This study seeks to investigate the interaction profile of the L5 protein of oncolytic adenovirus with the overexpressed surface receptors of pancreatic cancer. This is an important area of research because pancreatic cancer is one of the most fatal malignancies with a very low patient survival rate. Multiple therapies to date to improve the survival rate are reported; however, they show a comparatively low success rate. Among them, oncolytic virus therapy is a type of immunotherapy that is currently under deliberation by researchers for multiple cancer types in various clinical trials. Talimogene laherparepvec (T-VEC) is the first oncolytic virus approved by the US Food and Drug Administration (FDA) for melanoma. The oncolytic virus not only kills cancer cells but also activates the anticancer immune response. Therefore, it is preferred over others to deal with aggressive pancreatic cancer. The efficacy of therapy primarily depends on how effectively the oncolytic virus enters and infects the cancer cell. Cell surface receptors and their interactions with virus coat proteins are a crucial step for oncolytic virus entry and a pivotal determinant. The L5 proteins of the virus coat are the first to interact with host cell surface receptors. Therefore, the objective of this study is to analyze the interaction profile of the L5 protein of oncolytic adenovirus with overexpressed surface receptors of pancreatic cancer. The L5 proteins of three adenovirus serotypes HAdV2, HAdV5, and HAdV3 were utilized in this study. Overexpressed pancreatic cancer receptors include SLC2A1, MET, IL1RAP, NPR3, GABRP, SLC6A6, and TMPRSS4. The protein structures of viral and cancer cell protein were docked using the High Ambiguity Driven protein–protein DOCKing (HADDOCK) server. The binding affinity and interaction profile of viral proteins against all the receptors were analyzed. Results suggest that the HAdV3 L5 protein shows better interaction as compared to HAdV2 and HAdV5 by elucidating high binding affinity with 4 receptors . The current study proposed that HAdV5 or HAdV2 virus pseudotyped with the L5 protein of HAdV3 can be able to effectively infect pancreatic cancer cells. Moreover, the current study surmises that the affinity maturation of HAdV3 L5 can enhance virus attachment with all the receptors of cancer cells. Pancreatic cancer is the most fatal and extremely aggressive malignancy with a high recurrence and mortality rate . ChemothAmong a range of cancer therapies, oncolytic virus therapy is a novel targeted immunotherapy and has obtained noticeable attention. Viruses selectively exterminate cancer cells through lysis resulting in antitumor immune simulation. The specificity and effectiveness of oncolytic viruses make them an attractive therapeutic approach. However, success lies under the goal to modify  viruses for enhanced tumor selectively and immune stimulation. Furthermore, the co-administration of oncolytic viruses with other therapeutic agents has also been found to be very effective . The verHuman adenovirus is a commonly used oncolytic virus against various cancer types. Gendicine and Oncorine are the two adenoviruses that are currently approved for the treatment of cancer in China . ONXY-15log2FC > 1) in all the datasets and also identified in The Cancer Genome Atlas (TCGA) cohort. These genes include MET, NPR3, IL1RAP, SLC2A1, SLC6A6, GABRP, and TMPRSS4, which encode for cellular membrane receptors (Adenovirus cell attachment is instigated by the interaction of the knob domain region of the fiber protein with the human cell receptors . The CAReceptors . The oveeceptors –27.SLC2A1 gene encodes for glucose transporter type 1 (Glut1), which is a member of the GLUT protein family. It is responsible for glucose transport across the cell membrane. The structure of SLC2A1 comprises 12 transmembrane helices, with 6 extracellular and 7 cytoplasmic domains , 29. Carα chain and transmembrane 140-kDa β chains (β -catenin (Wnt) pathways. These signaling pathways cause cell proliferation, evading apoptosis and leading to the development of angiogenesis and invasiveness  also known as NPR-C is a member of the glycosylated receptor family. This is present in the form of a dimer, and it is responsible for the regulation of natriuretic peptide (NP) hormones in the blood . Three NSLC6A6 encodes for multipass membrane transport protein. It is a member of the solute carrier 6 (SLC6) family of sodium and chloride ion-dependent transporter proteins , 42. TheAll the above-enlisted receptors have a direct or indirect link with the progression of pancreatic cancer. In this study first, the structures of all receptors are retrieved. For SLC2A1, MET, IL1RAP, and NPR3 receptors, Protein Data Bank (PDB) structures are available, while for other receptors, machine learning-based structure prediction is utilized to generate the protein structure. The structures of the L5 proteins for all three serotypes are downloaded from PDB. All the receptors are docked with the HAdV2, HAdV3, and HAdV5 L5 proteins. The binding affinity and interaction pattern of each complex are analyzed to identify which target receptor effectively binds with the L5 proteins. For the positive control, the interaction pattern of CAR and DSG2 with the HAdV2, HAdV3, and HAdV5 L5 proteins is analyzed and compared with pancreatic cancer receptors. The interaction analysis of CR1 with the HAdV5 L5 protein and CD46 with the HAdV3 L5 protein is also performed to compare the binding affinity of these receptors with pancreatic cancer receptors. NPR3, GABRP, SLC6A6, and TMPRSS4 showed the highest binding affinity with the HAdV3 L5 protein, while SLC2A1 and IL1RAP show better interaction with HAdV2.The overall workflow of this study includes structure retrieval and preparation of human receptors and virus L5 protein. Then the docking of receptors with L5 and interaction analysis of both molecules was performed. The overall workflow of the study is given in https://www.uniprot.org/) against each UniProt ID provided in https://blast.ncbi.nlm.nih.gov/Blast.cgi) was performed to determine percentage sequence identity based on which structural homologs were identified. The MET, SLC2Al, NPR3, IL1RAP, CAR, DSG2, CD46, and CR1% identity was greater than 97%, which means that the protein structure for these receptors was already available. For the SLC2A1 receptor, multiple PDB structures with a similarity >99% were identified with a sequence length of 492 amino acids. Out of all hits, a structure with high resolution (2.4 Å) with PDB ID 6THA was selected and downloaded from PDB (https://www.rcsb.org/). The PDB ID 1KY0 was selected for the NPR3 receptor based on the PSI-Blast result. The 1KY0 structure of the NPR3 extracellular domain is present with 88% sequence coverage and a high-resolution (2.40 Å) X-ray diffraction structure. The MET receptor comprises a large extracellular domain, a transmembrane region, and an intracellular domain with a sequence length of 1,390 amino acids. The extracellular domain comprises 25 to 932 amino acid residues. Two PDB structures with similarity >99% and 57% sequence coverage were identified through PSI-Blast. The PDB structures 2UZX (2.80 Å) and 6GCU (6.00 Å) are further analyzed. Both cover the extracellular domain region but have a missing loop region. To model the missing loop regions for MET, a homology protein model was generated using Swiss-Model (https://swissmodel.expasy.org/), using 2UZX as the PDB template.The sequences of receptors were retrieved from UniProt (https://zhanglab.ccmb.med.umich.edu/I-TASSER/) (http://raptorx.uchicago.edu/) (https://robetta.bakerlab.org/)  was selected, with 100% percentage identity and 34% sequence coverage identified through PSI-Blast. The selected PDB structure comprises amino-terminal immunoglobulin domain (D1). This D1 domain is necessary and sufficient for HAdV binding with CAR . For theTASSER/) , RaptorXgo.edu/) , and macab.org/) . TrRosetab.org/) . Then thMolecular dynamic (MD) simulation of selected models of TrRosetta server for proteins was performed. GROMACS  was used for energy minimization and MD simulation of all proteins . The OPLhttps://www.uniprot.org/) against each UniProt ID provided in https://blast.ncbi.nlm.nih.gov/Blast.cgi) was performed to determine the percentage sequence identity basis on which PDB structures were determined. The percentage identity and sequence coverage of selected PDB structures are provided in https://www.rcsb.org/). For HAdV2 and HAdV3, high-resolution PDB structures IQIU (2.40 Å) and 1H7Z (1.60 Å) were selected. PDB structure for both serotypes comprises the head region and a small portion of the shaft region. For HAdV5, L5 4ATZ PDB was selected with a resolution of 1.95 Å and comprises the knob region. Although the sequence coverage for all the L5 proteins was less, it was considered for this study because the structure of the knob region is required to analyze its interaction pattern with pancreatic cancer receptors.The sequences of the L5 protein for all serotypes were retrieved from UniProt  to determine active site residues or protein interface residues  server for protein–protein docking of both molecules (it0) rigid body energy minimization, (it1) semi-flexible refinement in torsion angle space, and (itw) refinement in explicit solvent refinement. The HADDOCK scoring function is the linear combination of energies and buried surface area in all three stages of docking. Docking run through HADDOCK software is computationally extensive, so the server provides users with an easy-to-use interface and removes the burden of computational resources required for installation and docking run . After that, binding affinity (ΔG) and dissociation constant (dK) of the protein–protein complex were calculated using PRODIGY (PROtein binDIng enerGY prediction) (https://wenmr.science.uu.nl/prodigy/)  . Prodigyrodigy/) . Other tFor the docking run, structures of receptors and L5 protein were prepared. For SLC2A1, MET, NPR3, and IL1RAP, receptors and L5 proteins with known PDB structures were employed, while structures of GABRP, TMPRSS4, and SLC6A6 were modeled.The PDB structures of GABRP, TMPRSS4, and SLC6A6 proteins were not available. The PSI-Blast result for GABRP shows 48.34% identity, SLC6A6 47.34%, and TMPRSS4 37.99% Table 1.The protonated structures of pancreatic cancer receptors were docked with the protonated HAdV2 L5 protein using the GURU interface of the HADDOCK server. The HADDOCK generates water-refined interaction conformations of both interacting molecules and groups similar conformations in clusters. The superimposed structures of all structures in one cluster were generated S13, aloβ turn and H2 domain), 175 to 188 (3 β 1 γ 2 β turns and H10 domain), 295 to 308 (H18 domain and β turn), 355 to 366 (3 β turns corresponding to H22 domain), and 426 to 431 (H24 domain). The residues of two chains of the HAdV2 L5 protein complex interact with SLC2A1. In chain B, residue numbers include 439 to 443 (β turn), 523 to 528 (H5 domain and β turn), and 566 to 570 (β turn). In chain C, residue numbers include 412 to 415 (β hairpin), 449 to 460 (β turn and F stand), 469 to 472 (β and γ turn), 505 to 513 (γ turn and H4 domain), and 538 to 547 (β hairpin turn).Cluster 2 was selected for the SLC2A1 interaction complex with the L5 protein HAdV2 (HADDOCK score −64.6 ± 16.0 and Z-score −1.8) Table 4.