About

Xiaotong Li is an assistant professor in the Department of Chemistry at North Carolina State University, having joined the faculty in January 2024. She obtained her B.S. in chemistry from Nankai University in Tianjin, China, in 2016, and earned her Ph.D. in chemistry from Northwestern University in 2021 under the supervision of Prof. Mercouri Kanatzidis. Her doctoral research focused on structure-property relationships of 2D halide perovskites. Following her Ph.D., she conducted postdoctoral research at the California Institute of Technology with Prof. Kimberly See, working on cathode materials for Li and Na-ion batteries. Her research group primarily focuses on the synthesis and structural determination of new hybrid and inorganic materials for energy-related applications. She is particularly interested in designing new halide perovskites and hybrid organic-inorganic metal halides, as well as studying their structure-property relationships.

Research topics

• Medicine
• Biology
• Computational biology
• Materials science
• Computer Science
• Composite material
• Genetics
• Evolutionary biology
• Ecology
• Cell biology
• Optoelectronics
• Pathology
• Environmental health
• Cartography
• Anatomy
• Neuroscience
• Bioinformatics
• Biochemistry
• Geography

Selected publications

• Magnetic dendritic Fe2O3@PPy composites for enhanced electromagnetic wave absorption

  Surfaces and Interfaces · 2026-03-11

  article
  PublisherDOI

• Inversion of SPAD Values of Pear Leaves at Different Growth Stages Based on Machine Learning and Sentinel-2 Remote Sensing Data

  Agriculture · 2025-06-11 · 4 citations

  articleOpen accessSenior authorCorresponding

  Chlorophyll content is a critical indicator of the physiological status and fruit quality of pear trees, with Soil Plant Analysis Development (SPAD) values serving as an effective proxy due to their advantages in rapid and non-destructive acquisition. However, current remote sensing-based SPAD retrieval studies are primarily limited to single phenological stages or rely on a narrow set of input features, lacking systematic exploration of multi-temporal feature fusion and comparative model analysis. In this study, pear leaves were selected as the research object, and Sentinel-2 remote sensing data combined with in situ SPAD measurements were used to conduct a comprehensive retrieval study across multiple growth stages, including flowering, fruit-setting, fruit enlargement, and maturity. First, spectral reflectance and representative vegetation indices were extracted and subjected to Pearson correlation analysis to construct three input feature schemes. Subsequently, four machine learning algorithms—K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machine (SVM), and an Optimized Integrated Algorithm (OIA)—were employed to develop SPAD retrieval models, and the performance differences across various input combinations and models were systematically evaluated. The results demonstrated that (1) both spectral reflectance and vegetation indices exhibited significant correlations with SPAD values, indicating strong retrieval potential; (2) the OIA model consistently outperformed individual algorithms, achieving the highest accuracy when using the combined feature scheme; (3) among the phenological stages, the fruit-enlargement stage yielded the best retrieval performance, with R2 values of 0.740 and 0.724 for the training and validation sets, respectively. This study establishes a robust SPAD retrieval framework that integrates multi-source features and multiple models, enhancing prediction accuracy across different growth stages and providing technical support for intelligent orchard monitoring and precision management.

  PublisherOA PDFDOI

• Carbonization Tuned Core‐Shell Fe3O4@C Nanostructures with Enhanced Electromagnetic Wave Absorption

  Advanced Materials Interfaces · 2025-03-12 · 15 citations

  articleOpen access

  Abstract With the advent of high‐power electronic devices, communication satellites, and military radar systems, electromagnetic (EM) waves have caused significant pollution. In this work, hollow Fe 3 O 4 @C (H‐FO@C) composites are synthesized by employing an in situ polymerization and carbonization treatment. Effects of carbonization temperature on electromagnetic wave absorption of core‐shell structured H‐FO@C composites are symmetrically analyzed, and the impedance matching and attenuation ability are improved significantly by controlling carbonization temperature. The reflection loss (RL) and effective absorption bandwidth (EAB) of H‐FO@C composites carbonized at 650 °C are improved to −51.85 dB and 5.36 GHz (thickness 2.1 mm), respectively. When the thickness of composites increases from 2.1 to 2.4 mm, the EAB reaches 6.24 GHz. According to CST Studio Suit, the radar cross section (RCS) reduction value can be 24.26 dB m 2 for H‐FO@C composites. Both experiment and simulation results confirm that the H‐FO@C composites possess excellent EWA performance. This work provides a new way for advancing EWA materials.

