Single Cell Implementation to Find Expressed Retrotransposons (SCIFER). SCIFER aligns scRNA-Seq reads uniquely to the genome and extracts alignments from single cells by cell-specific barcodes. In contrast to the alignment performed using default parameters, this alignment strategy increases accuracy of L1 locus identification by retaining only reads that are uniquely mapped to individual L1 loci. L1 loci expressed in single cells are unambiguously identified using a list of L1 loci manually validated to be expressed in bulk RNA-Seq datasets generated from the same cell line or organ.Endogenous expression of L1 mRNA is the first step in an L1-initiated mutagenesis event. However, the contribution of individual cell types to patterns of organ-specific L1 mRNA expression remains poorly understood, especially at single-locus resolution. We introduce a method to quantify expression of mobile elements at the single-locus resolution in scRNA-Seq datasets called Validation of SCIFER using MCF7 cells determined technical parameters needed for optimal detection of L1 expression in single cells. We show that unsupervised analysis of L1 expression in single cells exponentially inflates both the levels of L1 expression and the number of expressed L1 loci. Application of SCIFER to analysis of scRNA-Seq datasets generated from mouse and human testes identified that mouse Round Spermatids and human Spermatogonia, Spermatocytes, and Round Spermatids express the highest levels of L1 mRNA. Our analysis also determined that similar to mice, human testes from unrelated individuals share as much as 80% of expressed L1 loci. Additionally, SCIFER determined that individual mouse cells co-express different L1 sub-families and different families of transposable elements, experimentally validating their co-existence in the same cell.SCIFER detects mRNA expression of individual L1 loci in single cells. It is compatible with scRNA-Seq datasets prepared using traditional sequencing methods. Validated using a human cancer cell line, SCIFER analysis of mouse and human testes identified key cell types supporting L1 expression in these species. This will further our understanding of differences and similarities in endogenous L1 mRNA expression patterns in mice and humans.The online version contains supplementary material available at 10.1186/s13100-022-00276-0. Long INterspersed Element-1 (LINE-1 or L1) mRNA has multiple negative consequences for genome stability. L1 mRNA expression can lead to retrotransposition of L1 transcripts or trans-retrotransposition of transcripts from other mobile elements, such as Alu or SVA [Expression of u or SVA . Additiou or SVA –7. Experu or SVA , 9. Prevu or SVA –14, but L1 sequences are abundant in the human genome, which contains 500,000 truncated and full-length copies, as well as in the mouse genome, which harbors 600,000 truncated and full-length copies –17. A crWe developed a method to rigorously analyze L1 mRNA expression at a single-locus resolution by filtering out truncated and passively transcribed L1 sequences , 20, 30.Methods to quantify transposable element expression in single cells, such as the study by Shao, et al,.  implemenSingle Cell Implementation for Finding Expressed Retrotransposons (SCIFER). SCIFER extracts scRNA-Seq reads generated from traditional 10X Chromium Single Cell 3′ Genomics sequencing along with their barcodes and realigns them to the reference genome using Bowtie [Here, we report a new bioinformatics tool designed for analysis of L1 mRNA expression quantification from individual L1 loci in single cells called g Bowtie  with unig Bowtie , 20, 30.g Bowtie , 20, 30.Single Cell Implementation to Find Expressed Retrotransposons) is a method to quantify L1 mRNA expression at the locus-specific level in scRNA-Seq datasets. SCIFER aligns data from a standard 10X Chromium Single Cell 3′ RNA-Seq dataset to the reference genome using procedures optimized to deal with the very high genomic copy number of L1 elements  by cross referencing the list of L1 loci that were assigned same-sense RNA-Seq alignments in the scRNA-Seq dataset with a list of full-length L1 loci validated to be expressed in MCF7 cells from a previous study  were down sampled in 10% intervals and the average number of expressed L1 loci detected in each resulting interval sample was compared Fig. A. This aP < 0.0001, comparing Fig. To determine limitations imposed by lower sequencing coverage, we performed SCIFER analysis on a low coverage MCF7 scRNA-Seq dataset with 10-fold less sequencing coverage and 5-fold more sequenced cells compared to the High coverage MCF7 scRNA-Seq dataset Fig.  B. The cr = 0.55, P < 0.0001 Additional file P < 0.0001). The positive L1 FPKM-RPM correlation and high percent of shared expressed loci detected between Low coverage scRNA-Seq and bulk RNA-Seq establishes that L1 expression detection, averaged across a population of cells, is not dramatically impacted by reduced sequencing depth. However, the number of expressed L1 loci per cell and their levels of expression are underestimated when a low coverage dataset is used. Additionally, similar to the High coverage scRNA-Seq dataset, L1 loci expressed in bulk RNA-Seq but missed by SCIFER in lower sequencing coverage scRNA-Seq datasets are those L1 loci that have low expression levels.We next compared the total L1 expression levels detected by SCIFER in the Low coverage MCF7 dataset with L1 expression levels detected in the bulk RNA-seq dataset. Despite reduced sensitivity of detection of L1 expression per cell in the Low coverage dataset, RPM per expressed L1 locus in the Low coverage dataset was positively correlated with bulk RNA-Seq L1 FPKM  to determine the extent of variation in L1 expression between individual cells Fig. C. This aWe also detect expression of housekeeping genes (HKGs) in cells with read numbers downsampled in 10% increments, the same three cells used in Fig. P < 0.0001, Additional file P < 0.0001, Additional file P < 0.0001, Additional file Previously, our comprehensive analysis of locus-specific L1 mRNA expression in mouse organs determined that testes express the highest levels of L1 mRNA compared to other mouse organs, including male and female brains, male and female lungs, ovaries, and uteri . To deteP < 0.0001, P < 0.0001, P < 0.0001, respectively, Fig. P = 4.39E-99, P = 1.20E-28, P = 3.89E-94, respectively, Additional file P < 0.0001, P < 0.0001, P < 0.0001, respectively, Fig. SCIFER analysis was performed on the scRNA-Seq mouse testis samples to discover the levels and patterns of L1 mRNA expression in different cell types using a list of L1 loci validated to be expressed in mouse testes from a previous publication . SCIFER To understand potential changes in L1 expression during spermatogenesis, we compared the identity of L1 loci expressed in different sperm cell types identified in our single cell pool. Comparison of L1 loci expressed in different sperm cell types determined that in the Mouse 1 dataset, Spermatocytes, Round Spermatids, Elongating Spermatids, and Condensing Spermatids share only 2% 5 of 212) of L1 loci expressed in testes, with 157 L1 loci being detected to be expressed only in Round Spermatids alone Fig. D. In the of 212 or = 0.51, P < 0.0001, Fig. r = 0.72, P < 0.0001, Fig. P < 0.0001, Fig. Previously we found that in MCF7 cells SCIFER detects 88–93% of expressed L1 loci detected in bulk RNA-Seq and the L1 FPKM-RPM levels of expressed L1s are positively correlated between SCIFER-detected L1 loci expression in single cells and bulk RNA-Seq  that were manually validated to be expressed in bulk RNA-Seq were analyzed in scRNA-Seq datasets  and Spermatocytes 0.029) compared to Spermatogonia (0.025), Elongated Spermatids 0.28), and Condensing Spermatids (0.007)  (P < 0.0001) , which allows unique mapping of scRNA-Seq reads to the genome while retaining cell-specific barcodes. SCIFER analysis also includes validating L1 expression detected in scRNA-Seq with the analysis of L1 expression in bulk RNA-Seq from a matching tissue sample using our existing approach for identifying expressed L1 mRNAs from individual sites, while eliminating those that are passively expressed [Using RNA-Seq to measure the expression of L1 elements derived from their own promoter is technically challenging, both because of difficulties properly aligning short-read sequences to a single, specific locus and the high levels of passive inclusion of L1 sequences in other transcripts that create high levels of background , 20–22. xpressed , 20, 30.We validated SCIFER as a method for measuring L1 expression in single cells by analyzing a scRNA-Seq dataset generated from a pooled sample of MCF7 and HEK293 cells to determine whether our method was sensitive enough to detect the same loci as seen in the bulk RNA-Seq studies .These findings have important implications for experimental design and comparative analysis of L1 expression in single cells. For example, comparisons between scRNA-Seq with significantly different average sequencing depth per cell are likely to produce artifacts regarding the number of expressed L1 loci per cell and the number of cells expressing specific L1 loci. However, both High and Low coverage datasets can be used for organ-derived studies to identify relative contribution of different cell types to L1 expression and the diversity and cell type specificity of expressed L1 loci in a specific organ Fig. .Our single-cell analysis also provides evidence that SCIFER can be adapted for detection of other repetitive sequences and assist in answering outstanding questions regarding mobile element biology. It has been proposed that L1 promoter evolution is driven in part by co-expression of different L1 subfamilies, which leads to their competition for cellular transcription factors and other transcriptional machinery –65. UsinWe also performed SCIFER analysis on human testes to observe whether L1 expression patterns in testes are conserved between mice and humans. Bulk RNA-Seq of human testes from two unrelated 20 yo donors showed a high level of similarity in the L1 loci expressed  of origin of L1 expression in different organs and species. Additionally, SCIFER has the ability to uncover evolutionary niches occupied by different classes of TEs or TE subfamilies, as well as to understand L1 impact on function and genome stability of single cells.MCF7 (ATCC HTB-22) and HEK293-FRT-LacZeo cells were maintained in DMEM with high glucose (Gibco) supplemented with 10% fetal bovine serum (Gibco), sodium pyruvate, essential and nonessential amino acids, and L-glutamine. Cells at 100% confluency were detached using accutase (Innovative Cell Technologies) and resuspended in DPBS. MCF7 and HEK293-FRT-LacZeo cells were combined at a 9:1 ratio with 180 MCF7 and 20 HEK293 cells per uL. The single cell suspension was confirmed to be 84.4–83.1% viable prior to RNA preparation and sequencing.Human testis bulk RNA samples from two 20-year-old (yo) donors were obtained from AMSBIO (product codes: CR562159 and CR562389). RNA was then poly-A selected prior to stranded, paired-end RNA sequencing using an Illumina NextSeq 2000 in the Tulane NextGen sequencing core.Single-cell RNA sequencing was carried out on individual cells using the 10X Genomics Chromium™ Single Cell 3′ Library & Gel Bead Kit v3 and 150 cycle kit by the Tulane NextGen sequencing core. Eight hundred fifty-three cells were sequenced and barcoded. Sequencing was carried out on an Illumina NextSeq 2000. Pooled MCF7 and HEK293-FRT-LacZeo cells were separated by aligning reads to an FRT-LacZeo genome. Cell barcodes with reads aligning to the FRT-LacZeo locus were classified as HEK293-FRT-LacZeo cells.The shallow sequenced MCF7 scRNA-Seq dataset and mouse and human testes scRNA-Seq datasets were obtained from NCBI SRA. The shallow sequenced MCF7 scRNA-Seq dataset is listed under SRR10018060. The mouse testes datasets are listed under SRR6129050 (Mouse 1) and SRR6129051 (Mouse 2). The human testes datasets are listed under SRR6860521 (24 yo) and SRR6860523 (25 yo).The strategy for detection of L1 mRNA used here has been described previously . BrieflyBioinformatic Analysis for bulk RNA-Seq , resulting in unambiguous assignment of reads to their best genomic location , as previously reported , 30. ForThe described formula for RPM is shown below:10X Chromium Single Cell 3′ Genomics data was demultiplexed and converted into fastq format using cellranger v3.1.0 mkfastq tool. The cellranger v3.1.0 count program was then used to analyze scRNA-Seq reads and alignment of reads to the appropriate reference genome to generate a gene expression matrix. The total number of L1 UMIs