β and γ turn) and 639 to 646 (β hairpin turn). Chain B residues include 267 to 273 (β and γ hairpin turn), 308 to 311 (H3 domain), 381 to 384 (β turn), 392 to 398 (β turn hairpin), and 421 to 425 (F sheet). The residues of two chains of the HAdV2 L5 protein interact with the MET receptor. Chain B residue numbers include 409 to 417 (β β hairpin turn), 441 to 447 (H2 domain), 467 to 476 (β γ β turn), and 544 to 547 (β β hairpin turn). The C chain residues include 412 to 416 (β hairpin) and 449 to 451 (β turn).For MET, cluster 18 (HADDOCK score −79.5 ± 1.1 and Z-score −2.0) was selected out of all generated clusters Table 4.β hairpin turn). In the B chain of the L5 protein interacts with the IL1RAP receptor, its residues include 407 to 410 (β hairpin turn), 414 to 419 (β hairpin turn), 441 to 451 (β β turn and H2 domain), and 474 to 477 (β turn). For NPR3, cluster 3 (HADDOCK score −55.0 ± 14.4 and Z-score −1.4) was selected (β β turn), 95 to 100 , 121 to 124 (H2 domain), and 380 to 383 (β β turn). The B and C chains of the L5 protein interact with the NPR3 receptor. B chain residues include 439 to 447 (β turn and H2 domain), 523 to 528 , and 566 to 570 (β turn). Chain C residues include 446 to 456 (H2 domain and β turn) and 507 to 512 (γ turn and H4 domain).Only one cluster with 4 conformations was generated for the interaction of IL1RAP and HAdV2 L5 in HADDOCK Table 4.β β turn, and start of H2 domain), 31 to 39 (two β turns), 70 to 73, 108 to 110 (β turn), 131 to 141 (β turn and A sheet), 204 to 211 (α γ β turn), and 241 to 244 (H7 domain). The two chains of the L5 protein interact with the GABRP receptor. Chain B residues include 402 to 410 , 415 to 420 (β hairpin turn), 439 to 448 (eta turn and H2 domain), 474 to 477 (β turn), and 484 to 486 (β hairpin turn and E sheet). Chain C residues include 463 to 470 (β γ turn), 501 to 514 (β β γ turn and H4 domain), and 538 to 552 (H6 domain and β β β hairpin turn).For GABRP, cluster 2 (HADDOCK score 85.9 ± 8.6 and Z-score −1.9) was selected Table 4.β β turn), 163 to 167 (β β turn), 180 to 183 (β turn), 201 to 205 (loop), and 216 to 222 (H13 domain). The two chains of L5 proteins B and C interact with the SLC6A6 receptor. The residues of chain B include 403 to 420 (β β β hairpin turn), 468 to 477 (β γ β turn), and 483 to 486 (β hairpin and E sheet). Chain C residues include 501 to 507 (β β β turn) and 581 and 582. For TMPRSS4, cluster 8 (HADDOCK score 139.9 ± 9.5 and Z-score −1.9) was selected (β turn), 282 to 290 (β γ β hairpin turn), 331 to 339 (β hairpin turn), 363 to 365 (γ β β hairpin turn), 383 to 387 (β β turn), and 405 to 410 (β hairpin turn). Chain B of the L5 protein interacts with TMPRSS4, and its residues are 406 to 420 (β β β hairpin turn), 439 to 450 (β turn and H2 domain), and 469 to 477 (β γ β turn); other interacting residues include 488, 490, 544, 547, and 566. The secondary structure of SLC2A1, MET, IL1RAP, NPR3, GABRP, SLC6A6, and TMPRSS4 showing the interacting domains of receptors is given in For SLC6A6, cluster 1 (HADDOCK score −115.8 ± 6.9 and Z-score −1.6) was selected Table 4,β turn), 27 to 44, 94 to 100 (β hairpin turn and H1 domain), and 102, 104, and 106. Chains B and C of the L5 protein trimer interact with the CAR receptor. The residues of chain B include 406 to 410 (β hairpin loop), 416 to 420 (ta hairpin loop), 474 to 477 (β turn), 484, and 485. The residues of chain C include 501 to 509 (β γ turn), 548 to 550 (β hairpin loop), and 581.The best possible interaction complex for CAR and DSG2 was obtained after docking, with the best HADDOCK score and lowest Z-score then selected for refinement. For CAR, cluster 3 (HADDOCK score −57.2 ± 11.9 and Z-score −1.0) was selected (β γ β turn), 230 to 232, 237 to 242 , 251 to 257 (β hairpin loop), 294 to 298 (β turn), and 271, 273, 275, and 301. Chains A and B of the L5 protein trimer interact with the DSG2 receptor. The residues of chain A include 408, 409 (β hairpin loop), 468 to 477 (β γ β turn), and 482 to 485 . The residues of chain B include 461 to 464 (ta turn), 501, 502, 506 (β turn), 541 to 551 (β β hairpin loop), and 581. The list of interacting residues is provided in For DSG2, cluster 5 (HADDOCK score −66.5 ± 11.8 and Z-score −1.1) was selected (β turn), 181 to 183 (γ turn), 299 to 300 (H18 domain), 304 to 308 (start of H19 domain), 355 to 361 , and 426 to 432 . Chains A and C of the L5 protein trimer interact with the SLC2A1 receptor. The residues of chain A include 174 to 181 , 261 to 263 (β hairpin turn), and 300 to 304 (β turn). The residues of chain C include 188 to 196 (B sheet after H1 domain), 242 to 246 (γ and β turn), 289 to 293 (B sheet), 276, 279, 147, and 149.The best possible interaction complex for all receptors was obtained after docking, with the best HADDOCK score and lowest Z-score then selected for refinement. For SLC2A1, cluster 3 (HADDOCK score 126.4 ± 2.4 and Z-score −1.7) was selected (β γ hairpin turn), 379 to 384 (β turn and F sheet), 392 to 404 (β β hairpin turn), and 419 and 421. The two chains of the L5 protein interact with the MET receptor. Chain A residues include 260 to 263 (ta hairpin turn). Chain C residues include 197 to 205 (β turn), 249 to 253 (B sheet), and 281 to 288 ; other interacting residues include 296, 298, 154, 156, and 217. For IL1RAP, cluster 4 (HADDOCK score −44.2 ± 4.5 and Z-score −1.6) was selected (β turn and k sheet) and 115 to 119 . The two chains are the L5 protein interacting with the IL1RAP receptor. Chain A residues include 258 to 264 (β hairpin turn). Chain C residues include 190 to 194 (B sheet), 241 to 43 (γ and β turn), 250, 252, 261, 277 to 281 (β hairpin turn), and 289 to 293 (B sheet).For MET, cluster 5 (HADDOCK score −70.6 ± 7.2 and Z-score −2.4) was selected Table 4.β sheet). Chain B residues are 47 to 54 , 95 to 99 (β turn), 102 to 104 (A sheet before H2 domain), and 118 to 126 . The two chains of the L5 protein interact with the NPR3 receptor. The residues of chain A include 178, 257 to 261 (β hairpin turn), and 299 to 304 (β turn). The residues of chains B include 145 to 150 (β hairpin turn), 182 to 196 (H1 domain), 241 to 247 (γ β turn), 278, 289, and 179.For NPR3, cluster 4 (HADDOCK score −81.0 ± 7.4 and Z-score −1.3) was selected Table 4.β, β, β, β turn, H2 domain, β turn), 28 to 34 (β turn), 71 to 74 , 132 to 141 , 203 to 211 , and 241 to 244 (H7 domain). The two chains of the L5 protein interact with the GABRP receptor. Chain A residues include 174 to 178 (A sheet before H1 domain), 257 to 261 (β hairpin turn), and 299 to 304 (β turn). The C chain residues include 144 to 149 (β hairpin turn), 178 to 194 (H1 domain and B sheet), 240 to 247 (γ β turn), 275 to 280 (β hairpin turn), 300 to 302, and 250.For GABRP, cluster 2 (HADDOCK score 71.4 ± 8.2 and Z-score −2.5) was selected Table 4.−1) and the highest interactions (147) with HAdV3 than other serotypes (β turn), 163 to 170 , 178 to 190 , 197 to 203 (H12 domain), and 215 to 222 (H13 domain); other residues in fluids include 449, 147, and 151. The L5 protein residues that interact with SLC6A6 are chain A 258 to 265 (β hairpin turn). Chain C residues include 145 to 151 (β hairpin turn), 182 to 196 (H1 domain and B sheet), 268 to 279 , and 287 to 293 (B sheet). For TMPRSS4, cluster 3 (HADDOCK score 153.1 ± 2.7 and Z-score −1.2) was selected , 245 to 257 , 276 to 285 , 331 to 339 (β hairpin turn), 384 to 387 , and 405 to 408 (β turn). The two chains of the L5 protein interact with TMPRSS4. Chain A residues include 258 to 263 (β hairpin turn). Chain C residues include 146 to 150 (β hairpin turn), 178 to 183 , 185 to 196 (B sheet after H1 domain), 241 to 247 , 275 to 281 , and 285 to 293 (B sheet). The list of interacting residues for all interaction complexes is provided in For SLC6A6, cluster 2 (HADDOCK score 4.8 ± 10.8 and Z-score −1.8) was selected (erotypes Table 4.β turn), 23 to 50 , 92 to 109 , and 131. Chains A and C of the L5 protein trimer interact with the CAR receptor. The residues of chain A include 258 to 265 (β hairpin loop) and 296 to 301 (β turn). The residues of chain C include 146 to 149 (β hairpin loop), 178 to 196 , 242, 244, 245, 250 (γ and β turn), and 291 to 294 .The best possible interaction complex for CAR and DSG2 receptors was obtained after docking, with the best HADDOCK score and lowest Z-score then selected for refinement. For CAR, cluster 1 (HADDOCK score −78.2 ± 4.0 and Z-score −1.3) was selected (β γ β turn), 223 to 232 (β β β turn), 247 to 250 (β hairpin loop), 272 to 275 (β turn), 294 to 297 , 301, and 306. Chains A and C of the L5 protein trimer interact with the DSG2 receptor. The residues of chain A include 146, 149, 178, 179, and 299 to 302 (β turn). The residues of chain C include 145 to 153 (β hairpin loop), 177 to 179, 190 to 196 , 241 to 245 (γ β turn), 272 to 278 (γ hairpin loop), and 285 to 289 (B sheet).For DSG2, cluster 1 (HADDOCK score −82.4 ± 9.2 and Z-score −1.1) was selected (β turn), 92 to 109 , 112, 115 to 118 (γ hairpin loop), 126 to 131 (β turn), 148, 149, 156 to 159 (D sheet), and 182 to 184 (β hairpin loop). Chains A and C of the L5 protein trimer interact with the CD46 receptor. The residues of chain A include 178, 182, 185, 258 to 262 (β hairpin loop), and 298 to 303. The residues of chain C include 145 to 149 (β hairpin loop), 178, 179, 182, 185 to 196 , 242 to 247 , and 287 to 293 (B sheet). The list of interacting residues for all interaction complexes is provided in For CD46, cluster 1 (HADDOCK score −83.8 ± 13.2 and Z-score −1.7) was selected (β turn), 356 to 362 , and 426 to 432 (part of H24 domain). The two chains of the L5 protein interact with SLC2A1. Chain A residues include 396 to 399 , 404 to 410 (β hairpin turn), 476-477 (H2 domain), and 481 to 492 . Chain C residues include 501 to 513 .The best possible interaction complex generated by docking (HADDOCK) was then selected for refinement and interaction analysis. For SLC2A1, cluster 4 (HADDOCK score −66.3 ± 3.8 and Z-score −1.4) was selected (−1) (β turn), 582 to 586 (J sheet), 615 to 623 (H10 domain and β hairpin turn), and 700 to 706 (M sheet and β hairpin turn). Other residents include 40, 459 to 461, 521, 528, and 553. Chains A and B of the L5 protein interact with MET. Chain A residues include 396 to 399 , 476 to 477 (H2 domain), and 481 to 496 . Other important residues include 580 and 581. For IL1RAP, cluster 5 (HADDOCK score −44.2 ± 4.5 and Z-score −1.6) was selected  and 485 to 492 (A sheet and loop).For MET, cluster 36 (HADDOCK score −78.8 ± 3.7 and Z-score −1.7) was selected (−1) Table 4.