Application of graphene oxide in improving quality of tea trees

Abstract

The invention belongs to the technical field of biological agriculture, and particularly discloses application of graphene oxide to improvement of tea tree quality. According to the application of the graphene oxide in improving the quality of the tea tree, the tea tree is treated through the graphene oxide with an effective dose, the rhizosphere and phyllosphere microflora structures of the tea tree are adjusted, and then the quality is improved. The invention discloses application of graphene oxide to improvement of tea tree quality. The diversity and structure of tea tree rhizosphere microorganisms can be remarkably changed, catechin substances and special flavor substances are improved, the health function value of tea leaves is enhanced, astringency, freshness, acid and bitter balance of the tea leaves is optimized, and a mellow and harmonious unique flavor is formed.

Description

Technical Field

[0001] This invention belongs to the field of bio-agricultural technology, specifically relating to the application of graphene oxide in improving the quality of tea trees. Background Technology

[0002] Tea, a globally popular beverage, owes its flavor profile and health benefits to the rich bioactive components contained in its leaves, such as catechins, theanine, and theanalgin. The proportions and metabolic dynamics of these substances are core factors determining the sensory quality and health benefits of tea. Therefore, achieving precise and safe regulation of the tea plant's metabolic network to promote the efficient accumulation of beneficial flavor substances and health-promoting active ingredients has become a key issue continuously explored by the modern tea industry. Currently, the main cultivation techniques for improving tea quality revolve around optimizing fertilization strategies, meticulous water management, scientific shading regulation, and the application of exogenous plant growth regulators. While traditional agronomic practices play a role in maintaining the basic growth of tea plants, their effect on the targeted accumulation of key secondary metabolites is often limited, making it difficult to meet the specific requirements of high-quality tea production for flavor and functional components. Meanwhile, while the application of chemically synthesized plant growth regulators can influence plant physiological processes to some extent, the potential problems that arise cannot be ignored: their residue risks in tea may raise food safety concerns; long-term use is increasingly putting pressure on the soil microecology and surrounding environment of tea gardens, which also contradicts consumers' general expectations for natural, green, and sustainable agricultural products. Therefore, developing new quality control technologies that are environmentally friendly, have clear targets, and have well-defined mechanisms of action has become an urgent need for industrial upgrading.

[0003] The systemic regulatory effects of graphene oxide on the physiological metabolic network of important economic crops like tea, particularly on key flavor and health components of tea, remain unclear. Furthermore, the rhizosphere and phyllosphere microbial communities, acting as a second genome, have a profound impact on nutrient absorption, healthy growth, and the synthesis of secondary metabolites in plants. Whether graphene oxide can indirectly affect tea quality by regulating the structure of tea-related microbial communities remains unknown, severely limiting its precise application in the safe production of high-quality tea. Summary of the Invention

[0004] This invention aims to provide the application of graphene oxide in improving the quality of tea trees. It can significantly change the diversity and structure of rhizosphere microorganisms in tea trees, enhance catechins and characteristic flavor substances, improve the health function value of tea, optimize the balance of astringency, freshness, sourness and bitterness in tea, and create a more mellow and harmonious unique flavor.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: Application of graphene oxide in improving the quality of tea leaves from tea plants.

[0006] Preferably, treating tea trees with an effective dose of graphene oxide increases the content of flavor compounds in tea leaves.

[0007] Preferably, the flavor compounds include one or more of the following: kaempferol, quercetin, hesperidin, epigallocatechin gallate, theobromine, theaflavin-3-gallate, quinic acid, betaine, serine, L-arginine, theagallin, and gallic acid.

[0008] Preferably, the application is to regulate the structure of the rhizosphere microbial community of tea trees.

[0009] The present invention also provides a method for improving tea quality, comprising the following steps: The tea trees were treated with a root irrigation using a graphene oxide solution.

[0010] Preferably, the concentration of the graphene oxide solution is 20-30 mg / L.