  PublisherOA PDFDOI

• Multi-Omics analyses reveal dynamic interactions between DNA methylation and transcriptional regulation during black raspberry ripening

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-11-27

  preprintOpen access

  Abstract We profiled developmental-series transcriptome, methylome, and metabolome profiling to reveal extensive epigenetic reprogramming during black raspberry ( Rubus occidentalis ) fruit ripening. Fruit tissues exhibit globally higher DNA methylation than leaves, particularly in CHG and CHH contexts. Local methylation in all cytosine contexts progressively decreases in promoter regions during ripening, whereas CHG and CHH methylation increase in transposon-rich regions. Two primary methylation transitions—promoter hypomethylation and CHH hypermethylation—govern transcriptional shifts in genes involved in ripening processes. Methyl-binding transcription factors with activation potential likely promote CHH hypermethylation-linked transcriptional activation. Multi-omics integration revealed coordinated anthocyanin accumulation parallels expression of biosynthetic and regulatory genes within coherent networks. Elevated non-CG methylation in heterochromatin coincides with increased transcription of histone variants mediating chromatin compaction, suggesting chromatin remodelers fine-tune accessibility for methyltransferases. Our study highlights both genome-wide and locus-specific epigenetic reprogramming and demonstrates a coordinated interplay between DNA methylation and transcriptional regulation during black raspberry fruit ripening.

  PublisherOA PDFDOI

• Intercropping wheat and rapeseed in Cd-polluted weakly alkaline soil: Crop productivity, Cd enrichment capacity, and rhizosphere soil characteristics

  Journal of Agriculture and Food Research · 2025-02-06 · 3 citations

  articleOpen access1st author

  Phytoremediation is the use of plants to remove or neutralise environmental contaminants, and it provides a promising approach in addressing heavy metal-contaminated soils. However, single-species phytoremediation can be challenged by both its limited metal ion extraction efficiency and crop yield. In this study, we investigated the beneficial effects of intercropping rapeseed ( Brassica napus ) and wheat (Triticum spp.) varieties (Jimai and Zhengyu) with different Cd accumulation potentials on several agronomic traits. The results revealed that intercropping with Jimai and Zhengyu wheat markedly increased the dry and kernel weights of Zhongshuang rapeseed by 10.89–28.57 % and 18.95–27.76 %, respectively. Additionally, intercropping reduced the transfer of active Cd to plants, as indicated by the observed increase of 37.25–154.91 % inbound Cd states, and the available Cd content in the root soil was reduced by up to 35.74 %. This approach also enhanced crop yield, soil health and microbial diversity, thereby highlighting its potential utility in effective soil remediation and sustainable agricultural practices. • Intercropping rapeseed and wheat significantly boosts phytoremediation efficiency. • The wheat varieties Jimai and Zhengyu enhance the growth and yield of rapeseed. • Cadmium transfer to plants is significantly reduced in intercropped systems. • Intercropping improves soil health and increases microbial diversity. • A sustainable strategy for managing heavy metal contamination is proposed.

  PublisherDOI

• Genome-wide identification and expression analysis of the ferritin family in sweetpotato and its two diploid relatives

  BMC Plant Biology · 2025-06-05 · 1 citations

  articleOpen accessSenior author

  Ferritin (FER), a type of iron-storing proteins, play an essential role in iron storage and in protection against oxidative stress. However, there is limited detailed information regarding FERs in sweetpotato. In this study, a total of 17 putative FER genes, 7, 5 and 5 FERs in sweetpotato (I. batatas, 2n = 6x = 90) and its two diploid relatives I. trifida (2n = 2x = 30) and I. triloba (2n = 2x = 30), located on chromosomes were identified. Phylogenetic analysis revealed that these genes are divided into two different groups. Promoter analysis revealed that IbFER promoters contained a number of abiotic/biotic stress-responsive elements, hormone-responsive element, and iron-dependent regulatory sequence. The structural motif analysis of FER proteins showed that Euk_Ferritin domain was identified near the C-terminus and the structures were relatively conserved in sweetpotato and its two diploid relatives. Transcriptome and RT-qPCR analysis demonstrated that the expression of FERs were detected in different tissues and showed tissue specificity, and they responded to abiotic stresses, such as drought, salt and Fe deficiency. Our results provide a theoretical basis for future genetic research, development of breeding strategies against abiotic stresses and food enrichment with iron in sweetpotato.