β turn), 474 to 477 (H2 domain), 482 to 492 (β hairpin turn and A sheet), and 404. For GABRP, cluster 12 (HADDOCK score 63.8 ± 5.4 and Z-score −2.3) was selected , 29 to 38 (β β turn), 71 to 74 , 131 to 141 , 204 to 209 (β γ β turn), and 243 to 248 (H7 domain). For GABRP, the two chains of the L5 protein interact with the receptor. Chain A residues include 404 to 409 (β hairpin turn), 474 to 479 (H2 domain), 481 to 490 (β hairpin turn and A sheet), and 466. Chain B residues include 501 to 508 (α β β turn), 543 to 550 (γ β γ turn and β hairpin turn), and 581.For NPR3, cluster 9 (HADDOCK score −52.7 ± 11.0 and Z-score −2.1) was selected Table 4.β, β turn), 179 to 185 (β turn), and 201 to 220 ; other important residues include 147, 151, 154, and 158. Chain A residues of the L5 protein interact with SLC6A6 including 396 to 399 , 404 to 409 , and 474 to 490 . Chain B residues include 492 to 497 (A sheet), 580, and 581. For TMPRSS4, cluster 1 (HADDOCK score 88.5 ± 0.9 and Z-score of −1.0) was selected (β turn), 282 to 287 , 331 to 339 (β hairpin turn), 382 to 387 , and 405 to 408 (β turn). The residues of chain A of the L5 protein interact with TMPRSS4 including 396 to 398 , 463 to 469 (β turn) 474 to 497 , and 581. The list of interacting residues for all interaction complexes is provided in For SLC6A6, cluster 1 (HADDOCK score −76.9 ± 2.7 and Z-score of −1.5) was selected Table 4.etaβ β  turn and C sheet), 106 to 111 (C sheet and β turn), and 113. Chain A of the L5 protein trimer interacts with the CAR receptor. The residues of chain A include 412, 416, 417 (β hairpin loop), 466 to 479 (β turn and H2 domain), and 487 to 492 (A sheet).The best possible interaction complex for all receptors was obtained after docking, with the best HADDOCK score and lowest Z-score then selected for refinement. For CAR, cluster 2 (HADDOCK score −66.9 ± 2.3 and Z-score −1.6) was selected , 207 to 209 (γ β turn), 212 to 219 , 221 to 225 (E sheet), 245, and 247. Chain A of the HAdV5 L5 protein interacts with the DSG2 receptor. The residues of chain A include 417, 461 to 479 (β β turn and H2 domain), 482 to 492 (β hairpin loop and A sheet), and 544 to 549 (γ β hairpin loop).For DSG2, cluster 3 (HADDOCK score 126.4 ± 2.4 and Z-score −1.7) was selected (β turn), 33, 34, 75 to 78 (β hairpin loop), 82 to 87 (β hairpin loop), and 95 to 106 (β γ turn). Chains A and B of the HAdV5 L5 protein interact with the receptor. The residues of chain A include 404 to 408 (β hairpin loop), 462 to 477 (β β β turn and H2 domain), 482 to 492 (β hairpin loop and A sheet), 544, and 546. Chain B residues include 501 to 508 (β β β turn), 548, and 549. The interaction profile of CR1 domains 15, 16, and 17 with the HAdV5 L5 protein was also determined. Cluster 1 (HADDOCK score −75.9 ± 4.4 and Z-score −2.5) was selected (β hairpin loop), 1,087 to 1,094 (β hairpin loop and Y sheet), and 1,110 to 1,119 (Y sheet and β hairpin loop). Chain A of the HAdV5 L5 protein interacts with the receptor. The residues of chain A include 404, 417, 462 to 471 (β β β turn), 474 to 479 (H2 domain), 485 to 493 (A sheet), and 544 to 547 (γ hairpin loop). The secondary structures of CAR, DSG2, and CR1 showing the interacting domains of receptors are given in For CR1 domain 1 and 2 interaction with HAdV5, cluster 3 (HADDOCK score −86.1 ± 8.3 and Z-score −1.8) was selected . The replacement mutation or deletion of any residue out of the above-given list results in ablating the HAdV5 L5 protein interaction with the CAR receptor  , Tyr477 receptor . Anotherreceptor . We alsoβ turn), and Glu426 to Pro431 . The MET has the lowest binding affinity with the L5 protein, out of all the receptors. Only HAdV5 makes the most number of interactions with a total of 93 with a binding affinity of −10.8. MET has no common residues that interact with all L5 proteins. The IL1RAP shows a high binding affinity of −11.5 kcal mol−1 with a total of 75 interactions with HAdV2. The list of IL1RAP residues, which show interaction with all three L5 proteins, is Arg77 to Ile83 , Phe85, Arg86, Arg117, Asn118, and Thr119 (RNT). NPR3 shows the highest binding affinity with HAdV3 L5 with a total of 92 interactions. The NPR3 residues, which show interaction with all three L5 proteins, are Ala47, Leu48, Pro49, Pro50 , Pro95, Gly96, and Thr97 .The SLC2A1 receptor shows a high binding affinity with all three L5 proteins. The list of SLC2A1 residues that show interaction with all three L5 proteins is Asp177, Asn182, Lys183, Glu299, Lys300, Gln304, Leu358 to Leu361 , Gly31 to Asp34 , Ser37, Ser71 to Ser73 , Glu132 to Asn137 , Leu139, Arg141, His204 to Gln209 , Leu243, and Tyr244. The list of SLC6A6 residues that show interaction with all three L5 proteins is Tyr147, Gln151, Gln154, Asn163, His164, Ser165, Trp166, Asn167 , Lys180, Ser181, Val182, Ser183 , Arg201, Asn202, Val203 , and Leu216, Lys217, Trp218, and Asp219 . The list of TMPRSS4 residues that show interaction with all three L5 proteins is Gln227, His228, His245, Arg248, Lys249, His250 , Asn282 to Lys287 , Phe331 to Met339 , Gln384, Gly385, Ser387, and Ser405 to Tyr408 .The GABRP shows the highest number of interactions with all the L5 proteins with a binding affinity of more than −15 kcal molvia HAdV5. They have found that (Ad.5/3-CTV-M7) has an antitumor effect on primary and distant site tumor growth (in metastasis). This study showed that Ad.5/3-CTV-M7 could be utilized as a therapy against pancreatic cancer. Our study also exhibited the same outcome, as the HAdV3 L5 protein interacts better than the HAdV2 and HAdV5 L5 proteins with pancreatic cancer cell receptors.In this study, we attempt to identify better interacting adenovirus L5 protein with pancreatic cancer receptors, so that serotype can be used more effectively as an oncolytic virus. The HAdV3 L5 shows better interaction with 4 receptors NPR3, GABRP, SLC6A6, and TMPRSS4 with the highest number of interactions and better binding affinity. HAdV2 shows a better interaction profile with SLC2A1 and IL1RAP with high binding affinity, while HAdV5 interacts well with MET. The affinity maturation can be performed by mutating the fiber knob of the HAdV3 to enhance its interaction with the SLC2A1, MET, and IL1RAP specifically and also escalate the binding affinities with other receptors. David Curiel’s lab research was able to increase the efficacy of HAdV5 against prostate cancer by recombination of HAdV5 with the HAdV3 L5 protein , 66. In Oncolytic virus therapy is a promising immunotherapy approach that can be used for the treatment of pancreatic cancer. The virus’s entry entirely depends on the attachment of the virus with the cancer cell surface receptors. In the current study, the attachment or interaction profile of 3 adenovirus serotypes with the 7 pancreatic cancer cell receptors is analyzed. The L5 proteins of 3 serotypes of adenovirus HAdV2, HAdV3, and HAdV5 are docked with the seven highly expressed pancreatic cancer receptors SLC2A1, MET, IL1RAP, NPR3, GABRP, SLC6A6, and TMPRSS4. The binding affinity of the L5 proteins with human receptors is analyzed. The HAdV3 L5 protein displays a better interaction profile and high binding affinities with 4 receptors NPR3, GABRP, SLC6A6, and TMPRSS4, while HAdV2 shows better results with SLC2A1 and IL1RAP. It is proposed that affinity maturation of HAdV3 can be performed by incorporating the important interacting residues of HAdV2 and HAdV5, to enhance its binding affinity with the remaining 3 receptors SLC2A1, MET, and IL1RAP. In the future, we will perform a wet lab analysis for these interactions to further validate our results.The original contributions presented in the study are included in the article/Ethical approval/written informed consent was not required for the study of animals/human participants in accordance with the local legislation and institutional requirements.MN and RP conceived and designed the study. MN conducted the analysis and wrote the manuscript. All authors took part in the analytical discussions and critical review of the manuscript.The National University of Sciences and Technology (NUST) will pay the publication fee for this article.