[0011] Compared with the prior art, the present invention has the following advantages and technical effects: This invention discloses the application of graphene oxide in improving tea quality. For the first time, low-concentration graphene oxide is applied to a tea cultivation system, revealing its mechanism of action from a microbial ecology perspective. Experimental results show that graphene oxide can significantly alter the diversity and structure of the rhizosphere microorganisms in tea trees, particularly by specifically enriching beneficial bacteria—actinomycetes—with a relative abundance increase of over 5.8%. Actinomycetes are known to possess various probiotic functions, including antibiotic production, pathogen inhibition, and nutrient decomposition promotion. This optimized microecological structure provides a favorable environment for the healthy growth and disease resistance of tea trees. Furthermore, it significantly enhances catechins and characteristic flavor compounds, strengthening the health benefits of tea and optimizing the balance of astringency, freshness, acidity, and bitterness, resulting in a more mellow and harmonious unique flavor.

[0012] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0013] Figure 1 This is a demonstration diagram of the experiment's operation; Figure 2 The results are from a metabolomics analysis, in which... Figure 2 In the figure, A represents the sample correlation heatmap. Figure 2 The B in the figure is the principal component analysis (PCA) score plot; Figure 3 Classification and quantity of differentially accumulated metabolites (DAMs); Figure 4Classification and statistics of KEGG pathways; Figure 5 For differential metabolite analysis, among which, Figure 5 In the diagram, A represents the volcano plot of differentially expressed metabolites between the CKL (control group) and L25 groups. Figure 5 B in the diagram represents the volcano plot of differential metabolites between the CKL and L50 groups. Figure 5 C in the figure represents the volcano diagram of differential metabolites between the CKL and L100 groups. Figure 5 In the diagram, D represents the KEGG pathway enrichment analysis of differentially metabolites between CKL and L25. Figure 5 In the figure, E represents the KEGG pathway enrichment analysis of differential metabolites between CKL and L50. Figure 5 F in the figure represents the KEGG pathway enrichment analysis of differential metabolites between CKL and L100; Figure 6 For the distribution of differentially accumulated metabolites, among which, Figure 6 In the diagram, A is a Venn diagram showing the distribution of differentially accumulated metabolites (DAMs) in the three comparison groups; Figure 6 B in the table represents the KEGG pathway enrichment analysis of the common DAM (793 metabolites) identified in all three comparison groups. Figure 7 To analyze the content of flavor compounds in large-leaf tea under different graphene treatments, among which... Figure 7 A in the text represents kaempferol. Figure 7 B in the formula represents quercetin. Figure 7 The C in the formula is hesperidin. Figure 7 In this context, D stands for epigallocatechin gallate. Figure 7 E in the formula stands for theobromine. Figure 7 F in the text stands for theaflavin-3-gallate. Figure 7 G in the text stands for quinic acid. Figure 7 H in it stands for betaine. Figure 7 The "I" in the formula stands for serinelleucine. Figure 7 J in the text stands for L-arginine. Figure 7 K in the text represents tangalcin. Figure 7 L in the figure represents gallic acid; Figure 8 For rhizosphere microbiome analysis, among which, Figure 8 In the chart, A represents the OTU quantity statistics. Figure 8 In the figure, B is a nonmetric multidimensional scaling (NMDS) diagram based on the composition of rhizosphere soil microbial communities. Figure 8 C in the figure represents the Chao1 index box plot. Figure 8 D in the figure represents the ACE index box plot. Figure 8 E in the figure represents the Simpson exponent box plot. Figure 8 F in the figure represents the Shannon index box plot; Figure 9 16S rRNA gene sequencing analysis of the rhizosphere soil microbial community structure and functional pathways of Camellia japonica under different graphene treatments was conducted. Figure 9 In the graph, A represents a stacked bar chart showing the relative abundance of bacteria in the rhizosphere soil of Camellia plants under different graphene treatments. Figure 9 B in the graph represents a stacked bar chart showing the relative abundance of bacteria in the rhizosphere soil of Camellia plants under different graphene treatments. Figure 9 C in the figure represents the functional pathway analysis of the microbial community between the CKR group and the R25 group. Figure 9 In the diagram, D represents the functional pathway analysis of the microbial community between the CKR group and the R50 group. Figure 9 E in the figure represents the functional pathway analysis of the microbial community between the CKR group and the R100 group. Detailed Implementation

[0014] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0015] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.

[0016] Source of experimental materials: Test plants: Hainan large-leaf tea with uniform growth status.