from SCIFER analysis were loaded into the gene expression matrix and analyzed using Seurat v4.0.5. The datasets were normalized  and L1 expression was compared between cell clusters and cell types (FindAllMarkers).n = 2) were made using the normalized gene expression levels downloaded from the Seurat object and Welch’s t-tests.Seurat v4.0.5 was used to compare gene expression levels between clusters and cell types . Cells wt-test with Welch’s correction when making comparisons between two groups and by one-way ANOVA with multiple comparisons and “two-stage” Benjamini, Krieger, & Yekutiel for controlling the false discovery rate when making comparison within a n > 2 group. To measure linear correlation between two variables and determine the r value, a two-tailed Pearson correlation test was performed. Data presented from Seurat was analyzed using a non-parametric Wilcoxon rank-sum test with Bonferroni correction and the adjusted P-values from Bonferroni correction are plotted. Statistical analysis was performed with GraphPad Prism and Seurat v4.0.5.Bar graphs are presented with the mean and standard deviation bars. Data were analyzed by two-tailed Student’s Additional file 1. Single cell RNA-Seq reads align to the 3′ end of genes and L1 loci. A. Alignment of scRNA-Seq reads (top), bulk RNA-Seq reads (middle), and DNA-Seq reads (bottom) to the L1 consensus sequence using Bowtie v0.12.8. The sequencing read scale is indicated in the bottom left corners. B. Alignment of MCF7 bulk RNA-Seq and MCF7 scRNA-Seq reads to the actin (ACTB) gene locus (top) and an L1 locus (bottom). Images were taken from IGV and the visible tracks include, from top to bottom, chromosome location indicated by the red line, scale in base pairs, mappability from a DNA-Seq samples aligned with the same bowtie settings used for RNA-Seq , RNA molecules per cell (middle), and percent of mitochondrial reads per cell (right) for each MCF7 cell cluster. C. The number of expressed L1 loci per cell for all High coverage MCF7 cell clusters is shown in the individual value plot . D. The L1 mRNA expression level per cell for all MCF7 clusters is shown in the individual value plot . E. The normalized expression levels for MCF7 Clusters 1, 3, and 4 from Seurat analysis are shown . F. The number of reads per HKG averaged between 3 MCF7 cells downsampled in 10% intervals is shown. G. The average number of expressed HKGs detected in 3 MCF7 cells is shown with decreasing read depth . The black bars have significantly higher numbers of detected HKGs compared to the gray bars . The lightest gray bar (10%) indicates detection of less than half of expressed HKGs detected with 100% of reads. H. A correlation matrix of HKGs expressed in 10 MCF7 cells. The HKGs are listed at the top of the matrix and the 10 cells are listed along the left side. Filled boxes indicate that the HKG was expressed in the corresponding cell.Additional file 3. Low depth sequencing reduces sensitivity of L1 mRNA expression detection in MCF7 single cells. A. A t-SNE plot of the Low coverage MCF7 scRNA-Seq dataset. Cell clusters are indicated in the figure legend. B. The number of million mapped reads per cell in each cluster is shown in the individual value plot. C. L1 mRNA expression quantified by RPM for each cell is shown in the individual value plot. D. The number of expressed L1 loci per cell in each cluster is shown in the individual value plot E. The L1 FPKM values for bulk RNA-Seq (left y-axis) and L1 RPM values for Low coverage MCF7 scRNA-Seq (right y-axis) are shown in the dot-plot. Orange circles indicate L1 loci with detected expression in the bulk dataset that were not detected to be expressed in scRNA-Seq. The nested Venn diagram shows the number of shared expressed L1 loci from the bulk and scRNA-Seq datasets. The violin plot shows the FPKM values for expressed L1 loci unique to the bulk dataset and those shared between bulk and scRNA-Seq .Additional file 4. Expression patterns of genes involved in spermatogenesis in mouse testes. Cell types are abbreviated as follows: Spermatocytes (Spcyt), Round Spermatids (RS), Elongating Spermatids (ES), Condensing Spermatids (CS), Sertoli (Ser), Leydig (Ley). A. Mouse 1 gene expression patterns for TATA-Box Binding Protein Like-1 (TBPL1) (A1), Protamine (PRM1) (A2), and Transition Protein 1 (TNP1) (A3). B. Mouse 2 gene expression patterns for TBPL1 (B1), PRM1 (B2), and TNP1 (B3). C. The heat map shows the normalized expression patterns for each cell type and gene in Mouse 1 (top) and Mouse 2 (bottom). The asterisks indicate a significant change in gene expression in the cell type compared to the preceding cell type in the row (P < 0.0001). D. The normalized expression levels for Mouse 1 testis cell types from Seurat analysis are shown .Additional file 5. SCIFER analysis of L1 mRNA expression in mouse testis biological replicates. A. The number of million mapped reads per cell for each testis cell type is shown for Mouse 1 . B. The number of million mapped reads per cell for each testis cell type is shown for Mouse 2 ANOVA,, ***, < 0.0005, ****,< 0.0001). C. L1 mRNA expression measured by RPM per cell for all testis cell types is shown in the scatter plot for Mouse 1 . D. L1 mRNA expression measured by RPM per cell for all testis cells types is shown in the scatter plot for Mouse 2 . E. The number of expressed L1 loci per cell for each testis cell type is shown in the scatter plot for Mouse 1 . F. The number of expressed L1 loci per cell for each testis cell type is shown in the scatter plot for Mouse 2 . G. The L1 RPM values for L1 loci indicated on the X-axis are shown for each sperm cell type in Mouse 2. The Venn Diagram shows the number of expressed L1 loci shared by the sperm cell types with the percentage of shared expressed L1 loci indicated above the diagram. H. L1 mRNA expression compared between Mouse 1 and Mouse 2 for Spermatocytes (SC) (H1), Round Spermatids (RS) (H2), Elongating Spermatids (ES) (H3), and Condensing Spermatids (CS) (H4).Additional file 6. Expression patterns of genes involved in limiting L1 expression and integration in mice. Cell types are abbreviated as follows: Spermatocytes (Spcyt), Round Spermatids (RS), Elongating Spermatids (ES), Condensing Spermatids (CS), Sertoli (Ser), Leydig (Ley). A. Mouse 1 gene expression patterns for Phospholipase D Family Member 6 (PLD6) (A1), Heat Shock Protein 90 Alpha Family Class A Member 1 (HSP90AA1) (A2), and Ubiquitin Like with PHD And Ring Finger Domains 1 (UHRF1) (A3). B. Mouse 2 gene expression patterns for PLD6 (B1), HSP90AA1 (B2), and UHRF1 (B3). C. The heat map shows the normalized expression patterns for each cell type and gene in Mouse 1 (top) and Mouse 2 (bottom). The asterisks indicate a significant change in gene expression in the cell type compared to the preceding cell type in the row (P < 0.0001).Additional file 7. Expression of LTRs and L1 subfamilies is non uniform across single cells in mouse testis. A. The number of expressed L1 loci per L1 subfamily is shown for Mouse 1. The L1 subfamilies are indicated in the legend. B. The number of Round Spermatids expressing L1 loci from each mouse L1 subfamily is shown in the Venn diagram. C. The number of reads per LTR locus is shown for bulk RNA-Seq, Mouse 1 Single Cells, and Mouse 2 Single Cells. The Venn diagram shows the number of expressed LTR loci shared between the three datasets. D. Cells expressing at least one LTR loci are shown for each cell type in Mouse 1 (D1) and Mouse 2 (D2). LTR expression is indicated with purple and L1 co-expression is indicated with teal.Additional file 8 Expression patterns of genes involved in spermatogenesis in human testes. Cell types are abbreviated as follows: Spermatogonial Stem Cells (SSCs), Differentiating Spermatogonia (Diff Spg), Spermatocytes (Spcyt), Round Spermatids (RS), Elongating Spermatids (ES), Condensing Spermatids (CS), Macrophages (Macro), Endothelial (Endo), Myoid (Myo), and Sertoli (Ser). A. 24 yo Human Testis gene expression patterns for PRM1 (A1), SPAG6 (A2), TNP1 (A3), and TNP2 (A4). B. 25 yo Human Testis gene expression patterns for PRM1 (B1), SPAG6 (B2), TNP1 (B3), and TNP2 (B4). C. The heat map shows the normalized expression patterns for each cell type and gene in 24 yo Testis (top) and 25 yo Testis (bottom). The asterisks indicate a significant change in gene expression in the cell type compared to the preceding cell type in the row (P < 0.0001). D. The normalized expression levels for 24 yo testis cell types from Seurat analysis are shown .Additional file 9. Analysis of L1 mRNA expression in human testes with bulk RNA-Seq and scRNA-Seq. A. The L1 FPKM values for L1 loci listed on the X-axis are shown for two 20 yo human testis bulk RNA-Seq samples. The number of expressed L1 loci shared between the two replicates is shown in the Venn diagram with the percentage of shared expressed loci indicated above the diagram. B. The L1 RPM values for L1 loci listed on the X-axis are shown for a 24 yo and a 25 yo human testis scRNA-Seq sample. The number of expressed L1 loci shared between the two replicates is shown in the Venn diagram with the percentage of shared expressed loci indicated above the diagram. C. The million mapped reads per cell for all cell types in the 24 yo scRNA-Seq dataset is shown in the scatter plot. Statistics are shown for sperm cells and sperm progenitor cell types only. D. The L1 mRNA expression level per cell for all 24 yo testis cell types is shown in the scatter plot. E. The number of expressed L1 loci per cell for all 24 yo testis cell types is shown in the scatter plot. F. The million mapped reads per cell for all cell types in the 24 yo scRNA-Seq dataset is shown in the scatter plot. Statistics are shown for sperm cells and sperm progenitor cell types only. G. The L1 mRNA expression level per cell for all 25 yo testis cell types is shown in the scatter plot. H. The number of expressed L1 loci per cell for all 25 yo testis cell types is shown in the scatter plot. I. The L1 RPM values for L1 loci indicated on the X-axis are shown for each sperm cell type in the 25 yo human testis sample. The Venn Diagram shows the number of expressed L1 loci shared by the sperm cell types with the percentage of shared expressed L1 loci indicated above the diagram. J. The L1 FPKM values for bulk RNA-Seq (left y-axis) and L1 RPM values for scRNA-Seq (right y-axis) of human testis are shown in the dot-plot. Orange circles indicate L1 loci with detected expression in the bulk dataset that were not detected to be expressed in scRNA-Seq. The nested Venn diagram shows the number of shared expressed L1 loci from the bulk and scRNA-Seq datasets. K. The violin plot shows the FPKM values for expressed L1 loci unique to the bulk dataset and those shared between bulk and scRNA-Seq .Additional file 10. Expression patterns of genes involved in limiting L1 expression and integration in humans. Cell types are abbreviated as follows: Spermatogonial Stem Cells (SSCs), Differentiating Spermatogonia (Diff Spg), Spermatocytes (Spcyt), Round Spermatids (RS), Elongating Spermatids (ES), Condensing Spermatids (CS), Macrophages (Macro), Endothelial (Endo), Myoid (Myo), and Sertoli (Ser). A. 24 yo Human Testis gene expression patterns for DNMT1 (A1), MECP2 (A2), KDM1A (A3), TP53 (A4), TRIM28 (A5), ERCC4 (A6), MOV10 (A7), and RNASEH2B (A8). B. 25 yo Human Testis gene expression patterns for DNMT1 (B1), MECP2 (B2), KDM1A (B3), TP53 (B4), TRIM28 (B5), ERCC4 (B6), MOV10 (B7), and RNASEH2B (B8). C. The heat map shows the normalized expression patterns for each cell type and gene in 24 yo Testis (top) and 25 yo Testis (bottom). The asterisks indicate a significant change in gene expression in the cell type compared to the preceding cell type in the row (P < 0.05).Additional file 11. Directions for running Single Cell Implementation to Find Expressed Retrotransposons (SCIFER).Additional file 12. Coordinates of L1 loci identified to be expressed in bulk RNA-Seq of MCF7 cells and included in SCIFER analysis.Additional file 13. Coordinates of LTR loci identified to be expressed in bulk RNA-Seq of 2 mo mouse testes and included in SCIFER analysis.Additional file 14. Coordinates of L1 loci identified to be expressed in bulk RNA-Seq of 2 mo mouse testes and included in SCIFER analysis.Additional file 15. Coordinates and FPKM of L1 loci identified to be expressed in bulk RNA-Seq of 20 yo human testes and included in SCIFER analysis.Additional file 16. Results of comparing gene expression values in mouse testes using unpaired t-test with Welch’s correction.Additional file 17. Results of comparing gene expression values in human testes using unpaired t-test with Welch’s correction.