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Sustainable water processing techniques have been extensively investigated and are capable of improving water quality. Among the techniques, photocatalytic technology has shown great potential in recent years as a low cost, environmentally friendly and sustainable technology. However, the major challenge in the industrial development of photocatalyst technology is to develop an ideal photocatalyst which must have high photocatalytic activity, a large specific surface area, harvest sunlight and shows recyclability. Keeping these views, the present review highlighted the synthesis approaches of graphene/metal oxide nanocomposite, characterization techniques and their prominent applications in photocatalysis. Various parameters such as photocatalyst loading, structure of photocatalyst, temperature, pH, effect of oxidizing species and wavelength of light were addressed which could affect the rate of degradation. Moreover, the formation of intermediates during photo-oxidation of organic pollutants using these photocatalysts is also discussed. The analysis concluded with a synopsis of the importance of graphene-based materials in pollutant removal. Finally, a brief overview of the problems and future approaches in the field is also presented. Drinkabzirconia .2 are the most prominent material for their use as photocatalysts to treat the wastewater. ZnO has shown considerable potential in recent years due to its unique features such as its reasonable cost, strong oxidation capacity, extraordinary photosensitivity, bio consistency, acceptable photocatalytic performance, high chemical stability  excellent stability, 2) low price and low toxicity and 3) improved photocatalytic activity compared to other semiconductor materials , a chemist at Oxford University, and L. Staudenmaier in 1898 . Hummerset of GO .Moreover, based on the surface functionalization in the development of graphene semi-continuous composites with tunable textural and surface chemical features, carboxylic, hydroxyl and epoxy groups combine with valuable functionality compounds such as polymeric materials and metal oxides and accountable to use in different applications . Further2 received considerable interest in the area of the photocatalysis. The lowering of recombination process and improved the resistance to photo-corrosion for the ZnO and TiO2 photo catalyst will be of great concern. The integration of ZnO and TiO2 nanoparticles into GO surface resulting in high stability and preventing graphene-based layers from being joined due to strong van der Waals pressures that exist among graphene-based layers. In material science, synthesis of new, more efficient nanocomposites based on graphene oxide/metal oxide is becoming more and more important and some regularly used techniques are listed as follows.Due to their nano-size and good optoelectronic characteristics, the nanocomposites GO/ZnO and GO/TiOSol-gel is one of the most promising approaches to produce composite, which was originally used in late 1980s, for the synthesis of nanocomposites. The sol-gel technique is a multi-step procedure based on the metal precursors sequentially hydrolyzed for a metal hydroxide resolution followed by an instantaneous condensation to create a 3D gel, as illustrated in The gel produced is dried, which leads to aerogel or xerogel and the desired product being formed afterwards. Aquatic or non-aquatic solvents are utilized, and results are more porous than other nanocomposites and have a larger area. Graphene materials are suitable precursors to the sol-gel approach because of their strong dispersive capacity to react with covalent aqueous or non-watery chemicals and functions. During the last several decades, ZnO-rGO nanocomposites have been developed using sol-gel methods by including nanoparticles of ZnO on the surface of rGO sheets. One study reported by Azarang et al. on the preparation of ZnO-rGO using sol-gel method to investigate the effect of rGO concentration on the optical properties of the nanocomposites. Solvothermal and hydrothermal methods played an important role in various scientific fields either for commercial research purposes or basic research. These approaches are favorable as the process is simple, efficient and low-cost. The methods are also environmental friendly as there are no hazardous organic solvents being used. Usually, water or ethanol was used as the solvent and the nanoparticles are formed under a high temperature and pressure for a given time. Using this one-pot method, the metal oxide can be attached and/or wrapped to the rGO sheets simultaneously with the reduction of graphene oxide . Last fe2 solution (2 ml) obtained from Zn (CH3COO)2 solution and NaOH (1 g) was subjected to liquefaction in 25% NH3.H2O (25 ml) respectively. The mixture was added slowly in presence of NaOH solution (0.1 M) for increasing the pH until ∼10 while the solution was kept stirred in an oil bath until dark brownish liquid produced at temperature 60 ± 2°C. Later, the mixture was being exposed into treatment involving hydrothermal methods for a day (24 h) at 180°C temperature. The generation of the end product was ended by washing it with ethanol, deionized water and lastly, dried up into an oven at 60°C temperature  for 10 h at distinct temperatures which are 98°C and 80°C. The final product was then cooled until achieved 25°C temperature. The cooled sample was washed by using distilled water and ethanol. Ahmad et al. reported that the ZnO/graphene-Ag nanocomposites fabrication used simple solvothermal technique for organic dyes degradation  was produced by Hummers’ method and Zn (OH)perature . Saravanperature . The proation M. . In prev2/GO nanocomposite characteristically synthesized in optimized ratio. First, Zn2+ ions had been deposited evenly on the surface of GO and then turned it into ZnO NPs with the addition of NaOH and NaBH4 at 150°C. ZnO/graphene then produced at the end of reaction after graphene oxide reduced into graphene. Generally, ZnO NPs have grown into a range, 10–20 nm size with distribution of exquisite particle physical size on the surface of graphene layers  with stainless steel-based autoclaves for the same amount of time reaction as in ZnO synthesis. Last step, the end product obtained was washed with water, then dried out in vacuum at temperature 80°C. RGO compounds had excellent electron transportation and improved well surface area that contributed good effects to improve the nanocomposite’s photocatalytic reaction. Sahatiya and Badhulika also reported about an easy as one-step method for single graphene in-situ preparation which doped with zinc oxide (GO-ZnO) nanocomposite through using electrospinning  and aggregated porphyrin nanorods (50–60 nm width) and several hundreds of nanometers in length on the graphene surface  and grinding balls made up of stainless steel (5 mm). They fixed the ration of grinding balls to samples at 20:1 at which the mass of the balls is 500 g and the materials are 25 g. Ethanol was used as dispersant in the process. The materials were milled under the rotate speed of 500 rad/min for 12 h, and then the final product was separated from the grinding balls and dried under 60°C for 6 h. The hybrid composites showed an excellent absorption performance due to the external planar anisotropy with flake shape after ball milled . AdditioComposites preparation of GO/metal oxide is often characterized by several parameters like surface properties, optical activity, size, crystallinity, heat stability, shape, etc. These characteristics are the important informations that is required to develop composites with excellent properties. Herein, the characterization techniques are divided into following types; spectroscopic, microscopic and elemental analysis including crystalline phases analysis using XRD technique. Other characterization on thermal and physical properties of the composites will also be included in the discussion.Fourier transform spectroscopy (FTIR) is widely used techniques to identify organic compounds and in some cases, inorganic compounds. It is a rapid and non-destructive method that could detect a range of functional groups and is sensitive to changes in molecular structure. FTIR measures the absorption of infrared radiation by the sample material against wavelength. From there, the infrared absorption bands identify molecular components and structures. This method is well fitted with regard to surface chemicals, surface chemical residues, and identification on the surface of NPs for organic functional groups . In the case of GO/metal oxide composites, FTIR is best fitted with Raman spectroscopy to get the full view of the composites properties.8 ms−1); h = Planck’s constant i.e. 6.626 × 10−34 J s; 1 eV = 1.6 × 10−19 J and λ = wavelength . UV-vis measure the amount of UV light absorbs by a substance. It can be used to determine analyte concentrations or the chemical conversion of a component. The range of reflection and absorption is measured so that the nanocomposites visible and ultraviolet optical activity is defined . The chavelength . UV-vis velength .2 composite. The electronic properties were analyzed based on the diffuse reflectance spectra that are used to calculate the band gap energy. The calculation is done according to Kubelka-Munk transformed function as follow:n = 2 for permitted indirect transitions, n = 3 for forbidden indirect transitions, n = 1/2 for permitted direct transitions, and n = 3/2 for forbidden direct transitions. In another study by Ruidíaz-Martinez et al., they used UV-vis technique, to study the electronic structure of the rGO-TiOThere are several types of microscopic analysis that frequently used for composites study including scanning electron microscopy (SEM), transmission electron microscopy (TEM). SEM is a technique that may be used to improve the sample’s resolution. The surface morphology, such as determining the exact shape of nanoparticles and topographic analysis, is frequently studied using SEM and TEM methods. The main distinction is that TEM delivers more information about the interior structure and has higher resolution than SEM. The average size of material is also determined using such procedures or approaches. Meanwhile, the AFM method is often employed to collect information on the surface nature, size, shape, topography, roughness, and particle size distribution of nanocomposites . The SEMK = Scherrer constant, Β = full width at half-maximum (FWHM), λ = wavelength of the X-ray, D = particle size and θ = Bragg angle while in Bragg’s Law d is the d-spacing. Besides that, EDX method may be used to determine the types of elements present in a sample as well as their concentration. It is also commonly used to estimate the elemental makeup of composites. Moving forward, XPS is a surface-sensitive quantifiable spectroscopic technique which relies on the photoelectric consequence that can detect the elements present in a material  or on its surface, as well as their chemical states, and the overall electronic structure and density of the electronic states in the material. 