[0017] Test material: Black graphene oxide powder, with 5-10 layers and a sheet diameter of 10-50 mm. m, purity >90%.

[0018] The basic nutrient solution formula is shown in Table 1 below.

[0019] Table 1 Basic Nutrient Solution Formula

[0020] In this invention, unless otherwise specified, all other test materials and instruments are conventional test materials in the field and can be purchased through commercial channels.

[0021] Example 1 This experiment used pot experiments to investigate the regulatory effect of graphene oxide on the growth characteristics and physiological metabolic network of large-leaf tea.

[0022] Graphene oxide powder was added to the basic nutrient solution, and after thorough stirring and ultrasonic dispersion, graphene oxide nutrient solutions with concentrations of 25 mg / L, 50 mg / L and 100 mg / L were prepared respectively.

[0023] Experimental design: A control group (CK, basal nutrient solution) and a graphene oxide treatment group (graphene oxide nutrient solution) were set up, with 10 biological replicates in each group, arranged completely randomly.

[0024] Experimental design: A control group (CKL, watered with an equal amount of basic nutrient solution) and a graphene oxide treatment group (GO, watered with 25 mg / L, 50 mg / L and 100 mg / L graphene oxide treatment solution, respectively) were set up. Each group had 10 biological replicates, arranged in a completely randomized manner.

[0025] Treatment Procedure: Every 15 days, perform a root drenching treatment on the tea bushes, applying 200 mL of the appropriate solution to each pot at a uniform rate. During routine management, supplement with deionized water as needed based on soil moisture to maintain suitable humidity and ensure a consistent growing environment for all tea bushes. A total of three treatments are performed. A schematic diagram of the treatment procedure is shown below. Figure 1 As shown.

[0026] Samples were collected 7 days after the final processing.

[0027] Root soil samples: Carefully shake off the rhizosphere soil that is tightly bound to the tea tree roots, collect it in sterile centrifuge tubes, quickly freeze it in liquid nitrogen, and then transfer it to an ultra-low temperature freezer at -80°C for storage, for subsequent extraction of microbial community DNA.

[0028] Leaf samples: Fresh leaf samples from the first to third tender leaves at the tip of each tea tree were collected, flash-frozen in liquid nitrogen, and stored at -80℃ for metabolite extraction.

[0029] The samples collected from the above groups were processed as follows: 1. High-throughput sequencing of microbial communities: Collected samples underwent DNA extraction and high-throughput sequencing. Soil genomic DNA was extracted using the TGuide S96 Magnetic Soil / Foulte DNA Kit (Tiangen Biotech (Beijing) Co., Ltd.) according to the manufacturer's instructions. The V3+V4 regions of the V1-V9 hypervariable region of the 16S rRNA gene were amplified using primers (338F: ACTCCTACGGGAGGCAGCA; 806R: GGACTACHVGGGTWTCTAAT). The amplified products were examined for integrity using agarose gel electrophoresis and used for Illumina library construction. The constructed libraries were then sequenced using an Illumina novaseq 6000.

[0030] 2. Bioinformatics analysis: Qualifying sequences with a similarity threshold exceeding 97% were assigned to an Operational Taxonomic Unit (OTU) using USEARCH (version 10.0). OTU / ASV classification annotations were based on the Naive Bayes classifier in QIIME2 using the SILVA database with a confidence threshold of 70%. Alpha analysis was performed using QIIME2 software to determine the species diversity complexity of each sample. Principal Coordinate Analysis (PCoA) was also performed. Diversity calculations were performed to assess the diversity of species complexity in the sample. One-way ANOVA was used to compare bacterial abundance and diversity. Linear discriminant analysis (LDA) and effect size (LEfSe) were used to assess differentially abundant taxa. Sequencing data were analyzed using the online platform BMKCloud (https: / / www.biocloud.net).