  PublisherOA PDFDOI

• <i>OsCYP22</i> Interacts With <i>OsCSN5</i> to Affect Rice Root Growth and Auxin Signalling

  Plant Cell & Environment · 2025-01-26 · 1 citations

  articleOpen access

  Beyond structural support, plant root systems play crucial roles in the absorption of water and nutrients, fertiliser efficiency and crop yield. However, the molecular mechanism regulating root architecture in rice remains largely unknown. In this study, a short-root rice mutant was identified and named Oscyp22. Oscyp22 showed impairment in the growth of primary, adventitious and lateral roots. Histochemical and fluorescent staining analyses revealed reduced cell elongation and division activity in the root of Oscyp22. Further analysis showed that Oscyp22 displayed an impaired response to auxin treatment, indicating a disruption in the auxin signal transduction. Transcriptome analysis and auxin content measurement suggested that OsCYP22 might be involved in auxin synthesis and transport. Protein assays demonstrated that OsCYP22 could interact with OsCSN5 and induce its rapid degradation. Notably, Oscsn5 mutants also showed short root phenotypes and deficiencies in auxin response. These findings suggest that OsCYP22 plays a role in rice root growth potentially through auxin signalling and OsCSN5 stability.

  PublisherOA PDFDOI

• Discovery and characterization of a new β-glucosidase from Wolfiporia cocos through RNA sequencing and heterologous expression in Escherichia coli

  International Journal of Biological Macromolecules · 2025-01-16 · 1 citations

  article
  PublisherDOI

• BED-YOLO: An Enhanced YOLOv10n-Based Tomato Leaf Disease Detection Algorithm

  Sensors · 2025-05-02 · 14 citations

  articleOpen accessSenior authorCorresponding

  As an important economic crop, tomato is highly susceptible to diseases that, if not promptly managed, can severely impact yield and quality, leading to significant economic losses. Traditional diagnostic methods rely on expert visual inspection, which is not only laborious but also prone to subjective bias. In recent years, object detection algorithms have gained widespread application in tomato disease detection due to their efficiency and accuracy, providing reliable technical support for crop disease identification. In this paper, we propose an improved tomato leaf disease detection method based on the YOLOv10n algorithm, named BED-YOLO. We constructed an image dataset containing four common tomato diseases (early blight, late blight, leaf mold, and septoria leaf spot), with 65% of the images sourced from field collections in natural environments, and the remainder obtained from the publicly available PlantVillage dataset. All images were annotated with bounding boxes, and the class distribution was relatively balanced to ensure the stability of training and the fairness of evaluation. First, we introduced a Deformable Convolutional Network (DCN) to replace the conventional convolution in the YOLOv10n backbone network, enhancing the model's adaptability to overlapping leaves, occlusions, and blurred lesion edges. Second, we incorporated a Bidirectional Feature Pyramid Network (BiFPN) on top of the FPN + PAN structure to optimize feature fusion and improve the extraction of small disease regions, thereby enhancing the detection accuracy for small lesion targets. Lastly, the Efficient Multi-Scale Attention (EMA) mechanism was integrated into the C2f module to enhance feature fusion, effectively focusing on disease regions while reducing background noise and ensuring the integrity of disease features in multi-scale fusion. The experimental results demonstrated that the improved BED-YOLO model achieved significant performance improvements compared to the original model. Precision increased from 85.1% to 87.2%, recall from 86.3% to 89.1%, and mean average precision (mAP) from 87.4% to 91.3%. Therefore, the improved BED-YOLO model demonstrated significant enhancements in detection accuracy, recall ability, and overall robustness. Notably, it exhibited stronger practical applicability, particularly in image testing under natural field conditions, making it highly suitable for intelligent disease monitoring tasks in large-scale agricultural scenarios.

  PublisherOA PDFDOI

• Multi-Omics analyses reveal dynamic interactions between DNA methylation and transcriptional regulation during black raspberry ripening

  Research Square · 2025-11-24

  preprintOpen access
  PublisherOA PDFDOI

Frequent coauthors

• Zhenhua Zhang

  Fudan University

  40 shared

• Longhua Fan

  Zhongshan Hospital

  32 shared

• Clint Chapple

  Purdue University West Lafayette

  21 shared

• Feiyu Kang

  Tsinghua University

  18 shared

• Jing Liu

  Central South University

  17 shared

• Ying Xia

  Edith Cowan University

  17 shared

• Jianjun Liu

  Zhongshan Hospital

  16 shared

• Yafei Zhang

  Yantaishan Hospital

  16 shared

Labs

• Xiaotong Li LabPI