Based on coupled-mode theory (CMT) and the finite-difference time-domain (FDTD) approach, we propose a graphene metasurface-based and multifunctional polarization beam splitter that is dynamically tunable. The structure, comprising two graphene strips at the top and bottom and four triangular graphene blocks in the center layer, can achieve triple plasma-induced transparency (PIT). In a single polarization state, the computational results reveal that synchronous or asynchronous six-mode electro-optical switching modulation may be performed by modifying the Fermi levels of graphene, with a maximum modulation degree of amplitude (MDA) of 97.6% at 5.148 THz. In addition, by varying the polarization angle, a polarization-sensitive, tunable polarization beam splitter (PBS) with an extinction ratio and insertion loss of 19.6 dB and 0.35 dB at 6.143 THz, respectively, and a frequency modulation degree of 25.2% was realized. Combining PIT with polarization sensitivity provides a viable platform and concept for developing graphene metasurface-based multifunctional and tunable polarization devices. Polarization, along with amplitude and phase, is an essential property of electromagnetic wave propagation. A polarization beam splitter (PBS) is a crucial polarization modulation device that can separate two orthogonal polarization modes in electromagnetic waves and can be used in single-polarization devices within various parts of an integrated system so that subsequent chip signal processing can be performed independently. Consequently, it has significant applications in optical storage, optical communication, and integrated optical circuits ,2,3, andMany scientists prefer graphene because it exhibits less major losses in metamaterials than conventional metallic materials. Metamaterials made of graphene also benefit from dynamic modulation, which modifies the graphene’s Fermi levels by varying the externally supplied bias voltage to induce various visual phenomena. In optical devices, including electro-optical switches ,12,13,14However, the majority of these devices only work in one polarization state. PBSs have become a popular area of research because integrated systems require a polarization control mechanism to maintain a single polarization state across the system. Due to their low process needs and straightforward design, most PBSs are currently constructed using the directional coupling design approach. For instance, Tian et al., developed a portable ultra-wideband PBS , while BThis work proposes a triple-layer metamaterial made up of two graphene strips and four graphene blocks. Finite-difference time-domain (FDTD) simulations were performed , and couachieved . Xia et achieved . The extachieved  and becoy-polarized light, and transmission spectra as shown in y-direction is present alone. The graphene strip in the x-direction is not instantly activated by the incident light when it exists independently. Instead, it belongs to a dark mode, as indicated by the black line in The top view structural unit of the graphene metamaterials is shown in As shown by the green line in A representing Bright Mode 1, resonant cavity B representing Bright Mode 2, and resonant cavity C representing Dark Mode. As a result, incoming light can stimulate it twice, making it a bright mode (Bright Mode 2). Triple PIT is produced at Peak 1, Peak 2, and Peak 3 when the three structures are combined. Furthermore, when the three-layer graphene structure is present, Bright Mode 1 influences the interstitial coupling between the graphene blocks, which results in some deviation of Peak 2 from the resonance point at 8.034 THz and transmission spectra of the entire structure, as shown by the red line in A, B, and C can be expressed by the following equation [A, B, and C, and is related to the thickness of the dielectric films A, B, and C are the amplitudes of the three modes, “in/out” are the input or output waves, and “Based on the schematic diagram, the relationship between resonant cavities equation .(1)y-polarized state, as depicted in Firstly, we analyze the results in the As shown in The performance of the electro-optical switch can be expressed by the modulation degree of amplitude (MDA) :(13)MDA=Firstly, the polarization angle of the incident light is varied from 0° to 90°, as shown in x-polarized state. Due to the different pinch distances of the graphene blocks in the x and y directions, the distance between the two resonance points increases. Still, x-polarization does not couple with the Dark Mode to generate the PIT. These are the primary reasons for the polarization sensitivity of this structure. The polarization sensitivity we analyzed coincides with Zhang’s theory that the PIT of metamaterials is destroyed when the polarization angle changes [As shown in  changes .y-polarization is selected, and the x-polarization is filtered out. Instead, the x-polarization is filtered out at 3.201 THz, and the y-polarization is filtered out at 7.062 THz. These enable the realization of a multi-frequency polarizer. When combined with the polar coordinates in The four frequency points’ specific modulation positions and implementation mechanism are denoted in x-polarization and 0.42, 0.04, 0.09, and 0.4 for y-polarization, respectively. As mentioned in some articles, the transmittance is considered to be in a normal operating state when it is greater than 0.8 [x-polarization wave is reflected with a reflectivity of up to 86% and almost no absorption, so we can assume it is entirely reflected back. At this frequency point, the orthogonal polarizations are spatially separated, creating an excellent polarization beam splitter. As shown in x-polarized light is reflected, and y-polarized light is transmitted directly. In addition, the performance of a PBS is typically determined by the polarization extinction ratio (PER) and insertion loss (IL), as shown in the following equations [x- and y-polarization were synchronously blue-shifted.As shown in than 0.8 . Only atquations :(14)PER=This PBS can be dynamically modulated by changing the Fermi levels of graphene. The modulation degree of frequency is typically used to describe the frequency modulation effect, as shown in the following equation:In conclusion, we simulated a three-layer metamaterial structure of two graphene bands and four graphene blocks. Based on plasma-induced transparency, a six-mode dynamically tunable synchronous and asynchronous electro-optical switch was achieved by tuning the Fermi levels of graphene; its MDA is 80.1%, 97.6%, 67.8%, 80.2%, 80%, and 84.9 % at 3.021 THz, 5.148 THz, 6.248 THz, 7.062 THz, 7.745 THz, and 9.458 THz, respectively. Moreover, we explored the impact of polarization angle variation on graphene metamaterials. The results demonstrate that a change in the polarization angle disrupts the PIT formed by the structure, resulting in polarization sensitivity. Consequently, a polarization beam splitter was constructed with PER and IL values of 19.6 dB and 0.36 dB, respectively. In addition, based on the dynamic tunability of graphene, the operating frequency point of the polarization beam splitter is adjustable with an MDF up to 25.2%. This research lays the groundwork for the future construction of graphene-based multifunctional optoelectronic devices and configurable polarization beam splitters. The findings provide a theoretical basis for designing and implementing graphene-based, multifunctional, fully transmittable metasurface beam splitters for on-chip integration.