[0031] 3. Preparation of metabolite extracts and quality control of samples: After vacuum freeze-drying, the sample was ground into powder using a grinder. The powder was dissolved in pre-cooled extraction buffer (methanol:water volume ratio = 3:1, containing 1-2-chlorophenylalanine), vortexed for 30 seconds, ground for 4 minutes, sonicated in an ice-water bath (40 Hz, repeated 3 times) for 5 minutes, and centrifuged at 13800 × g for 15 minutes at 4°C. The supernatant was then collected and mixed to obtain the quality control sample. The supernatant was vacuum dried to obtain the extract, which was then added to 40... Mix L-methoxyamine reagent (methoxyamine hydrochloride dissolved in 20 mg / ml pyridine) thoroughly and incubate at 80°C for 30 min. Then add 60 mg / ml of the reagent to the sample. L-trifluoroacetamide (containing 1% trimethylchlorosilane) was incubated at 70°C for 1.5 h, cooled to room temperature, and then 5% trifluoroacetamide was added. L-saturated fatty acid methyl esters were used for subsequent ultra-high performance liquid chromatography and tandem mass spectrometry for substance identification of the samples.

[0032] 4. High-performance liquid chromatography (HPLC) data acquisition conditions: Gas chromatography and time-of-flight mass spectrometry were used with a DB-5MS capillary column (30m × 250). m×0.25 m) Gas chromatography-time offlight-mass spectrometry (GC-TOF-MS) was performed on the collected samples. The operation was carried out according to the parameters set by Shanghai Baiqu Biomedical Technology Co., Ltd.

[0033] 5. Sample quality control: Mass spectrometry data were analyzed using ChromaTOF v4.3x software. The LECO-Fiehn Rtx5 database was used for qualitative analysis (mass spectrometry matching and retention time index matching). Finally, the standards (quality control) were subjected to quality control, with peaks below 50% detection rate or with a relative standard deviation of peak area >30% being removed.

[0034] Data preprocessing and data analysis: By processing the raw data (processing methods: deviation filtering, missing value filtering, missing value imputation, and standardization), the impact of systematic errors was reduced, highlighting the biological significance. The data were logarithmically transformed and centered using SIMCA v16.0.2 software, followed by principal component analysis (PCA). Differential metabolites were screened using a t-test p-value < 0.05. The quantitative relationships of differential metabolites between groups were displayed using Venn diagrams, and the overall distribution of differential metabolites was visualized using volcano plots. Differential metabolites in each group were compared, and the obtained differential metabolites were standardized using z-scores and subjected to K-means cluster analysis. Cluster analysis was performed on differential metabolites based on their quantitative values, and the results were visualized using heatmaps, clearly showing the accumulation of differential metabolites. Pathway annotation of differential metabolites was performed on the KEGG website (https: / / www.genome.jp / kegg / pathway.html), resulting in a metabolic pathway enrichment map of differential metabolites. The results are as follows: Figures 2-7 As shown.

[0035] To assess experimental stability, correlation analysis was performed on all samples. The Pearson correlation coefficients among all quality control samples exceeded 0.9, confirming the high stability of the analysis and the reliability of the data. Figure 2 (A) Biological replication analysis showed that the correlation coefficients within each treatment group were significantly higher than those between groups, indicating that GO treatment induced significant and reproducible changes in the metabolomic profile of tea plants. Principal component analysis was used to visualize the overall metabolic differences. PCA score plots showed that PC1 and PC2 explained 21.1% and 32.5% of the total variance, respectively. Furthermore, there was significant separation among the four treatment groups, with the control group showing tight clustering, while the three GO treatment groups exhibited a concentration-dependent gradient distribution along the PC1 axis. Figure 2 (B in the text). This indicates that GO treatment is the main factor driving systemic, concentration-dependent changes in leaf metabolites. The minimum overlap of the confidence ellipses further supports the significant metabolic differences between groups.

[0036] Based on annotations of the top 20 detected metabolites from the HMDB database, isoprene lipids were identified as the most abundant category, containing 118 metabolites. Figure 3 The second largest category is organic oxygen compounds, with 104 metabolites. In addition, 82 carboxylic acids and their derivatives were detected, which are key intermediates in primary and secondary metabolism. Notably, 78 flavonoids and 69 fatty acyl groups were identified. Numerous benzenes and their substituted derivatives (66), coumarins and their derivatives (41), and steroids and their derivatives (32) were also detected. These findings reflect a comprehensive reprogramming of the tea plant metabolome in response to GO exposure.