2, GO and TiO2-GO composites. The TiO2 and TiO2-GO show two peaks at the binding energies of 458.5 and 464.3 eV for Ti 2p, which was assigned to the Ti 2p1/2 and Ti 2p3/2. The spectrum of TiO2 demonstrated two peaks of O 1s at 529.2 and 532.3 eV. The peaks of O 1s at about 530.3 and 531.7 eV were from the signal of O-H, C=O and C-O of GO. The TiO2 revealed four peaks at the binding energy of 529.2, 530.8, 532, and 533 eV, respectively, corresponding to the oxygen atoms of TiO2 and GO , X-ray photoelectron spectroscopy (XPS) and X-ray diffraction (XRD). XRD is used to examine nanocomposites’ crystalline phase and is distinguished by diffraction patterns for a crystal structure. X-rays may penetrate nanoparticles in order to generate the particular pattern of diffraction . In addi2 and GO .Thermal analysis of composites can be conducted through various instrumentation methods. Among all, thermogravimetric–differential thermal analysis (TG/DTA) is the most reported approaches used to illustrate the material’s thermal stability and thermal potency. Other than that, particle size analysis (PSA) is a method for determining the average size of nanocomposites. Meanwhile, Brunauer–Emmett–Teller (BET) analysis is also can be employed to study the specific surface area and porosity of composites.2, WO3, Fe2O3, SnO2, CuO, etc. Graphene derivatives inhibit corrosion, and the liquidation into the water of metal oxide nanoparticles, other than restricting the recombination of the electron hole, thereby extending the photocatalytic lifetime , which has more area than GO/ZnO nanocomposites, for photocatalysts with highly approachable surfaces . The GO/ZnO nanocomposites preparation through solvothermal method was reported by Atchudan et al.   . A numbe2–graphene composites and adjusted threshold weights for graphene (0.05 wt%)  . The phoP25 TiO2 . Liang eubstrate . The GO/activity . The TiOAu, etc. . These m2 and ZnO, other metal oxide like WO3, Fe2O3, CuO and SnO2 are considered as nontoxic photocatalyst for pollutant removal due to their low-cost, fast response and recovery time. WO3, is an n-type semiconductor, activated under visible light irradiation having small bandgap (2.4–2.8 eV) that makes it a suitable catalyst for degrading organic compounds  degradation via photocatalysis  and hydrogen peroxide (H2O2) into suspension of ZnO/TiO2 is common procedure used to increase the photo-oxidation rate   .The pH level is another key factor determining the degradation of organic compounds during photocatalytic reaction. In essence, pH has a substantial influence on the surface charge of catalyst, contaminant hydrolysis, oxidant and contaminant ionization degree . The sur2/GO composites as photocatalysts.The photocatalysis degradation development may lead to functions at optimized temperature as well as distinctive pressure because the photonic activation carries direct effect on it. This is valuable for water sanitization behaviour and the thermal phase, can be omitted to preserve power . Overall2 for 1,8-diazabicyclo (5.4.0) undec-7-ene photodegradation  or graphene nanocomposites material in the presence of light. In this case, it is required to establish a relationship between parent compound and intermediates, weather these products are more harmful or less harmful than parent compounds utilizing the toxicity test in respect of the degradation products acquired . The GC-MS analysis of an unirradiated and irradiated Ketoprofen solution showed that a single peak  was obtained in first case and several peaks were appeared at retention times (Rt) 4.07, 5.28, 7.27, 10.75, 13.20 and 17.25 in later case as shown in (3) and 2-phenylpropanoic acid (4) and a possible route for the formation of these products can be seen in Few examples which are previously discussed in literature such as Umar et al. studied the ketoprofen and chlorothalonil photodegradation pathway and found intermediate products by using the Mn-doped TiOxidation . For exaRt = 14.15 and 17.01 based on the pattern of mass fragmentation and molecular ions, were characterized as 4 and 3, which indicate a replacement of a cyanide group with a hydroxylated benzene ring group.Similarly, in the case of chlorothalonil, photodegradation and intermediate products are shown in +) peak at m/Z = 284. The peaks of m/Z of 304–306, can be attributable to the center of MB, generated by a central aromatic heterocyclic aperture by the electromagnetic interaction between •OH and sulfhydryl (C -S+ = C), leading to sulfoxide moiety production (C—S(=O)—C)  dye solution . The ESIS(=O)—C) . As a coS(=O)—C) . Low-intS(=O)—C) . MB soluic salts . Based oFurthermore, Rajamanickam et al. studied the degradation of 4-nitrophenol by using the ZnO as photocatalyst . The for2 composite as a photocatalyst. LC–MS was used to examine TBBPA intermediates in the photocatalytic degradation process. The photocatalytic degradation route of TBBPA was postulated based on the identified intermediates. The TBBPA decomposition was nearly 100% within 60 min with the formation of several intermediates, which were discovered by mass spectrometry. The mass spectra peak detected five major production ions at 465 m/z, 401 m/z. 387 m/z, 307 m/z and 227 m/z, which were labeled Products A–E as presented in Similarly, Cao et al., studied the routes for the formation of intermediate products (tetrabromobisphenol (TBBPA)-A) using the graphene–TiOBy/ comparing the mass spectra with identified products from the degradation of TBBPA reported in previous published reports . The pro2 composites was postulated based on the preceding discussion of identified intermediates and prior studies. It was also noticed that TBBPA was photodegraded by three different pathways: debromination, substitution, and dihydroxylation. However, according to an extensive literature survey there were not many studies on discussion of graphene derivatives with ZnO or TiO2 nanocomposite photodegradation by using GC–MS or EIS-MS. Few studies of ZnO or TiO2 were characterized by GC–MS or EIS-MS.Furthermore, the peak area of the above-mentioned intermediates increased, implying the emergence of additional substrates, namely intermediates and products. The peak areas of product A and product D both increased initially and then declined, indicating that their appearance was dominating in the first stage. Then both Product A and Product D were rapidly degraded. After 30 and 10 min, respectively, the peak areas of Product B1/B2 and Product E rose rapidly and reached a maximum. Then, when the irradiation duration was extended, they altered irregularly. Product E was not the end product, as can be observed, and it may have been converted into some tiny organic compounds that were not discovered in our investigation. On the other hand, over the entire 60 min, the peak area of Product C1/C2 rose steadily and constantly. It was discovered that Product C1/C2 was most likely stable and/or that their creation was faster than their degradation during the procedure. Further, the potential mechanism for the photodegradation of TBBPA by graphene–TiO2-based photocatalysts, photocorrosion is more critical than chemical corrosion. To date, numerous researches have concentrated on the improvement of ZnO or TiO2-based photocatalyst photocorrosion resistance and this should be extended to other system too. Further, bioinspiration can be a suitable technique to build new, effective, light-trapped, pollutant-absorbing designs. The synthesis of GO from waste material is an ideal approach to overcome the synthesis problem.To date, a wide variety of nanocomposites from graphene and its derivative with metal oxides are being developed for use in photocatalysis field. The research in this area is still ongoing and continually expanding as they have to overcome the technological and economic barriers in order to be practically viable. There are several concerns about the existing systems that must be addressed. Firstly, there are presently no ways of producing graphene derivatives in huge amounts which are scalable and affordable despite their remarkable applicability. Secondly, researchers have demonstrated that the photocatalytic performance of these graphene-based nanocomposites could be increase by enhancing the absorption of light, load separation and conveyance, and a longer operational life of the nanocomposite systems. Based on this point of view, some constrain need to be focused on to overcome the major scientific and technical problems preventing their utilization in applications at large scale and make it unsuitable at economic importance. For example the synthesis and design of the new graphene-based nanocomposites for photocatalyst still require integral thinking; hence the success of photocatalysis will demand future interdisciplinary effort between researchers and engineers. This approach might be useful in addressing the post-revival, recyclability, and reusability of nanocomposites. Next is the stability in the development of photocatalysts is another significant problem due to their limited useful lives and photocorrosion. For example, ZnO or TiO2 nanocomposites were studied in the expansion of new and futuristic photocatalysis materials. The present review highlighted the literature survey about graphene/ZnO and graphene/TiO2 nanocomposites from 2010 to 2020. The coupling between graphene and photoactive metal oxide semiconductors increases the photocatalytic function of metal oxide for the destruction of different aquatic contaminants such as organic dyes, heavy metal ions and pathogens. Graphene’s unique features in conjunction with nanomaterials’ size-dependent features provide other capabilities such as high adsorption capacity, wider light absorption range and better charging separation capabilities and great stability. The development of graphene/metal oxide nanocomposite-based water treatment technology is a very intriguing but tough challenge for researchers since there still are few issues, such as what the future of these composites will be like, and how environmentally friendly do they become? This also represents an important problem for the recycling and disposal of these expired composites. Strategies for the recycling and disposal in home water purity devices for waste creation will be developed concurrently. Finally, the conclusion is, the graphene/metal oxide nanocomposite has enormous possibilities as an effective photocatalyst, the problems with these materials need to be solved, for example with the nature of the chemical interaction or the bonding of metal oxides and the graphene. The need to study them carefully in order to make a more efficient photocatalyst is not fully known. In order to maximize output from these composites, thorough research on the optimization of the graphene content in the composite is needed.In summary, graphene/ZnO and graphene/TiO