Plasmon coupling and hybridization in 2D materials plays a significant role for controlling light–matter interaction at the nanoscale. We present a near-field radiation heat transfer (NFRHT) between vertically separated graphene and black phosphorene sheets at different temperatures in nanoscale separations. Radiation exchange from the theory of fluctuation electrodynamics is modulated by the carrier density of graphene and phosphorene. Direct comparison of NFRHT black phosphorene–graphene to symmetric graphene–graphene radiation exchange can be as much as 4 times higher for the selected doping range in both armchair (AC) and zigzag (ZZ) orientations of BP. The strong NFRHT enhancement of the specific optical properties of the heterogenous 2D material is due to the strong coupling of propagating surface plasmon polaritons as demonstrated by the distribution of the heat transfer coefficient. We also demonstrate that the magnitude of the near-field radiation enhancement is found to acutely depend on the vacuum gap of the graphene and BP pair. Interestingly, for separation distances below 200 nm, the total near-field heat transfer between black phosphorene and graphene exceeds that between graphene and graphene by 5 times. The radiation enhancement can be further tuned based on the orientation, AC, and ZZ of black phosphorene. These results prominently enable dynamic control of the total NFRHT relying on tunable anisotropic characteristics of BP irrespective of graphene's optical conductivity. Furthermore, the heterogeneous pairs of 2D materials potentially provide alternative platforms to achieve beyond super-Planckian radiation. Plasmon coupling and hybridization in 2D materials plays a significant role for controlling light–matter interaction at the nanoscale. Control of NFRHT has been covered in several structural arrangements and materials;11–17 however, a challenge arises in enhancing NFRHT due to the large number of independent parameters, and more importantly, lack of degree of freedom for active tunability.Near-field radiative heat transfer (NFRHT) between two bodies separated by a distance much smaller than or comparable to the thermal wavelength can break the blackbody (BB) limit.2 and, very recently, black phosphorene.18–21 Graphene exhibits highly tunable optical properties via voltage-gating,18,22 good thermal stability,23 and high carrier mobility.18 Moreover, graphene supports strongly confined surface plasmons with a frequency that varies with the wave number.24–26 Theoretically predicted tuning and coupling of surface mode plasmon of a graphene sheet with a nearby body, resulting in a significant increase of the NFRHT,27–30 has been confirmed experimentally.31 In addition, the near-field active thermal radiation response of graphene in subwavelength geometries, graphene ribbon,32,33 periodic nanoribbon,34 or nanodisks35–37 has demonstrated enhanced radiation. Most recently, Black phosphorus (BP) is also reported to have a layer dependent tunable direct bandgap,38 thermal stability,39 high carrier mobility,20,21 and remarkable in-plane anisotropic electrical and optical properties.40 In contrast to graphene, BP shows polarization dependency in the anisotropic surface plasmon response due to an order of magnitude difference in the in-plane effective electron masses along the two crystal axes results.41,42 These plasmon properties allow for the realization of novel 2D polarization dependent and tunable NFRHT.33,43–45 Another interesting approach to study is the NFRHT between heterogenous 2D material, shown to be possible using a graphene sheet and a MoS2 sheet.46 However, both graphene and MoS2 are nearly isotropic materials. This makes the graphene and MoS2 orientation indiscriminate in light matter interaction. Recently, the NFRHT of the graphene (GR) heterogenous (GR–BP) in contact with bulk substrate SiC has shown anisotropic effects in near field radiation heat transfer.47 However, SiC supports hyperbolic phonon polariton modes in the midinfrared range. In hybrid structures composed of 2D material, graphene/SiC or BP/SiC, SPPs can couple with hyperbolic phonon polariton.47 The hybrid modes move from the substrate and introduce a change in the propagation, and evanescent waves either greatly improve or reduce the NFRHT by creating a new channel. Therefore, it is important to understand the NFRHT and tuning capability graphene and BP heterogeneous system without dispersive hyperbolic layer.In this context, a breakthrough for active tuning mechanics has been effectively achieved in two-dimensional (2D) semiconductors: prominently graphene or MoS48 the near-field radiation transfers energy between the BP and the graphene, which are compared to two identical suspended graphene sheets by analysing the distribution of the heat transfer coefficient of the emitter and the receiver 2D layers. The NFRHT model shows that BP–GR has a more selective optical modulation compared to the graphene–graphene system. Furthermore, the NFRHT differences between these two systems are explored, considering the inherent BP optical anisotropy nature. The effect of the reorientation of BP is found to be significant in modulating the NFRHT, perhaps creating possible conditions roughly equivalent to or greater than graphene. The hybrid modes, which evolve from low to high parallel wave vectors in the infrared range, controlled by graphene and BP optical parameters determine the tunnelling probability. We also discuss the total radiation, enhancement in the 2D structure relative to the value of separation distance, offering potential routes toward passive or active control of NFRHT. In particular, we examined the ratio of total heat transfer of BP–GR and GR–GR, and its dependence on the anisotropy, doping, and spacing.Through numerical and analytic solutions based on the transfer matrix method combined with fluctuation electrodynamics,σg(ω), is modelled with a 2D sheet conductivity that includes the contribution of an intraband transition term σD , and interband transitions term, σI, where σg(ω) = σD(ω) + σI(ω).49G(x) = sinh[x/kBT]/[cosh(μ/kBT) + cosh(x/kBT)], e is the electron charge, ℏ is the reduced Planck constant, kB is the Boltzmann constant, μ is the chemical potential, T is temperature, ω is the angular frequency, and τ is the relaxation scattering time, which we have fixed to the value τ = 10−13 s.50The optical conductivity in graphene, σjj, of BP is given by a simple semiclassical Drude model in the infrared regime,40j represents AC and ZZ-direction η describes the relaxation rate and is chosen to be 10 meV40 which is within the range obtained from ab-simulation51 and Di = πne2/mj is the anisotropic Drude weight, n is the electron doping, and mj represents the layer dependent effective mass of electrons along the j, (AC or ZZ) direction. In this work we use monolayer BP with arm-chair, mx = 0.15m0, and zigzag, my = 0.7m0, direction masses, where m0 is the free electron mass.40The anisotropic layer dependent surface conductivity, σij, consisting of transverse conductivities, longitudinal conductivities, and crossed conductivity components, which lies in the xy-plane. We obtain the Fresnel coefficients solving Maxwell's electromagnetic equation imposing the appropriate electromagnetic field boundary condition and representing the 2D conducting sheet surface current (Js) from Ohm's law. We assume that the electric field Ei in medium i, and Ej in medium j, i and j indicated medium above and below the 2D layer, with dielectric permittivity εi = εj = 1.0. The corresponding magnetic field components, Hi and Hj, can be found using Maxwell's equation. The boundary conditions of the parallel and perpendicular gives n̂ × (Ei − Ej) = 0, n̂ × (Hi − Hj) = Js. The unit vector normal to the 2D layer is n̂ = −ẑ, and Js = σijE. Solving the boundary conditions, the Fresnel coefficients of reflection for s, p and the cross-polarization term expressions are52,53rppij, rssij and rspij are the Fresnel coefficients between the medium i and j for s, p and the mixture sp polarization. The z-component wavevector for s and p-polarization along each region is , , and ε0 is the vacuum permittivity, μ0 is the vacuum permeability and β is the wave vector parallel to the surfaces of the 2D layers. This work studies BP AC orientation aligned with x-direction and ZZ along y-axis and vice versa. The incident polarization direction is collinear to one of the principal BP directions so that the crossed conductivity effect, σxy = 0, results in the cross-polarization rps = 0. A similar method has been followed recently by Xiao-Jie Yi et al.47 A rotated BP from the optical axis does change the momentum vector of along x and y and hence the expression of the reflection spectra.54Before setting the model of radiation heat transfer, we examined the light–matter interaction for the suspended isotropic 2D materials. As shown in TE) and the thermally cold (TR) surfaces can be formulated following the framework of fluctuational electrodynamics as:55q(ω) is the net spectral heat flux, Θ = ωℏ/(exp(ωℏ/kBT) − 1)is the average energy of Planck's oscillator, and ξs(p) is the energy transmission coefficient that depends on the frequency and wave vector corresponding to s(p)-polarization of light. The energy transmission coefficient considering polarization coupling and contribution of evanescent modes, β > k0, and propagating modes, β < k0, is given by56k0z is the normal wavevector component in vacuum of BP and graphene, Im takes the imaginary part of a complex quantity, and d is the distance of the vacuum gap.Now the NFRHT initiated by the coupling of the propagating surface modes meditated between the thermally hot  and σR (receiver), respectively, are brought into proximity with a vacuum gap separation d. The temperatures of the emitter and the receiver are labeled TE = 300 K and TR = 290 K, (TE > TR), respectively. We represent gate modulation or applied voltage bias effect in terms of changing the carrier density n, of graphene and BP. This indirect relation of gate-modulation of BP and graphene can be controlled independently leading to more degrees of freedom. The carrier density potential of graphene can be described also in terms of chemical potential, ng = μ2/(ℏvf)2π, where vF = 106 ms−1 is the Fermi velocity. The values of ng used in this work ranges from 0.74 × 1012 cm−2 to 4.7 × 1013 cm−2, with an equivalent range of μ = 0.1 eV to 0.9 eV, and nb = 2.0 × 1012 cm−2 to 9.0 × 1013 cm−2 in order to get optimum NFRHT.Throughout the paper, we consider the configuration in x-direction and ZZ is in the y-direction. The separation distance d between graphene and BP is 10 nm. The figure shows the spectral heat flux (SHF) calculated from ng = 0.74 × 1012 cm−2, 6.6 × 1012 cm−2 and high doped 2.65 × 1013 cm−2, which corresponds to a chemical potential of μ = 0.1 eV, 0.3 and 0.6 eV, respectively, and BP varying the doping-level, nb = 0.5 × 1013 cm−2, 1.0 × 1013 cm−2, 5.0 × 1012 cm−2.First, we consider the scenario in TE = 300 K and TR = 290 K. We selected identical gate voltage for the emitter and receiver graphene, ngE = ngR = ng since the latter case shows strong spectral heat flux.38 Therefore, low gate modulated NFRHT between identical graphene shows enhanced radiative energy transport than between low and highly gate modulated graphene. Further support of this conclusion is provided in the ESI Fig. 1 (a) and (b).ng = 0.74 × 1012 cm−2 and decreases more than ten times when the doping is 2.65 × 1013 cm−2. In addition, at higher graphene carrier density, ng = 2.65 × 1013 cm−2, SRH becomes broader due to weakening of interband effect.27,29,38 In the case of BPE–GRR, increasing number density nb from 0.2 × 1013 cm−2 to 5.0 × 1013 cm−2 has a dramatic effect on the SHF at low carrier density, ng = 0.74 × 1012 cm−2, n. Compared to BPE–GRR for all ng and nb, GRE–GRR is higher than BPE–GRR at ng = 2.65 × 1013 cm−2. The role of the dependency of ng and nb on the SHF is further evaluated for 6.6 × 1012 cm−2 and 2.65 × 1013 cm−2, ng = 4 × 1012 cm−2 SHF for BPE–GRR is higher than the that of GRE–GRR from moderate to high doping, ng = 6.6 × 1012 cm−2 the SHF of BPE–GRR is smaller than GRE–GRR and larger by a factor of two for nb = 5.0 × 1012 cm−2 and 5.0 × 1013 cm−2. This indicates in BPE–GRR there is a critical nb value between 5.0 × 1012 cm−2 and 1.0 × 1013 cm−2 making the transition of enhanced SHF than GRE–GRR, in ng = 2.65 × 1013 cm−2, we observe that BPE–GRR 2D system outperforms identical graphene sheets, within the whole range of nb, having two times higher SHF for ng = 1.0 × 1013 cm−2 and 5.0 × 1013 cm−2.In all figures of x-axis and AC direction is along the y-axis. For this purpose, we show in nb is close to 1.0 × 1013 cm−2 for ng = 0.74 × 1012 cm−2, reaches a minimum or a comparable density to GRE–GRR system. In the ZZ direction the SHF can be tuned by a factor of 2 to 4 between