lengths were measured using a ruler. For leaf length, 3 of the visually biggest trifoliate were chosen in each plant, and their length was measured using a digital caliper, as seen in Figure 9. After harvest, the samples were divided into leaves, stems, and roots immersed in liquid nitrogen to prevent any chemical structural change for future tests. Then, the samples were milled and kept under refrigeration at −20 °C for further metabolomics quantification assays. The greenhouse temperature was recorded using a hygrometer (YASSUN), obtaining temperature and humidity values at midday. Climatic data were taken from the geo-electromagnetic center of the National Autonomous University of Mexico, Juriquilla campus (longitude: 100°26′48.81″ W, latitude: 20°42′14.87″ N) [40] at noon each day. The atmospheric data obtained during the growth period can be visualized in Table S17, present in the supplementary data.

4.4. Total Phenol and Flavonoid Quantification

For extract elaboration, 1 g of the fresh frozen sample was weighed and placed in falcon tubes with 10 mL of methanol. The extract was then agitated for 24 h in complete darkness. The extract was then separated and used for total phenol and flavonoid quantification. Total phenolic content was determined according to the Folin–Ciocalteu spectrophotometric method [41] modified for a 96-well microplate. Total phenol content results were expressed as equivalent mg of gallic acid (eq. mg) per gram of fresh sample. Total flavonoid content was expressed as equivalent mg of rutin per gram of fresh sample and was determined by the 2-aminoethyl-diphenyl borate reagent method [42].

4.4.1. Antioxidant Activity

The extracts’ antioxidant activity was evaluated with the 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical method [43], and the results were expressed as the percentage of DPPH discoloration (% radical inhibition) named as well as percentage inhibition (IHB), which was calculated with Equation (3).

$$IHB\%=\left(\frac{A_{DPPH}-A_S}{A_{DPPH}}\right)\times 100$$

(3)

where (A_S) is the absorbance of the solution containing the sample, and (A_DPPH) is the DPPH solution’s absorbance. All the spectrophotometric measurements were obtained in a Thermo Scientific Multiskan Go spectrophotometer.

4.4.2. Chlorophyll Index

The chlorophyll index was quantified using a SPAD 502 Plus Chlorophyll Meter from Minolta Co., Ltd. SPAD values were determined for plants in each treatment group [44,45] taking three readings per plant for an average amount per plant.

4.5. Data Analysis

Statistical analysis was performed using the software GraphPad Prism 8 (8.0.2, Dotmatics, San Diego, CA, USA). An ANOVA test was used for morphological and total phenol and flavonoid analysis, and a significant statistical difference was determined using a Dunnett and a Tukey pairwise comparison. The data significance value was p ≤ 0.05 in all the analyses.

5. Conclusions

The presence of TiO₂ NMs through constant soil irrigation caused a distressing effect on alfalfa crops, resulting in lower growth rate, alterations in chlorophyll index micronutrient uptake, and increased metabolic profiles. Synthesis pathway modification and different doping elements led to changes in NM characteristics, leading to different metabolomic and morphological alteration levels as proven by the two-way ANOVA analysis. The results suggest that the stress level is influenced by the physicochemical properties of NMs obtained through their synthesis pathway, so TiO₂-alfalfa interactions with different contents of dopants, exposure, and concentration times are needed to understand the interaction mechanisms and improve TiO₂ usage as an effective material for crop improvement.

Apart from the distressing effects caused by TiO₂, NM’s interaction with alfalfa in its multiple presentations caused an outcome in which the secondary metabolite content of total phenols was increased in several treatments, indicating that nanomaterials cause a disruptive interaction with alfalfa crops. The statistical analysis showed that factors such as the dopant, the synthesis method, and the interaction of both significantly influenced parameters such as the chlorophyll index, stem size, % inhibition, and total phenol content (root). Parameters such as the chlorophyll index and stem size showed differences in the types of nanomaterials, generating a decrease in said parameters. However, a significant influence of NMs synthesized by Mw-SG on the total content of phenols and % of inhibition was verified, indicating a modification in the characteristics of the NMs due to the synthesis method, increasing the interaction of said NMs with alfalfa and increasing said parameters.

Further assays for determining NM uptake by roots and translocation are required to address safety concerns related to NMs’ fate through the food chain. Additionally, a deeper understanding of the dose–response models (lineal, threshold, and hormesis) involved in the NP-alfalfa interaction may help understand other toxicological behavior of TiO₂ and other photocatalytic nanomaterials.