An increasing number of studies have indicated a close link between DNA methylation and tumor metabolism. However, the overall influence of DNA methylation on tumor metabolic characteristics in prostate cancer (PCa) remains unclear.  We first explored the subtypes of DNA methylation modification regulators and tumor metabolic features of 1,205 PCa samples using clustering analysis and gene set variation analysis based on the mRNA levels of DNA methylation modification regulators. A DNA methylation-related score (DMS) was calculated using principal component analysis and the DNA methylation modification-related gene signatures to quantify DNA methylation characteristics. We then performed a meta-analysis to identify the hazard ratio of DMS in the six cohorts. In addition, a nomogram was drawn using univariate and multivariate Cox analyses based on the DMS and clinical variables. Finally, a drug sensitivity analysis of the DMS was performed based on the genomics of drug sensitivity in cancer datasets.  Three PCa clusters showing different DNA methylation modification patterns and tumor metabolic features were identified. A DMS system was established to quantify the characteristics of DNA methylation modification. PCa samples showed a differential metabolic landscape between the high and low DMS groups. The prognostic value of the DMS and nomogram was independently validated in multiple cohorts. A high DMS was associated with increases in the tumor mutation burden, copy number variation, and microsatellite instability; high tumor heterogeneity; and poor prognosis. Finally, DMS was closely related to different types of antitumor treatment.  Improving the understanding of tumor metabolism by characterizing DNA methylation modification patterns and using the DMS may help clinicians predict prognosis and aid in more personalized antitumor therapy strategies for PCa. CDKN24) is a suppressor gene that encodes the protein p16 and affects the cell cycle and hypermethylation in PCa ..β [TGF-βP value <0.001 were selected using the R package limma as DMM-related genes for further analysis [Differentially expressed genes (DEGs) between three DMM patterns with adjusted analysis .We performed further statistical analysis of the prognostic DMM-related genes by performing univariate Cox regression analysis on 2259 DEGs, and 337 DMM-related genes associated with DFS were selected for further analysis. To quantify the characteristics of DNA methylation modification, we performed principal component analysis (PCA) of the meta cohort to generate a DMS system. PC1 and PC2 were extracted to calculate the DMS as shown in the equation below. The advantage of this approach is that it concentrates the score on the largest set of highly correlated (or unrelated) gene blocks in the set while downweighting the contribution of genes that are not tracked by other set members , 20.i−PC2i), where i is the expression of 337 prognostic DMM-related genes. The correlation between clinical variates, DMM regulators, DMS, and DFS of patients with PCa was analyzed by performing univariate analysis. The prognostic model and nomogram were constructed using multivariate Cox regression analysis. Kaplan–Meier (K-M) survival curves were used for prognostic analysis, and log-rank tests were performed to calculate P values. To test the precision of the risk model and nomogram, time-dependent receiver operating characteristic (ROC) analysis was performed using the R package survival ROC 1.0.3. An area under the ROC curve >0.60 indicates that the prediction ability of the model was meaningful, whereas a value of >0.75 indicates an outstanding predictive value of the model. The C-index was calculated using the R package pec. Decision curve analysis was performed using the R package ggDCA.DMS = Σ and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed to identify enriched GO and KEGG pathways of selected genes using the R package clusterProfiler with a cutoff of e < 0.05 .t-test was used to compare two groups with normally distributed variables whereas the Mann–Whitney U test was used to compare two groups with nonnormally distributed variables. To compare the three groups, one-way analysis and Kruskal–Wallis tests of variance were used as parametric and nonparametric methods, respectively. Contingency table variables were analyzed using the chi-square test or Fisher's exact test. A combined analysis of the hazard ratio of DMS between the six cohorts was performed by meta-analysis (fixed-effect model). Fisher's exact test was used to calculate the difference for contingency table variables, and the correlation coefficient of two variables was obtained using Spearman's correlation analysis. Statistical significance was defined as a two-tailed P value < 0.05. All statistical analyses were performed using R software .Unpaired Student's P < 0.05), as shown in P < 0.05; P < 0.05; P < 0.05; We next examined the relationship between DMM regulators and DFS through prognostic analysis of PCa. First, we collected data from 1,205 patients with DFS and other clinical information from six independent cohorts . We thenn = 156), DMM pattern B (n = 196), and DMM pattern C (n = 329). We first compared the expression levels of the regulators between the three DMM pattern groups and found significant differences in TET1, TET2, TET3, DNMT3A, DNMT3B, ZBTB33, ZBTB38, ZBTB4, UHRF1, UHRF2, MBD1, MBD3, MBD4, UNG, MECP2, TDG, SMUG1, and NTHL1 expression levels . We also detected three DMM-related gene clusters based on the expression characteristics of these 337 genes using unsupervised cluster analysis .To explore the relationship between DMS and DMM regulators, we calculated the coefficient between the DMS and DMM regulators and compared the expression levels of these regulators between the high and low DMS groups Figures . The resP < 0.05; Spearman correlation: P < 0.05; Figures We also obtained TCGA PRAD 450 K data. First, we calculated the DMS of TCGA PRAD cohort samples and divided these samples into high and low DMS groups based on the median DMS (DMS = 72.10). Unsupervised clustering (single-center method) was performed based on the 500 genes showing the most variable DNA methylation levels in turn based on DNA methylation characteristics. Remarkably, the heat map indicated that the characteristics of DNA methylation were closely associated with the DMS . We thenP < 0.05; Figures To verify the biological functions associated with DMS, we selected the significant differential ssGSEA scores of KEGG pathways and observed significant differences in the ssGSEA scores of metabolic pathways, including propanoate, arachidonic acid, fatty acid, sulfur, linoleic acid, riboflavin, alpha-linoleic acid, and 30/41 metabolic pathways , GSE70768 (log-rank test: P=0.021), GSE116918 (log-rank test: P=0.013), DKFZ (log-rank test: P=0.159), and MSKCC (log-rank test: P=0.068) cohorts  in six datasets  has been reported to reflect tumor heterogeneity . BecauseP < 0.001; Figures Copy number variations (CNVs) occurring upstream of genes regulate gene expression and influence tumor occurrence and development . To explP < 0.001; Microsatellite instability (MSI) is also a crucial indicator of genome instability associated with tumor heterogeneity . We obtaβ using the corresponding gene sets , may promote the progression of PCa, by altering metabolic pathways. Additionally, pattern C and gene cluster C were associated with the longest DFS and best prognosis, and their activated metabolic pathways fatty_acid, ascorbate_and_aldarate, cysteine_and_methionine, histidine, tryptophan, beta_alanine, selenoamino_acid, glutathione, starch_alanine, pyruvate, propanoate, butanoate, and cytochrome_p450(drug) may suppress PCa progression.Considering the heterogeneity of DNA methylation modifications individually, it is necessary to quantify the DNA methylation modification spectrum of each sample. Therefore, we constructed a DMS system and validated it in multiple PCa cohorts. To confirm that the DMS is associated with DNA methylation characteristics, we conducted unsupervised clustering using DNA methylation levels of the top 500 genes with the greatest variation in PCa in the TCGA PRAD cohort. Unexpectedly, there was a close correlation between the DNA methylation status and DMS level (high/low DMS group). Taken together, these findings reveal an association between the transcriptome and DNA methylome and support that the DMS can represent the characteristics of DNA methylation.Moreover, to validate the prognostic value of DMS, we performed hazard stratification and multivariate analysis between the DMS, TMB, Gleason score, and clinical factors in multiple PCa datasets. The results suggested that DMS is an independent prognostic factor and can help improve prognosis prediction in patients with PCa.SPOP and TTN mutation in patients with PCa. In addition, DMS can predict the efficacy of castration, androgen deprivation therapy, and chemotherapy.Finally, our results have clinical implications, as we established a DMS system that can describe DNA methylation characteristics. The prognostic value of the DMS system and nomogram based on clinical variables and DMS was validated in multiple PCa cohorts. The DMS can also be used to evaluate clinicopathological characteristics, such as the TMB, CNV, MSI status, and This study had some limitations. First, the reaction rate of the PCa antitumor therapy cohort was not accessible. The predictive value of the DMS for antitumor therapy in PCa must be further verified in further studies. Second, because of batch effects, the DMS cannot be directly compared between different cohorts without batch correction.In summary, we explored three different metabolic subtypes based on the characteristics of DNA methylation and established a DMS system for PCa. DMS, which is associated with the tumor metabolic status, TMB, CNV, and MSI, is an efficient index for predicting DFS and therapeutic responsiveness and may help facilitate personalized antitumor therapy for patients with PCa.