nb = 0.5 × 1013 cm−2 and nb = 1.0 × 1013 cm−2.So far, we have discussed the case of doping effect of BPE–GRR in AC orientation. However, besides electrostatic tuning of graphene and BP, effective mass of BP is readily different in the AC and ZZ direction, as shown in the optical conductivity 12 cm−2, a consistent result of AC orientation  in ξ of the BPE–GRR system, similar to the case in nb = 0.5 × 1013 cm−2, 1.0 × 1013 cm−2 and 5.0 × 1013 cm−2 and ng = 0.74 × 1012 cm−2 and 6.6 × 1012 cm−2 at separation distance, d = 10 nm.To explain the different phenomena observed in 27,28 Here one mode is originated from the BPE side and a second from the GRR; they are labelled SPB and SPG, respectively. The band are characteristics reflections of the SPPs dispersion band and an indication of electromagnetic coupling process of BP and graphene. The strength of the two bright band colors indicates a high transmission coefficient and the coupling strength of the SPPs of black phosphorene and graphene. Therefore, they are the major contributors to the SHF between black phosphorene and graphene. In nb = 0.5 × 1013 cm−2 and for ng = 0.74 × 1012 cm−2. The upper branch is BP like, and lower branch is graphene like. Short momentum and frequency spanning in the lower branch are due to the interband transition damping, specifically at energies ħω < 2μ.26,28,34The contours show two bright bands covering a wide range of frequencies and momentum. These bright bands correspond to the symmetric (lower frequencies) and anti-symmetric (higher frequencies) branches of the coupled SPPs between 2D materials.nb, indicating mismatch between the hybrid SPPs modes of the BP emitter and the graphene receiver, ng = 6.6 × 1012 cm−2, nb similarly increases movement of the bright band and shifts towards higher ω and a lower β. The rise in strong offsets of the branches lead to the overall reduction of the NFRHT observed at a large ng GB/QGG, as an efficiency factor to gain better insight into the performance of BP and graphene, where QGB is the BPE–GRR system total heat flux and QGG is the GRE–GRR system total heat flux.The strong modulation of the NFRHT between GR and BP is shown to be quite generic, and it appears to be influenced by a wide range of parameters. We test this concept by exploring the total radiation, Q, is computed for a range of BP and graphene carrier densities for extremely narrow separation d = 10 nm, intermediate d = 100 nm, and a larger separation d = 750 nm, which are displayed in d = 10 nm to d = 500 and 750 nm, the monotonic decrease of both maximum and minimum total heat flux is obvious because of the weaker SPPs coupling at a larger gap distance. In addition, this reduction is a result of weak near-field coupling and short propagation distance of the evanescence field, which decreases the available of plasmonically-activated modes. The heat flux ratio reaches up to the maximum scale 1.5, at d = 10 nm in both AC and ZZ orientation, η, diminishes for nb of BP and for all ng in AC orientation, while in ZZ orientation of BP leads to a greater η. From this theoretical analysis, we can conclude that BP–GR system shows superiorities compared to GR–GR in designing super Plank radiation heat transfer. As the separation increases further, efficiency of η > 1 is still sustained for the selected doping range, ng from 0.74 × 1012 cm−2 to 4.7 × 1013 cm−2 and nb from 2.0 × 1012 cm−2 to 9.0 × 1013 cm−2, there is a linear bright region that shows efficiency η > 1. Specifically, for ng < 1.18 × 1013 cm−2 and nb above 2.0 × 1012 cm−2 in ZZ orientation and from 0.1 eV to 0.8 eV and BP doping level up to nb = 5.0 × 1012 cm−2 AC orientation. Away from this BP and graphene carrier density range, the ratio is less than 1 irrespective of the BP orientation, which is an undesirable case for NFRHT enhancement of the heterogeneous 2D BP–GR system. However, this theoretical result suggests an alternate solution of realizing enhanced and reduced near-field radiation heat transfer mechanism. In particular, we can freely control the functionality of this heterogeneous system enhancing and reducing heat transfer by changing the orientation of BP and doping of both BP and graphene. Furthermore, our results demonstrate the superiority of this heterogeneous 2D material pair, which give rise to the anisotropic thermal emission by physical reorientation of BP in addition to gate-modulation of graphene and doping of BP.The total heat flux, 12 cm−2). However, in the BPE–GRR system, the NFRHT critical value is significantly enhanced when a gate (>3.0 × 1012 cm−2) is applied to graphene and BP for selective doping. By choosing appropriate parameters of doping level of BP and graphene, a NFRHT 4 times higher can be achieved in BPE–GRR than GRE–GRR. This enhanced NFRHT effect is also realized in the total heat flux BP–GR pairs leading to more than 5 times higher than GR–GR system at d < 100 nm. In addition, by varying the relative orientation of the BP layer, one can switch from amplified NFRHT to reduced or vice versa. The NFRHT in 2D heterogeneous system resulting from preferential orientation would be dependent on the combination of gate-voltage (carrier density). Thus, for a given carrier density of BP and graphene, the heat tunnelling should be adjusted to obtain the desired radiation transfer either enhanced or reduced. We believe that our work contributes to the active pursuit in the field of SPPs supporting hybrid nonidentical 2D structures both made from atomically thin (few layers) materials. This study can further motivate future studies of hybrid surface plasmon induced NFRHT between new discovered anisotropic 2D ReS2 and ReSe2.57,58Our work explores NFRHT originated from SPPs assisted photon transmission for a pair coupled graphene and BP heterogeneous 2D materials. We found that the radiation beyond the Plank limit and enhanced photon tunnelling is favourable in a homogenous graphene–graphene system for low-gate modulation (<3.0 × 10The authors declare no conflict of interest.RA-009-C9RA04695J-s001


All of these make it an excellent candidate for several infrared applications such as photodetection, optical modulation, and optical sensing. However, a standalone monolayer graphene still suffers from a weak infrared absorption, which is ≅2.3%. In this work, a novel configuration of graphene metamaterial embedded inside Bundt optical-antenna (optenna) is demonstrated. It can leverage the graphene absorption up to 57.7% over an ultra-wide wavelength range from 1.26 to 1.68 µm . This range covers the entire optical communication bands of O, E, S, C, L, and U. The configuration mainly consists of a Bundt-shaped plasmonic antenna with a graphene metamaterial stack embedded within its nano-wide waveguide that has a 1.5 µm length. The gold average plasmonic loss is ≅25%. This configuration can enhance graphene ultra-broadband absorption through multiple mechanisms. It can nano-focus the infrared radiation down to a 50 nm spot on the graphene metamaterial, thus yielding an 11.5 gain in optical intensity . The metamaterial itself has seven concentric cylindrical graphene layers separated by silicon dioxide thin films, thus each layer contributes to the overall absorption. The focused infrared propagates tangential to the graphene metamaterial layers , and thus maximizes the light–graphene interaction length. In addition, each graphene layer experiences a double-face exposure to the nano-focused propagating spot, which increases each layer’s absorption. This configuration is compact and polarization-insensitive. The estimated maximum absorption enhancement compared to the standalone monolayer graphene was 25.1 times . The estimated maximum absorption coefficient of the graphene stack was 5700 cm Graphene is an attractive material for photodetection, optical modulation, and optical sensing applications because of its broadband optical absorption, high electron mobility, and electrical conductivity . The syn−1 [2 area consisting of a large number of graphene layers and arranged in a grating device backed by a mirror showed an enhanced unpolarized light absorption of 85% over the solar-spectral bandwidth [−1 at a slot gap of ≅15 nm. Critical coupling with guided Bragg-mirror resonance was used to achieve a narrow-band and polarization-sensitive complete absorption of ≅99% in the monolayer graphene [−1 absorption coefficient. A double layer graphene electro-absorption modulator on a silicon-on-insulator waveguide was demonstrated with an optical absorption of 0.13 dB µm−1 [Different techniques have been reported thus far on manipulating the graphene optical absorption ,16,17,18−1 . A 90 nmandwidth , which agraphene . A graphgraphene , which c dB µm−1 . Dart-ty−1. The absorption enhancement compared to standalone monolayer graphene can reach up to 25.1 times . The enhanced absorption has an almost continuous response over all the optical communication bands: O (1260 to 1360 nm), E (1360 to 1460 nm), S (1460 to 1530 nm), C (1530 to 1565 nm), L (1565 to 1625 nm), U (1625 to 1675 nm), with an overall bandwidth of ≅420 nm. This device configuration is promising for free-space infrared optical communications with applications in photodetection, optical modulation, monitoring, and sensing.In this work, a novel configuration of the graphene metamaterial embedded in a plasmonic waveguide  of Bundt1). At the horn input, the inner and outer diameters are 0.05 and 1.4 µm, respectively, whereas, the inner and outer diameters at the horn output are 0.7 µm and 0.8 µm, respectively, resulting in an annular 50 nm-wide gap. The second stage is a straight waveguide that is 50 nm wide and 1.5 µm long. The graphene metamaterial annular stack is embedded to fill the nano waveguide width. The stack consists of seven concentric cylindrical graphene sheets separated by 7 nm silicon dioxide thin films. The first and last graphene layers are in contact with the waveguide gold inner and outer surfaces. Of course, more graphene layers would enhance the metamaterial absorption further, however, seven layers were enough to fill up the 50 nm-wide waveguide, given that the minimum standard width of the separating thin films is 7 nm. The graphene metamaterial embedded Bundt optenna device was designed and simulated using the three-dimensional finite-difference time-domain (FDTD) method . The FDTσ) library, which is built around the well-known Kubo graphene model. The graphene complex refractive indices (n) are obtained using the formula:rε’ is relative permittivity; ‘oε’ is free-space permittivity; ‘ω’ is the infrared angular frequency; ‘Δ’ is the graphene layer thickness, which is assumed here to be 1 nm. The graphene chemical potential was selected to be 0.1 eV. A multi-coefficient model was used to fit the materials’ complex indices.The gold and silicon dioxide material models were selected from the software complex refractive indices library of Palik  whereas 2 = 1.5 µm) was maximized to achieve almost total absorption within the metamaterial. The waveguide acts as a Fabry–Perot due to multiple reflections between its input and output ports. Thus, the waveguide length was also optimized to have almost total destructive interference among the back-reflected waves at its input. In addition, the inner perimeter of the waveguide gap was selected to be equal to the maximum operating wavelength to sustain the mode propagating inside the waveguide. As seen in All of the Bundt optenna and the graphene metamaterial dimensions were optimized through numerical iterations to minimize the infrared back reflections and thus ensure the maximum input power to the device and in turn the maximum graphene absorption. The input coaxial horn dimensions were optimized to match the optical impedance of the free-space incident radiation. Additionally, the plasmonic waveguide dimensions were optimized to match the output optical impedance of the first stage coaxial horn. The length of the plasmonic waveguide  over the volume of the 1 nm thick graphene layer. This was achieved by applying two volumetric monitors, one for the electric field and the other for the refractive index within the absorbing layer. The absorbed power density was then calculated by E2 Imagε  where ‘|2 at the input to 0.11 µm2 at the output . After subtracting the gold losses on the horn gold surface, the gain  in the inferred intensity becomes ≅11.5 . Still, the dip in the curve (3.8%) corresponds to the maximum back reflection and thus yields a minimum graphene absorption. The intensity gain at the waveguide input for the 1476 nm wavelength was almost 11 dB, which yielded a maximum absorption after interaction with the metamaterial. out = Iin e−αL2, where ‘Iin’ is the input intensity to the waveguide, ‘Iout’ is the output intensity of the waveguide, and ‘L2′ is the metamaterial length that equals the plasmonic waveguide length. The maximum absorption coefficient