[0037] KEGG pathway annotation was performed on all identified metabolites to provide functional context. Figure 4 In amino acid metabolism, tryptophan metabolism (9 metabolites, 3.32%), arginine and proline metabolism (7 metabolites, 2.58%), and phenylalanine metabolism (6 metabolites, 2.21%) pathways were enriched, indicating that graphene regulates the biosynthesis and catabolism of amino acids. The biosynthesis of other secondary metabolites was highly enriched, particularly in the biosynthesis of various plant secondary metabolites (18 metabolites, 6.64%), flavonoid biosynthesis (14 metabolites, 5.17%), and phenylpropanoid biosynthesis (13 metabolites, 4.80%), confirming the strong promoting effect of graphene on secondary metabolite biosynthesis. Other pathways, including membrane transport (ABC transporters, 18 metabolites, 6.64%), nucleotide metabolism (purine metabolism, 17 metabolites, 6.27%), and lipid metabolism (α-linolenic acid metabolism, 7 metabolites, 2.58%), were also significantly enriched, indicating that graphene affects multiple physiological processes.

[0038] A comprehensive analysis of GO-responsive differential metabolites: Through non-targeted metabolomics analysis of tea plants treated with GO, we systematically identified differentially induced metabolites at different concentrations. Based on the criteria of Padj < 0.01, VIP > 1, and |log2FC| > 1, a total of 1785 significantly differentially induced metabolites were identified in the 25 mg / L GO treatment group, including 805 upregulated and 980 downregulated metabolites. Figure 5 (A) As the concentration increased, the number of differentially expressed metabolites in the 50 mg / L treatment group further increased to 2213 (1061 upregulated and 1152 downregulated), while the 100 mg / L treatment group had a total of 1944 differentially expressed metabolites (866 upregulated and 1078 downregulated). Figure 5 B and Figure 5 (C in the text). The number and regulatory patterns of these differential metabolites exhibit a clear gradient across different concentrations, indicating that GO induces a significant concentration-dependent metabolic response, with high concentrations triggering unique metabolic reprogramming.

[0039] Further KEGG pathway enrichment analysis revealed the metabolic pathways involved. Figure 5 D and Figure 5 (F in the text). In the 25 mg / L treatment, metabolites were mainly enriched in amino acid metabolic pathways, such as the biosynthesis of phenylalanine, tyrosine, and tryptophan (4 metabolites, 5.13%), phenylalanine metabolism (3 metabolites, 3.85%), and arginine and proline metabolism (2 metabolites, 2.58%). In secondary metabolism, the biosynthesis of various plant secondary metabolites (9 metabolites, 11.54%) and flavonoid biosynthesis (7 metabolites, 8.97%) were prominent. In addition, ABC transporters (5 metabolites, 6.41%) and purine metabolism (3 metabolites, 3.85%) were also common enrichment pathways. The 50 mg / L treatment had a more significant impact on histidine metabolism (2 metabolites, 4.40%), flavonoid and flavonol biosynthesis (7 metabolites, 7.69%), and galactose metabolism (3 metabolites, 3.30%). High-concentration treatment (100 mg / L) specifically enriched arginine and proline metabolism (2 types, 2.56%) and glycine, serine, and threonine metabolism (2 types, 2.56%), while flavonoid biosynthesis (5 types, 6.41%) and ABC transporter proteins (5 types, 6.41%) remained co-regulated. These results indicate that graphene regulates amino acid metabolism, secondary metabolite biosynthesis, and basal energy metabolism in tea plants in a concentration-dependent manner.

[0040] Depend on Figure 6 As shown in section A, 793 DAMs were shared across the three comparison groups (CKL vs L25, CKL vs L50, and CKL vs L100), constituting a core group of metabolites continuously affected by graphene treatment. Furthermore, 342, 550, and 441 unique DAMs were identified in the CKL vs L25, CKL vs L50, and CKL vs L100 comparisons, respectively, indicating that graphene not only induced shared metabolic responses but also triggered significant concentration-specific metabolic reprogramming.