Ion implantation is a superior post-synthesis doping technique to tailor the structural properties of materials. Via density functional theory (DFT) calculation and ab-initio molecular dynamics simulations (AIMD) based on stochastic boundary conditions, we systematically investigate the implantation of low energy elements Ga/Ge/As into graphene as well as the electronic, optoelectronic and transport properties. It is found that a single incident Ga, Ge or As atom can substitute a carbon atom of graphene lattice due to the head-on collision as their initial kinetic energies lie in the ranges of 25–26 eV/atom, 22–33 eV/atom and 19–42 eV/atom, respectively. Owing to the different chemical interactions between incident atom and graphene lattice, Ge and As atoms have a wide kinetic energy window for implantation, while Ga is not. Moreover, implantation of Ga/Ge/As into graphene opens up a concentration-dependent bandgap from ~0.1 to ~0.6 eV, enhancing the green and blue light adsorption through optical analysis. Furthermore, the carrier mobility of ion-implanted graphene is lower than pristine graphene; however, it is still almost one order of magnitude higher than silicon semiconductors. These results provide useful guidance for the fabrication of electronic and optoelectronic devices of single-atom-thick two-dimensional materials through the ion implantation technique. Ion implantation is a widely used technique to modify the structural and electronic properties of various materials in the semiconductor industry. For bulk materials, such as silicon-based semiconductor, the alien species can be introduced into the near-surface region of target material under irradiation of accelerated ion beams, and the concentration and depth distribution of doping atoms can be controlled by adjusting the flux and kinetic energy of incident ions. Similarly, the ion implantation technique could be applied in the modification and fabrication of two-dimensional materials ,2,3. In In spite of a variety of incident ion species, only the implantations of boron (B) ,8,9,10, In this work, spin-polarized density functional theory (DFT) calculation, ab-initio molecular dynamics simulations (AIMD) combined with stochastic boundary conditions (SBC) model are used to investigate the structural and electronic properties of implanted Ga/Ge/As-graphene systems at the atom-scale level. The kinetic energy ranges of incident Ga/Ge/As atoms are calculated to be successfully implanted into graphene through head-on collisions without inducing any vacancies, in agreement with available experimental results . The widTo simulate the energy exchange between a non-equilibrium system and the surrounding environment, it is essential to apply a reasonable heat bath. As proposed by Kantorovich and Rompotis , the colMolecular dynamics simulations based on spin-polarized DFT are carried out by Vienna Ab initio Simulation Package  with the projector augmented wave (PAW) potential method . The eleFirstly, we optimize the structures of orthorhombic 5 × 8, hexagonal 4 × 4 and 8 × 8 supercells, as well as the structures of Ga/Ga/As-doped graphene by DFT calculations. The calculated lattice constant of graphene is 2.465 Å, which agrees well with the experimental value of 2.47 Å . The finTo avoid the effect of thermal disorder on the results, the optimized orthorhombic 5 × 8 supercell by DFT calculation is used to quantify the kinetic energy ranges of the incident Ga/Ge/As atoms implanting into graphene lattice. During the process of implantation, Langevin thermostat is used to mimic energy dissipation at 300 K. The incident atom is initially placed at 10 Å above graphene layer, and the given incident energies range from 1 eV to 50 eV with 1 eV interval, corresponding to the initial velocity of ~ 0.1 Å/fs that is able to approach the graphene surface after about 100 fs. Only head-on collisions are considered since the energy transfer from an incident atom to a lattice C atom is maximal. In order to quantify the kinetic energy range of incident atom implanting into graphene, we performed dozens of independent AIMD simulations.An incident Ga/Ge/As atom collides head-on with a C atom in the graphene lattice, so only incident Ga/Ge/As atom with the appropriate kinetic energy can be introduced into the graphene lattice without producing defects. Our AIMD simulations show that a single Ge atom with 22 to 33 eV initial kinetic energy can directly implant into graphene by head-on colliding with a C atom of graphene, and the impacted C atom is sputtered, while the incident Ge atom with lower than 21 eV cannot be inserted into graphene, with higher than 34 eV can penetrate through the graphene layer with leaving a C vacancy defect. The minimal kinetic energy of implanting Ge atom into graphene is 22 eV, consistent with previous experimental results . SimilarAs shown in Our results show that Ge and As atoms have a wide range of kinetic energy for implantation, while Ga is relatively difficult to be inserted into graphene lattice, and the minimum kinetic energies Ga, Ge and As for implantation decrease with increasing atomic number. To verify the differences of implantation between Ga, Ge and As atoms, we analyze the kinetic energy transfer between the incident atom and the sputtered C atom as well as the corresponding atomic-scale snapshots of 26 eV Ga/Ge/As atoms implanted into graphene. As shown in Similarly, the kinetic energy transfers and the corresponding atomic-scale snapshots of 26 eV Ge and As atoms implanting into graphene are shown in As shown in +m2)2Ek1 . The cal3, Ge-C3 and As-C3 are 2.9–3.2 Å, 2.8–2.9 Å and 2.4–2.5Å, respectively. Obviously, the charge transfer from Ge/As atom to C atoms in distorted graphene lattice is greater, and there is a more significant charge accumulation among Ge-C3 and As-C3 than Ga-C3, which indicates that the interactions of Ge-graphene and As-graphene are stronger than that of Ga-graphene. Furthermore, there are hybridization peaks between Ge/As-4p and C-2p orbitals at about 1.5 eV above Fermi level, while the hybridization peaks between Ga-4p and C-2p orbitals appear at 1.0 eV. In brief, the differences in interactions of Ga/Ge/As and distorted graphene are mainly attributed to their different number of 4p electrons. As a result, as shown in As known, the zero-energy bandgap of pristine graphene has limited its applications for further electronic and optoelectronic applications. According to our AIMD simulations and DFT calculations as shown in ε2) and real parts (ε1) of dielectric function can be obtained by the momentum matrix elements between the occupied and unoccupied virtual wave functions and Kramers–Kronig dispersion relation, respectively [ε1 and ε2, the absorption coefficient, electron energy-loss (EELS) spectrum function, refractive index, extinction coefficient and optical reflectivity could be obtained by the equations: To investigate the optical properties of Ga/Ge/As-graphene, DFT calculation within the random phase approximation approach is applied . The optectively ,36,37. Bectively ,39,40.ε1) of ion-implanted Ga/Ge/As-graphene is similar to pristine graphene, while the peaks of ε2 shift to lower energy. As shown in Next, we demonstrate the optical properties of pristine and ion-implanted graphene. As shown in Similarly, ion-implantation can modify the refractive index, optical reflectivity and optical conductivity, as shown in To evaluate the carrier mobility, the effective mass method is used to calculate the transport properties of pristine and ion-implanted graphene. The carrier mobility can be obtained by |m*|2E12 ,45, wher5 cm2 V−1·s−1 as described in 4 cm2 V−1·s−1) as compared to that of pristine graphene. Interestingly, we find that the hole mobility of Ge-graphene is roughly three times larger than that of the electron, while the hole mobility of Ga/As-graphene is about two times smaller than that of the electron. The reason mainly comes from the different hybridization between implanted ions and graphene, which produces different dispersion shapes of valence and a conduction band near the Fermi level (as shown in 3 cm2 V−1 s−1 and ~4.5 × 102 cm2 V−1 s−1 at 300 K, respectively [Due to the nature of the zero-energy bandgap and linear dispersion relation near Fermi level, and the electron and hole mobility of pristine graphene at room temperature is very high up to 10ectively . These uThe control of structural and electronic properties of graphene and other atom-thick two-dimensional materials is a core issue in practical applications . On the In summary, systematic DFT calculations and AIMD simulations based on the SBC model are performed to study structural and electronic properties of implanted Ga/Ge/As-graphene systems at the atom-scale level. The dynamical process of ion implantation, as well as energy transfer and chemical interaction between incident ions and graphene, are investigated. The electronic, optical and transport properties of ion-implanted graphene are analyzed and discussed for possible applications in optoelectronic devices through the coupling with other Van de Waals 2D materials and substrates. The present work will accelerate and broaden the applications of graphene through ion implantation in ultrafast and low power electric and optoelectronic devices, gas sensing devices, single-atom catalysis, as well as a single-photon emitting system, etc.