was 5700 cm−1, which corresponds to the maximum graphene absorption. The average absorption coefficient was ≅4500 cm−1, which was considered as one of the record-high reported values of the graphene absorption coefficient. Still, the dip in the curve corresponded to the minimum graphene absorption and maximum back reflection.2) separating thin films , covering the entire optical communication bands . The configuration of the graphene-embedded Bundt optenna is promising for free-space infrared optical communications with applications in photodetection, optical modulation, monitoring, and sensing.A novel configuration to enhance the ultra-broadband infrared absorption coefficient in a graphene metamaterial using a Bundt optical antenna was numerically demonstrated. The graphene metamaterial stack consisted of seven layers of separated cylindrical graphene layers with a length of 1.5 µm. The antenna can nano-focus infrared radiation and propagate it tangential to each graphene layer, thus allowing for a double-face exposure. This polarization-insensitive configuration gives a 10.6 dB intensity gain and enhanced the graphene metamaterial absorption of up to 57.7%. The estimated maximum absorption coefficient of the graphene metamaterial was 5700 cm1 + L2), which constitutes the Bundt central post. The CVD can be used to deposit alternating layers of graphene and silicon dioxide of metamaterial on the rod bottom surface with a length of 1.5 µm. After that, CVD can be used to deposit more gold onto the metamaterial together with the entire rod sidewalls until reaching the full width of a unit cell. Then, the concentric conical horn can be formed inside the gold using focused ion beam milling while controlling the milling area by changing the beam current and etching time.Although this work focused mainly on the numerical simulations of graphene metamaterial embedded within a Bundt optenna, it is worth mentioning a hint on one suggested method for fabrication. A unit cell can be fabricated from the inside out. The chemical vapor deposition (CVD) method can be used to fabricate a cylindrical gold rod with a diameter of 0.7 µm and a total length of 3.5 µm  spectroscopy. The fabricated Bundt optenna samples without embedded graphene metamaterial can be tested with micro-FTIR to measure the optical absorption spectra across different wavelength ranges. Next, other fabricated Bundt optenna samples with the embedded graphene metamaterial can be tested to measure the difference in the optical absorption spectra over the same wavelength bands, and thus estimate the graphene metamaterial absorption by itself. Another method is to use a scattering-type scanning near-field optical microscope (SNOM) that is combined with a pseudo-heterodyne detection module, which can image the scattered near-fields of the micro- and nanostructures at a very small spatial resolution as small as 10 nm. The SNOM can map the scattered electric fields from the fabricated Bundt optenna samples with/without embedded graphene metamaterial, similar to extracting the optical power absorption by the Bundt gold material and then the graphene metamaterial.


Results from studying the effect of an applied electric voltage on the Raman spectrum of graphene deposited on a lithium niobate crystal substrate with a ferroelectric domain structure are presented. The use of the principal component method for data processing in combination with correlation analysis made it possible to reveal the contribution to the change in the spectra associated with the linear deformation of the substrate due to the inverse piezoelectric effect. An effect of the graphene coating peeling was found. Furthermore, bending deformations of the graphene coating associated with the presence of a relief on the substrate were found. An analysis of the change in the spectra of graphene under the application of an electric voltage made it possible to determine the height of this relief. The com devices . The dev devices ,9. Recen devices ,11,12,13Further investigation of the characteristics of graphene/piezoelectric structures will make it possible to expand their application in sensorics, acousto-optics and optoelectronics. An important direction is the development of non-destructive methods for characterizing both the substrate material and the 2D coating itself (graphene). One attractive technique is Raman spectroscopy. The main problem of this method is the noise of the spectra, as a result of which it is very challenging to reveal the influence of any small effects in the structures under study on the Raman spectra. This work presents results from a study on the possibility of using the principal component method in combination with correlation analysis to analyze the effect of piezoelectric substrate deformation on the coating of monolayer graphene. Previously, this approach has been used to probe changes in the composition and piezoelectric properties of crystals ,16.To study the effect of compression/stretching strain on the Raman spectra of monolayer graphene, a sample was fabricated based on a Z-cut substrate of a lithium niobate single crystal with a ferroelectric domain structure. The domain width is 500 µm for a sample thickness of 0.5 mm.3 substrate. An aluminum electrode is deposited on the underside of the substrate. The application of electrical voltage to the top and bottom electrodes causes periodic deformation of the substrate due to the inverse piezoelectric effect. The sign of deformation is determined by the direction of the crystallographic Z axis of the LiNbO3 substrate. A schematic representation of the deformed sample is shown in On the upper face of the substrate, a monolayer graphene coating is formed by the transfer method. This coating serves as the upper electrode. A monolayer CVD graphene on a copper foil manufactured by Rusgrafen was used. The transfer is carried out as follows. A polymethyl methacrylate (PMMA) film is deposited onto a copper foil with grown monolayer graphene by spin coating. After drying the PMMA, the graphene is removed from the backside of the copper foil in oxygen plasma. Then, the foil is etched in a solution of ferric chloride. After that, PMMA with graphene is washed in deionized water and caught on a LiNbOThe deformation of the graphene coating occurs due to the deformation of the substrate and can be studied using Raman spectroscopy. For this purpose, a Bruker Senterra Raman microscope with a 50× objective is used. The laser wavelength is 532 nm, with a power of 10 mW. The laser beam diameter is 2 μm. Electrical voltage is applied in the range from −1500 V to +1500 V with a step of 250 V. Thus, the maximum electric field strength applied to the sample is ±3 kV/mm, which is far from the values at which nonlinear effects appear .β) = 0.9 and a correlation factor |r| ≥0.5, the sample size should be at least N = 29. A total of 73 spectra was obtained, which corresponds to a total of three cycles of changes in the applied electrical voltage. Thus, the analysis of the results should be counted as statistically reliable and will make it possible to establish the presence of a correlation between the changes in the spectra and the applied electrical voltage.−1, and the I(2D)/I(G) ratio is greater than 1 [The spectra contain peaks characteristic of monolayer graphene, since the full width at half the maximum of the 2D and G line peaks is less than 40 cmr than 1 . There ir than 1 ,20, indir than 1 ,22. ThusIt should be noted that a significant spread of the background of the obtained spectra was observed, as well as the noisiness of the peaks. In addition, it is known that the dependence of the position and intensity of the G-peak of graphene, depending on the applied electric field, is nonlinear . The comr on the number of the main component.N scalar observations, then the Pearson correlation coefficient is defined as [Aμ and Aσ are the mean and standard deviation of A, respectively, and Bμ and Bσ are the mean and standard deviation of B.The correlation coefficient of two random variables is a measure of their linear dependence. If each variable has fined as r=1p-values lies between 0 and 1, where values close to 0 correspond to a significant correlation in r and a low probability of observing the null hypothesis. The p-values are calculated from the deviation between the observed value and a chosen reference value, given the probability distribution of the statistic, with a greater difference between the two values corresponding to a lower p-value. Mathematically, the p-value is calculated using integral calculus from the area under the probability distribution curve for all values of statistics that are at least as far from the reference value as the observed value is relative to the total area under the probability distribution curve.The range of p = 6.05 × 10−6.A correlation corresponding to the above parameters of statistical significance is observed for the seventh principal component PC7: r = 0.62, The linear regressions of PC7 for the full data set and for each cycle separately are shown in A comparison of the load vector for the first principal component, which is essentially a spectrum cleared of changes caused by any influences, with the load vector for the seventh principal component, for which a linear dependence on the applied electrical voltage was found, is shown in 2 in A periodical change in the intensity of the peak presumably associated with nitrogen adsorbed N2 in  on the g−1 towards larger wave numbers, which changes the position of the maximum in the range ±5 cm−1, depending on the magnitude and sign of the applied electric field. The characteristic shape of the peak, shifted by 6 cm−1 towards lower wave numbers from the 2D peak, indicates a change in its amplitude and width at half height.Most interesting is the fact that the graphene peaks shifted from the position of the maximum of the main graphene peaks, showing a linear dependence on the magnitude of the applied electrical voltage. Thus, next to the G peak, a satellite is found shifted by 11 cmObviously, the change in the position of the 2E peak is associated with the inverse piezoelectric effect. The combination of changes in the peaks next to G and 2D can be explained by the presence of bending deformations of the graphene coating .δh according to the inverse piezoelectric effect equation for the given experimental conditions:δh—crystal size changing along voltage applying direction; V—applied voltage; d33—piezoelectric strain coefficient of the LiNbO3 crystal. Piezoelectric constants of LiNbO3 can be found in [d33 = 6.2 pC/N. A simple calculation yields the change of crystal size for the applied voltage as V = 1500 V δh = 9.3 nm. The second part of the crystal is stretched by the same amount.Based on the above analysis, a conclusion can be drawn about the structure of the sample under study. First, the domain structure initially had a relief that caused the bending of the graphene coating, which manifested itself in the splitting of the G and 2D peaks. The application of voltage caused a change in the height of the relief. Based on the fact that the change in the position of the peak did not reach the main peak of graphene, it can be concluded that the relief did not disappear during the change in voltage. This means that the initial height of the relief was greater than its maximum change due to the application of an electric field. The height of the initial relief can be estimated according to the following reasoning: as a result of the application of an electric field, one part of the crystal is compressed by found in , such th−1 towards larger wave numbers is observed. The peak shifts towards lower wave numbers when the relief height decreases by the same value. It is shown above that the dependence of the peak position on the applied voltage is linear. Thus, in order for the peak to coincide with the main graphene peak G, it is necessary to apply approximately twice as much voltage, i.e., 3000 V. Consequently, the height of the domain structure relief without the application of an electric field is 37.2 nm.In the case of increasing relief height, a shift of the peak by 5 cmAs a result of this research, a linear dependence of the Raman spectrum of graphene deposited on the domain structure of a lithium niobate crystal on the applied electric voltage was isolated. This part of the contribution to the change in the Raman spectrum is associated with the deformation of the piezoelectric substrate due to the inverse piezoelectric effect. It was found that, during the cyclic application of an electric field, the effect of substrate deformation on the change in the Raman spectrum weakens from cycle to cycle. This may be due to the peeling of the graphene coating. It was also found that the substrate has a relief due to the presence of a ferroelectric domain structure.