[0041] Depend on Figure 6As shown in section B, in amino acid metabolism, pathways such as the biosynthesis of phenylalanine, tyrosine, and tryptophan (2 metabolites, 5.26%) and the degradation of valine, leucine, and isoleucine (2 metabolites, 5.26%) were significantly enriched. Among the biosynthesis of other secondary metabolites, the biosynthesis of multiple plant secondary metabolites (5 metabolites, 13.16%) and the biosynthesis of flavonoids (4 metabolites, 10.53%) were the most significantly enriched. Other significantly enriched pathways included glyoxylate and dicarboxylic acid metabolism in carbohydrate metabolism (2 metabolites, 5.26%), ABC transporters in membrane transport (3 metabolites, 7.89%), and pantothenic acid and CoA biosynthesis in cofactor and vitamin metabolism (2 metabolites, 5.26%). These findings collectively indicate that graphene treatment systematically regulates amino acid metabolism, secondary metabolite biosynthesis, and energy and cofactor metabolism in tea plants, exhibiting metabolic effects that are both common and concentration-specific.

[0042] Further analysis of 13 key flavor-related metabolites showed that different concentrations of GO significantly and dose-dependently regulated the accumulation of these compounds in tea plants, with distinct response patterns observed among different metabolite classes. Figure 7 Among flavonoids, kaempferol, quercetin, hesperidin, and epigallocatechin gallate generally showed a trend of promoting accumulation at low to medium concentrations and decreasing at high concentrations. Specifically, kaempferol reached its peak at L50, with levels significantly higher than the control and L100 groups. Quercetin increased sharply at L25, then gradually decreased with increasing concentration, but levels at L50 and L100 were still significantly higher than the control group. Hesperidin showed no significant difference between L25 and L50, but both were significantly higher than the control and L100 groups. EGCG reached its highest level at L25 and decreased with increasing concentration, but levels in all treatment groups were still significantly higher than the control group. Alkaloids showed different response patterns: theobromine accumulated most significantly at L50, reaching its peak level. Theaflavins-3-gallate increased significantly at L25, decreased slightly at L50, but increased again at L100, with all treatment groups significantly higher than the control group. The responses of organic acids and amino acids were more complex: quinic acid remained at high levels at L25 and L50, but decreased significantly at L100. Betaine, serine, and L-arginine all peaked at L25 and decreased with increasing concentration; among them, L-arginine at L50 was still significantly higher than the control group, while serine and betaine, although decreasing at L50 and L100, were still higher than the control group. Tea gallin increased at L25, but gradually decreased at L50 and L100, with the L100 group significantly lower than the L25 and L50 groups. In contrast, gallic acid increased significantly at L100, reaching the highest level among all treatments.

[0043] Graphene treatment profoundly affected the microbial community of the rhizosphere soil of tea trees. Regarding the number of microbial features and community structure, the control group (CKR) had 5479 features, significantly lower than the graphene-treated group (R25: 8910; R50: 7898; R100: 7694). The total number of features for all samples exceeded 26554, indicating that graphene treatment significantly increased the detectable microbial taxa and may have promoted the expansion of microbial species richness. Figure 8 (A in the original text). The stress value of the nonmetric multidimensional scaling analysis was 0.1101, confirming the reliability of the ordination. The clear clustering and separation among the CKR, R25, R50, and R100 groups are visually apparent. Figure 8 (B in the middle).

[0044] Regarding microbial richness, the Chao1 index showed an average of 2202.87 in the CKR group, which significantly increased to an average of 3380.12 in the R25 group. The average for the R50 group was 3018.05, while the average for the R100 group decreased to 2962.65. Figure 8 (C) The ACE index followed a highly consistent trend: the average R25 group was 2214.04, increasing to an average of 3393.99, the average R50 group was 3033.16, and the average R100 group decreased to an average of 2974.08 (p<0.05 for all group comparisons). Figure 8 (D in the text). These results indicate that 25 mg / L graphene had the strongest promoting effect on rhizosphere microbial richness, and the richness tended to decrease when the concentration increased to 50 and 100 mg / L. Regarding microbial diversity, the average Simpson index of the CKR group was 0.9930, increasing to 0.9953 in the R25 group, remaining at 0.9961 in the R50 group, and slightly decreasing to 0.9952 in the R100 group. Figure 8 The Shannon index showed more pronounced changes: the average R25 in the CKR group was 9.03, which increased significantly to 9.91 in the CKR25 group, the average R50 in the CKR50 group was 9.72, and the average R100 in the CKR100 group decreased to 9.51 (p < 0.05 for all groups). Figure 8 (F in the figure). This indicates that graphene treatment enhances the overall diversity of rhizosphere microorganisms, with the most significant effect at 25 mg / L, while diversity decreases at higher concentrations, consistent with the observed richness trend.