Clinically validated, automated arm-cuff blood pressure measuring devices (BPMDs) are recommended for BP measurement. However, most BPMDs available for purchase by consumers globally are not properly validated. This is a problem because non-validated BPMDs are less accurate and precise than validated ones, and therefore if used clinically could lead to misdiagnosis and mismanagement of BP. In response to this problem, several validated device lists have been developed, which can be used by clinicians and consumers to identify devices that have passed clinical validation testing. The purpose of this review is to describe the resources that are available for finding validated BPMDs in different world regions, to identify the differences between validated device lists, and describe current gaps and challenges. How to use validated BPMDs properly is also summarised. It is difficult to measure blood pressure (BP) accurately using the traditional manual auscultatory method. Automated, ‘press-button’ BP measuring devices (BPMDs) have simplified the measurement process greatly. Automated BPMDs eliminate most user-related errors in BP measurement that are common to manual BP , 2. HoweAll clinical validation studies should be conducted independently of the manufacturer to avoid potential conflicts of interest. New clinical validation studies of cuff-based BPMDs should adhere to the recently published ISO standard ISO 81060-2:2018) , also co0-2:2018 Most automated BPMDs available for purchase by consumers globally have not been properly validated for accuracy –9. PotenThis problem has created the need for resources to help clinicians and consumers determine which BPMDs have passed clinical validation testing. The primary resources that have been developed are validated device lists. There are several lists that summarise information from the clinical validation studies of BPMDs and are designed to be accessible to a non-specialist audience . This brhttps://bit.ly/ResourcesBP.Online validated device lists can be used to find validated BPMDs. The lists are developed and maintained by hypertension societies or not-for-profit collaboratives, as well as by one for-profit private organisation Table . HyperteMost BPMDs are validated for use in ‘general populations.’ This refers to subjects aged over 12 years, at least 30% men and 30% women, with a wide range of arm circumferences and BP levels . HoweverAs shown in Table Consensus still needs to be achieved on the definition of equivalent/derivative BPMDs. The levels of evidence currently required from manufacturers for a device to be accepted as equivalent on a validated device lists are varied. These range from acceptance in good faith on the basis of some equivalence information being provided to requiring more stringent notarised affidavits or evidence of adherence to European Union criteria (MEDDEV 2.7/1 rev 4) , as are the two general lists that are designed for global use . There is anecdotal evidence from some low-middle income countries that finding validated BPMDs is challenging . There aWeak and fragmented regulatory frameworks around BPMD validation has been identified by the Pan American Health Organisation/World Health Organisation flagship hypertension control program, HEARTS in the Americas . HEARTS There are some differences in the types of information provided by validated device lists about BPMDs for use in special populations and use of different cuff sizes. Some validated device lists use specific labelling or separate pages for BPMDs that are validated in special populations. This is an important step to ensure that the individuals who require these BPMDs can easily identify and select a suitably validated device. Some validated device lists provide information about the number and type of cuff sizes that are available for each BPMD. Cuff size and arm circumference range are important device characteristics because the accuracy of BP measurement is contingent on using a correctly sized cuff , 28, 29.Validated BPMDs must be used properly to ensure accurate BP measurements are obtained. There are at least five steps required to obtain accurate BP measurements, which are summarised in a video that was developed by the Welch Center for Prevention, Epidemiology and Clinical Research at the Johns Hopkins Bloomberg School of Public Health . The firhttps://bit.ly/ResourcesBP [www.stridebp.org/training).Those who are responsible for measuring BP should be skilled in the aforementioned steps required to obtain accurate readings. After initial training, regular refreshers on the correct measurement technique should be mandated . Any traourcesBP . The STRourcesBP . The e-lIn summary, most automated cuff BPMDs available for purchase by consumers globally have not been clinically validated for accuracy. Validated device lists enable consumers to identify accurate BPMDs. However, there are differences between validated device lists due to the validation protocols that are accepted, procedures for inclusion of equivalent devices and the types of BPMDs listed. Further, the different validated device lists may not be relevant across different regions of the world. Altogether, these differences may create confusion for consumers trying to identify clinically validated BPMDs. Other gaps and challenges to optimal use of validated device lists relate to identification of (1) validated BPMDs available for purchase in low-middle income countries, (2) BPMDs validated in special populations, and (3) cuff sizes available for purchase with each device. While selecting a clinically validated BPMD is essential to accurate BP measurement, these devices must also be used properly so that accurate BP measurements are obtained. Skill achievement and maintenance with training and refreshers to ensure that the five essential measurement steps are followed consistently is recommended.


A key component of the marine sulfur cycle is the climate-active gas dimethylsulfide (DMS), which is synthesized by a range of organisms from phytoplankton to corals, and accounts for up to 80% of global biogenic sulfur emissions. The DMS cycle starts with the intracellular synthesis of the non-gaseous precursor dimethylsulfoniopropionate (DMSP), which is released to the water column by various food web processes such as zooplankton grazing. This dissolved DMSP pool is rapidly turned over by microbially mediated conversion using two known pathways: demethylation (releasing methanethiol) and cleavage (producing DMS). Some of the formed DMS is ventilated to the atmosphere, where it undergoes rapid oxidation and contributes to the formation of sulfate aerosols, with the potential to affect cloud microphysics, and thus the regional climate. The marine phase cycling of DMS is complex, however, as heterotrophs also contribute to the consumption of the newly formed dissolved DMS. Interestingly, due to microbial consumption and other water column sinks such as photolysis, the amount of DMS that enters the atmosphere is currently thought to be a relatively minor fraction of the total amount cycled through the marine food web—less than 10%. These microbial processes are mediated by water column temperature, but the response of marine microbial assemblages to ocean warming is poorly characterized, although bacterial degradation appears to increase with an increase in temperature. This review will focus on the potential impact of climate change on the key microbially mediated processes in the marine cycling of DMS. It is likely that the impact will vary across different biogeographical regions from polar to tropical. For example, in the rapidly warming polar oceans, microbial communities associated with the DMS cycle will likely change dramatically during the 21st century with the decline in sea ice. At lower latitudes, where corals form an important source of DMS (P), shifts in the microbiome composition have been observed during thermal stress with the potential to alter the DMS cycle. In comp−1 , and the−1 . However−1 , with ne−1 .42−) and methanesulfonate (MSA) aerosols, but the efficiency of this process in producing CCN has been questioned [Once ventilated to the atmosphere, DMS is rapidly oxidized to form non-sea-salt sulfate , the precursor compound to DMS. It has been long recognized that different phytoplankton genera synthesize DMSP to varying degrees , with coRecent observational studies have confirmed the importance of marine DMS emissions to atmospheric CCN ,29 and nMarine bacteria were first recognized for their role in the decomposition of organic material and the remineralization of inorganic nutrients, a role that only became fully understood during the 1980s. Azam and colleagues described a “microbial loop” in which dissolved organic matter released by phytoplankton and grazers is consumed by bacteria. Bacteria are consumed by protozoa, which are then consumed by microzooplankton that are part of the traditional grazing chain . It was However, the role of microbial heterotrophs in the DMS (P) cycle was not fully appreciated at the time of the original CLAW hypothesis. Indeed, it was not until two seminal papers by Simo  and Kien2 and warming on the marine microbial loop and the cycling of DMS (P). As noted by Cavicchioli et al. [Algal intracellular DMSP can be released to the water column via a number of processes, namely, zooplankton grazing , exudatii et al. , the rec42−), which is abundant in the ocean. Sea-to-air transfer of S occurs via the ability of phytoplankton to synthesize cellular DMSP, the microbial-mediated transformation to DMS, and its subsequent emission to the atmosphere, where it has a possible involvement in climate regulation. As noted above, DMS provides the main biogenic source of reduced S to the global atmosphere. The annual global production of DMSP by deep ocean phytoplankton  is conservatively estimated as 3.8 Pg C year−1 [−1 [It is now recognized that organosulfur compounds both in the water and the cell play important roles in the marine food web by mediating interactions between phytoplankton and heterotrophic bacteria ,60. SulfC year−1 —this is C year−1 . DMSP isC year−1 ,64 and cC year−1 ,65. FielC year−1 ,67, wherar−1 [−1 .3SH), via the demethiolation pathway, or to DMS via the enzymatic cleavage pathway, and the consumption of both dissolved DMSP and DMS by bacteria. Interestingly, MeSH is also a volatile compound but its sea-to-air flux estimates are scant, although it has recently been estimated to be comparable to, albeit less than, that of DMS [A conceptual model of the key processes in the marine cycle of DMS and its precursor compound DMSP is presented in t of DMS .However, this conceptual model is only partly complete as Li et al.  describeAlgal intracellular DMSP is released into the water column by a variety of processes, e.g., exudation , cell lyPelagibacterales) clade, which is of ancient evolutionary origin and comprises the most abundant and ubiquitous clade of heterotrophic marine bacteria in the oceans, estimated to make up 25% of the plankton community [The uptake and assimilation of dissolved DMSP can satisfy the energy, carbon, and sulfur demands of entire marine bacterial communities . A case ommunity . Tripp eommunity  observe Roseobacter group  can establish mutualistic relationships with phytoplankton that are, in part, maintained by exchanges of DMSP. A similar sulfonate-based mutualistic interdependence between phytoplankton and SAR11 organisms has also been found in the North Pacific [Furthermore, O’Brien et al.  point ou Pacific .From the conceptual model presented in Measurements of DMS yield ,81,82,83Roseobacter group, a ubiquitous group of marine bacteria, are well-known MeSH producers [Members of the roducers , and in roducers  posited roducers , which croducers .2-driven impacts in the coming decades, including ocean acidification (OA), warming, deoxygenation, and loss of sea ice cover [2 increases appear to confound the situation, with decreases in phytoplankton biomass combined with increases in bacterial production reported in the Arctic [The global oceans face multiple COce cover , all of ce cover . Warmingce cover ,90. The ce cover ,92,93. Sce cover . Thus, tce cover . Howeverce cover . Despitece cover . Howevere Arctic .Over the last two decades, numerous studies have explored the effect of climate change on the broad structure and functioning of the marine food web and the possible changes in net primary production (NPP). This has been attempted through both modelling and mesocosm experiments ,103,104.Prognostic DMS models have been under development since the early 1990s . Most ofThe difficulty of applying DMS prognostic models at the global scale has prompted efforts to develop globally applicable diagnostic schemes that predict DMS seawater concentration based on variables such as surface chlorophyll and light, which can be retrieved using satellite remote sensing algorithms ,119,120.Notwithstanding the lack of a globally applicable DMS prediction algorithm , there hBock et al.  examine −1 reduction in ocean carbon uptake by 2100 under the high-emission scenario RCP8.5. The inclusion of more complex ecological parametrizations in the ESM simulates a more modest decline in the ocean carbon sink capacity of ~0.06 PgC year−1 during the 21st century [−1 [As indicated above, it is likely that the simulated variability in future DMS trends is related to the inability of current ESMs to accurately project future changes in marine primary production. Depending on whether the ESM formulation has biological processes that are temperature-dependent or not, simulated NPP can either increase or decrease under projected climate change . Indeed, century , comparetury [−1 .2 level on the DMS (P) cycle by perturbing either one or both variables simultaneously. Avgoustidi et al. [2 (700 ppm) in Emiliania huxleyi and found that DMS production was roughly halved, which they attributed to a change in intracellular DMSP at lower pH values. Spielmeyer and Pohnert [Thalassiosira pseudonana, Phaeodactylum tricornutum) and a prymnesiophyte (Emiliania huxleyi) under the influence of increased temperature, by 6 °C, and elevated CO2 to 790 ppmv. The diatoms and prymnesiophytes revealed opposite trends for DMSP. In diatoms, increased CO2 and temperature led to decreased DMSP concentrations, however elevated levels of this metabolite under the influence of these parameters were observed for Emiliania huxleyi.Over the past decade, several mesocosm and ship-board bioassay experiments have examined the impact of future increased temperature and pCOi et al.  looked a Pohnert  analyzedEmiliania huxleyi. Four different environmental conditions were tested: ambient, elevated CO2 (+CO2), elevated temperature (+T), and elevated temperature and CO2 (+TCO2). In comparison to the ambient treatment, average DMS production was about 50% lower in the +CO2 treatment. Importantly, temperature had a strong effect on DMS production, and the impacts outweighed the effects of a decrease in pH, with a more than doubling of intracellular DMSP.Arnold et al.  also rep2 (160–830 ppm) favored the growth of large diatoms, which outcompeted other phytoplankton species and reduced the growth rate of smaller, DMSP-rich dinoflagellates. This decreased the grazing rate of heterotrophic dinoflagellates, resulting in reduced DMS production via grazing activity, and highlights the influence of community composition changes on future DMS production under warming.Park et al.  conducte2 perturbation experiments in NW European waters and found that the changes induced consistent, marked increases in DMS and decreases in DMSP, in contrast to results from longer-term mesocosm experiments. Bacterially mediated DMS processes appeared to be insensitive to ocean acidification, with no detectable effects on dark rates of DMS consumption and gross production and no consistent response seen in bacterial abundance [Hopkins and Archer  conductebundance .Amphidinium carterae to investigate the effects of elevated CO2 concentration and temperature on the growth and production of DMSP and DMS, however they found no significant effects on the concentrations and cell-normalized concentrations of DMSP or DMS. Saint-Macary et al. [Li et al.  conductey et al.  observed2 resulted in a 15% and 40% decline in average concentrations of DMS compared to the control. Results from S-35-DMSPd uptake assays indicated that neither concentrations nor microbial scavenging efficiency of dissolved DMSP was affected by increased pCO2. However, their results show a reduction of the mean microbial yield of DMS by 34% and 61% in the 2 × pCO2 and 3 × pCO2 treatments, respectively [Significant positive correlations were found between bacterial production and concentrations of DMS in Canadian waters during autumn. The 13-day mesocosm experiments were characterized by blooms of diatoms, with the results pointing to temperature-associated enhancement of bacterial DMSP metabolism and large increases to DMS (>200%), negating any decreases due to acidification . Howeverectively .Emiliania huxleyi, it would seem likely that the adverse effect of increased pH on the growth of this calcifying organism would also result in a negative impact on the overall production of both DMSP and DMS [Clearly, the results of these perturbation experiments are varied and appear to sensitively depend on the phytoplankton groups present, the ocean region examined (polar or temperate), and the perturbation applied. Subtle interactions are also evident, which can lead to counter-intuitive results. For instance, in studies of natural communities that are dominated by the DMSP-rich species  and DMS , but thi and DMS .2 changes on future DMS (P) production. It is apparent that the results are sensitive not only to the phytoplankton functional group present but also to the length and type of experiment (mesocosm versus bioassay) and thus the potential for shifts in the community composition to occur.The range of different results in these experiments makes it difficult to present a synthetic view of the impact of temperature and/or pCOThe Coupled Model Intercomparison Project Phase 6 (CMIP6) provides climate projections for a range of scenarios to understand the past, present, and future climate. For a better understanding of the impact of the projected climate change on SST, mixed layer depth (MLD), and pH, these are examined for both a contemporary (2001–2020) and end of century (2081–2100) climate, as simulated by CMIP6 models under a SSP2-4.5 and SSP5-8.5 Shared Socioeconomic Pathway (SSP). The possible impact of ocean warming and acidification on DMS (P) production and cycling is then discussed below.Model output for monthly mean SST (°C) and MLD (m) was obtained from the Australian Community Climate and Earth-System Simulator-Coupled Model version 2 (ACCESS-CM2)  for the −2) is expected by 2100, assuming a shift towards renewable energy and sustainable development [−2) is expected by 2100, assuming that development will be largely driven by fossil fuels [https://esfg-node.llnl.gov/search/cmip6 (accessed on 19 June 2022)).The historical simulations run to 2014, with solar variability, volcanic aerosols, and anthropogenic-driven changes in atmospheric composition (GHG and aerosols) forced by datasets that are largely based on observations . The conelopment . For SSPil fuels . Data weOcean warming poses a serious threat to many marine ecosystems. Anthropogenic GHG emissions trap solar radiation within the Earth’s atmosphere, leading to an accumulation of excess heat energy. More than 90% of this excess heat is stored in the ocean, leading to a 0.5 °C rise in global mean SST over the past 40 years . By the Temperature impacts the physiology of marine organisms, ocean currents, mixing, and the availability of nutrients in the ocean. Rising SST is predicted to contribute to a stratification of the upper ocean layers and an overall reduction in global mixed layer depth, particularly in the Southern Hemisphere mid–high latitudes and North Atlantic Ocean, where present-day MLD is deepest . Reduced2. Dissolution of CO2 forms carbonic acid, which readily dissociates into bicarbonate ions and protons, thereby reducing pH. Current anthropogenic CO2 emissions are 10.1 ± 0.5 Gt C year−1 [2 is absorbed by the ocean and has led to a decrease in global mean ocean pH of 0.1 units over the past 100 years [2 emissions by the end of this century under an SSP2-4.5 scenario, global mean ocean pH is predicted to decline by −0.18  concentrations and a low carbonate system buffering capacity, mainly due to the increased solubility of COd waters . This mad waters , althougd waters . Althougd waters ,158.In contrast, Hopkins et al.  report fOcean warming and acidification pose serious threats to tropical coral reef ecosystems, with impacts already evident through more frequent and severe coral bleaching events ,162,163,Reef-building Scleractinian corals have relatively narrow temperature tolerance ranges ,166,167,−1 of DMS  [a) in the Great Barrier Reef (GBR), Australia, average approximately 30 ppt in winter and 100 ppt in summer [a concentration reached 1149 ppt when the coral reef flat was thermally and osmotically shocked by rainfall [Coral reef ecosystems are important regional sources of dissolved and atmospheric sulfur, emitting an estimated 0.05–0.08 Tg yearear−1 S) . Reporten summer ,174, andn summer . On one DMSP biosynthesis and cleavage to DMS is upregulated in corals in response to thermal and irradiance stress ,176,177.Shifts in the coral holobiont microbiome can result in a change in DMS (P) production ,185. DMSIf corals can withstand rising ocean temperatures through natural means, such as the recruitment of temperature-tolerant endosymbionts ,184, DMSPorites corals have declined by 0.36% and 1.02%, respectively [Exacerbating the impacts of ocean warming, ocean acidification has already led to a decline in the bioavailability of carbonate ions , limitinectively . AlthougAs for ocean regions, algal and microbial communities play an important role in the production and cycling of DMS (P) in the coral holobiont and in coral reef waters . IncreasFurther, corals and algae compete for light, nutrients, and space in the reef matrix. Predicted declines in coral cover due to ocean warming, acidification, predation, and declining water quality may lead to an increase in algal biomass in coral reefs. Increased algal biomass is often observed after mass coral bleaching and mortality events , impedinEcological interactions between marine bacteria and phytoplankton are now recognized as playing a central role in the ocean’s major biogeochemical cycles. How these interactions may be modified as a result of climate change is the focus of this review, which examined the role of heterotrophic bacterioplankton in the cycling of a suite of organo-sulfur compounds, such as DMS. This volatile compound and its precursor DMSP are ubiquitous in the oceans, being synthesized by a large range of marine organisms from algae to corals. After ventilation, DMS constitutes the largest source of natural sulfur to the atmosphere and is thus also an important contributor to the atmospheric burden of sulfate aerosol with key relevance to the Earth’s radiative balance and climate.Due to its synthesis by numerous marine organisms, DMSP and its related compounds are an important source of DOM for bacterial heterotrophy in the surface ocean, where it has been estimated to contribute up to 100% of the sulfur requirements of some important marine bacteria groups. There is growing evidence that sulfonates serve as an ecologically important currency for nutrient and energy exchange between autotrophs and heterotrophs, highlighting the importance of organo-sulfates in regulating ecosystem function. This suggests that climate change impacts on the synthesis and cycling of DMS (P) could have important future consequences for the structure of the marine food web.2 changes on future DMS (P) production. It is apparent that the results are sensitive not only to the phytoplankton functional group present but also to the length and type of experiment conducted.However, perturbation experiments seeking to understand the impact of warming and OA on the synthesis of DMS have so far yielded equivocal results. The range of different results makes it difficult to provide a synthetic view of the impact of temperature and/or pCOAlthough numerous attempts have been made to numerically model the impact of warming on the DMS (P) cycle, there is no general agreement in the literature with regards to the sign and amplitude of the future trend in DMS concentration and sea-to-air flux. Importantly, these efforts are also constrained  by a lack of an explicit temperature-dependent parameterization of the key bacterial-mediated processes, such as demethylation and enzymatic cleavage. Considering the critical roles DMS and its precursor DMSP appear to play in supplying carbon and sulfur to marine heterotrophs, future efforts should focus on informing the many uncertainties still apparent in the literature regarding the ecological role of these compounds, and especially the nexus between future CC impacts on marine bacteria and the possible resultant changes in the marine DMS (P) cycles.