[0045] Regarding the microbial taxonomic composition, phylum-level analysis showed that Proteobacteria was the most dominant phylum across all treatment groups, with relative abundances of CKR: 23.57%, R25: 21.17%, R50: 23.95%, and R100: 31.71%. Figure 9The relative abundances of the phylum Acidobacteria were 23.74%, 19.21%, 22.66%, and 17.41%, respectively; those of the phylum Chlorophytum were 11.71%, 11.19%, 9.47%, and 8.30%, respectively; and those of the phylum Actinobacteria were 7.37%, 13.16%, 8.83%, and 8.08%, respectively. These results indicate significant differences in the relative abundance of each phylum among the treatment groups. For example, compared with CKR, the relative abundance of Actinobacteria increased in group R25, while the abundance of some phyla fluctuated in group R100, reflecting the concentration-dependent regulatory effect of graphene on the phylum-level community structure. Genus-level analysis revealed more subtle compositional differences (…). Figure 9 (B) The relative abundance of genera such as uncultured bacteria *f. norank*, *Candidatus*, *Koribacter*, and *Rhodanobacter* showed concentration-dependent changes in the CKR, R25, R50, and R100 groups. For example, the abundance of *Pseudomonas* was significantly higher in the R25 group than in other groups, while some uncultured genera showed unique changes in the R100 group, further indicating that graphene's regulation of rhizosphere microbial communities is specific at a finer taxonomic scale.

[0046] In the functional pathway difference analysis, significant differences were observed in tertiary pathways, including "metabolic pathways" (relative abundance: CKR 16.440%, R25 16.353%), "biosynthesis of secondary metabolites" (CKR 7.834%, R25 7.787%), "microbial metabolism in different environments" (CKR 4.416%, R25 4.517%), and "ABC transporters" (adjusted p<0.05). The R25 group generally exhibited higher metabolic potential in these pathways, indicating that 25 mg / L graphene significantly enhanced the overall metabolic activity, secondary metabolite synthesis capacity, and substance transport function of the rhizosphere microbial community. Figure 9 C). In the CKR vs R50 comparison, pathways such as "microbial metabolism in different environments" (CKR 4.416%, R50 4.458%), "antibiotic biosynthesis" (CKR 5.826%, R50 5.790%), "secondary metabolite biosynthesis" (CKR 7.834%, R50 7.804%), and "ABC transporter proteins" also showed significant differences (adjusted p<0.05). However, their enrichment patterns and relative abundance differed from the R25 group, reflecting the unique regulatory effect of 50 mg / L graphene on microbial function (C). Figure 9(D in the original text). In the CKR vs R100 comparison, pathways including "metabolic pathways" (CKR 16.440%, R100 16.499%), "biosynthesis of secondary metabolites" (CKR 7.834%, R100 7.709%), and "antibiotic biosynthesis" (CKR 5.826%, R100 5.670%) still showed significant differences (adjusted p<0.05). Figure 9 (E in the group). However, the metabolic potential of some pathways was lower than that of the R25 and R50 groups, indicating that the promoting effect of 100 mg / L graphene on microbial function was weakened, and it exhibited a different functional regulatory pattern compared with the lower concentration group.

[0047] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. Application of graphene oxide in improving the quality of tea leaves from tea plants.

2. Use according to claim 1, characterized in that, Treating tea trees with an effective dose of graphene oxide can increase the content of flavor compounds in tea leaves.

3. Use according to claim 2, characterized in that, The flavor compounds include one or more of the following: kaempferol, quercetin, hesperidin, epigallocatechin gallate, theobromine, theaflavin-3-gallate, quinic acid, betaine, serine, L-arginine, theagallin, and gallic acid.

4. The application according to any one of claims 1 to 3, characterized in that, The application is to regulate the structure of the rhizosphere and / or phyllosphere microbial community of tea trees.

5. A method for improving tea quality, characterized in that, Includes the following steps: The tea trees were treated with a root irrigation using a graphene oxide solution.

6. The method according to claim 5, characterized in that, The concentration of the graphene oxide solution is 20-30 mg / L.

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