Herein we report a unique structure design of graphene-isolated Pt from CoNi nanoparticles (PtǀCoNi) for efficiently catalytic CO oxidation in an oxygen-rich atmosphere. CoNi alloy is protected by ultrathin graphene shell from oxidation and therefore modulates the electronic property of Pt-graphene interface via electron penetration effect. This catalyst can achieve near 100% CO conversion at room temperature, while there are limited conversions over Pt/C and Pt/CoNiOx catalysts. Experiments and theoretical calculations indicate that CO will saturate Pt sites, but O2 can adsorb at the Pt-graphene interface without competing with CO, which facilitate the O2 activation and the subsequent surface reaction. This graphene-isolated system is distinct from the classical metal-metal oxide interface for catalysis, and it provides a new thought for the design of heterogeneous catalysts.Achieving CO oxidation at room temperature is significant for gas purification but still challenging nowadays. Pt promoted by 3 Achieving CO oxidation at room temperature is significant for gas purification but remains challenging to perform. Here, the authors report design of graphene-isolated Pt from cobalt-nickel nanoparticles for efficiently catalytic CO oxidation in an oxygen-rich atmosphere. Moreover, as a probe reaction in surface science, it promotes the development of heterogeneous catalysis2. It is well documented that CO adsorbs more strongly on bare Pt surfaces than O2. Therefore, the activation of O2 on Pt is difficult due to the saturated coverage of CO and is the rate-determining step of the reaction4. An approach to facilitate this step is to construct an active interface between Pt and 3d TMs oxides5, at which O2 can preferentially adsorb and dissociate into atomic O for reaction. However, such an interface usually requires a hydrogen-rich atmosphere to keep TMs oxides in the coordinatively unsaturated state, such as FeOx1−6, CoOx1−8, or NiOx1−10, otherwise, it will be deactivated by the deep oxidation of TMs in an oxygen-rich atmosphere.Pt-catalyzed CO oxidation to CO12. It has been found that graphene encapsulating iron-based catalysts exhibit high activity and stability in electrocatalytic oxygen reduction14, oxygen or hydrogen evolution18, and other reactions20 under harsh conditions . Their excellent catalytic performances are derived from the unique electron penetration effect13, that is, TMs in the metallic state do not need to directly contact the reactants but only employ their electrons to activate the outer graphene for reaction. Inspired by this, herein we design a unique graphene-isolated Pt from CoNi nanoparticles (PtǀCoNi) catalyst, of which CoNi can be protected by graphene from oxidation in an oxygen-rich atmosphere and thus effectively trigger the interface activity of Pt–graphene for CO oxidation at room temperature.Recently, a general strategy of graphene encapsulating TMs has been developed to protect TMs from oxidation and corrosion3CN or CH4 on CoNi nanoparticles (NPs). The obtained samples are labeled as CoNi@NC and CoNi@C, respectively, according to the carbon sources of CH3CN and CH4. Then, Pt NPs were synthesized in the form of sol and deposited on them and two control samples, commercial carbon nanotube (CNT) and carbon black (CB), with the same Pt loading amount of 4 wt%. The correspondingly obtained catalysts are labeled as Pt/CoNi@NC, Pt/CoNi@C, Pt/CNT, and Pt/CB, respectively.Graphene encapsulating CoNi alloy was prepared by chemical vapor deposition from CHThe morphology of these catalysts was characterized by a transmission electron microscope (TEM). As shown in Supplementary Fig. III edge (2p → 5d transition) for Pt/CoNi@NC, Pt/CoNi@C, Pt/CNT, and Pt/CB , indicating that electrons of CoNi NPs can penetrate through graphene to Pt NPs.To verify the electron penetration effect of this composite catalyst, the electronic structure of Pt in each catalyst was detected by synchrotron-based X-ray absorption near edge structure spectroscopy (XANES). Figure yst Fig.  was calc4 on the hollow graphene cage exhibits a positive Bader charge of +0.15 due to the electron transfer from Pt to C, whereas the Pt4 on the graphene cage with CoNi NPs inside exhibits a negative charge of −0.08, indicating that CoNi does indeed transfer electrons to Pt through graphene. Benefiting from the electron penetration effect, the binding energy between Pt4 and graphene is strengthened from −2.17 to −2.74 eV when encapsulating CoNi NPs into the graphene cage. This can explain the selective deposition of Pt NPs on the surface of graphene encapsulating CoNi alloy shown in Fig. Two comparable catalyst models were established to further confirm the electron penetration effect Fig. . The Pt4s + 2p orbitals of C in Pt4/CoNi@C and Pt4/C 22 shown in Supplementary Fig. 4/CoNi@C and Pt4/C 7, and the activity can be fully recovered after H2 reduction  Co and Ni can be rapidly oxidized even at near room temperature. Therefore, the intact graphene layer is essential to keep CoNi in the metallic state.To check the role of graphene in protecting CoNi NPs from oxidation, NAP-XPS was applied to monitor the valence states of Co and Ni in Pt/CoNi@NC during an in situ reaction in a flow of 0.067 mbar CO and 1.13 mbar OTo directly observe the break of graphene, TEM characterization on Pt/CoNi@NC samples Fig.  after tex/C catalysts  was applied. The detection depth of LEIS is only two atomic layersses Fig.  were per2 on Pt4/CoNi@C and Pt4/C. For both models, the adsorption energy of CO on Pt site even with a saturated coverage  calculations were carried out to further reveal the surface reaction mechanism of CO oxidation over Pt on graphene encapsulating CoNi alloy and the promotion mechanism of the electron penetration effect. Figure i@C Fig.  without 4/CoNi@C. The energy profile starts from model I with 4CO adsorbed on Pt4/CoNi@C. Then, a gaseous CO adsorbs on model I preferentially than O2 with a decrease of ∆G. On the 5CO-saturated adsorption structure (model II), O2 can still adsorb at the Pt–graphene interface (model III) with a slight increase of ∆G (0.44 eV) due to the loss of entropy. By contrast, O2 cannot adsorb at the interface of Pt4/C but would competitively adsorb on the CO-saturated Pt site with a huge increase of ∆G (1.61 eV). During the transition state (model TS-1), the activation of O2 is assisted by the adsorbed CO, and then CO2 is formed with a moderate barrier of 0.66 eV. The remaining adsorbed O atom will react with another re-adsorbed CO to form CO2 with a similar barrier of 0.63 eV. Therefore, the graphene encapsulating TMs can avoid the CO poisoning of Pt by providing additional sites for O2 adsorption  by urea co-precipitation with equimolar Co(NO3)2·6H2O (Alfa Aesar) and Ni(CH3COO)2·4H2O (Alfa Aesar). In detail, 1.06 g of Co(NO3)2·6H2O, 0.90 g of Ni(CH3COO)2·4H2O, and 1.8 g of urea were dissolved into 100 mL of water. After adding 6.0 g of 30% SiO2 sphere colloidal dispersion, the mixture was refluxed at 100 °C for 12 h with continuous stirring. The precipitate was washed with deionized water to neutral and then dried at 100 °C for 12 h. Secondly, the obtained CoNiOx/SiO2 solid (200 mg) was heated from 30 to 700 °C at a rate of 10 °C/min in a 100 mL/min of 30% H2/Ar to obtain CoNi metallic alloy. After reaching 700 °C, H2 was switched off and CH3CN (bubbling with 70 mL/min of Ar) or CH4  was introduced as carbon sources to form graphene on CoNi NPs. Finally, the cooled sample was immersed into an aqueous solution of 10 wt% HF at room temperature for 12 h and then washed and dried at 40 °C in a vacuum (~0.1 kPa) for 12 h. The obtained samples were labeled as CoNi@NC and CoNi@C corresponding to the carbon sources of CH3CN and CH4.First, CoNi layered double hydroxides were deposited on SiO24. In detail, 50 mg of NaOH and 50 mg of H2PtCl6·6H2O were successively dissolved into 5 mL of ethylene glycol, and then it was refluxed at 160 °C for 3 h with continuous stirring under the protection of Ar flow (50 mL/min) to obtain the Pt colloid. Next, the Pt colloid was added into the ethanol dispersion of CoNi@NC or CoNi@C at room temperature. After ultrasonic oscillation for 0.5 h, an aqueous solution of 1 mol/L HCl was added into the mixture to precipitate Pt NPs on CoNi@NC or CoNi@C. Finally, they were thoroughly washed and dried at 40 °C in a vacuum (~0.1 kPa) for 12 h to obtain Pt catalysts for use.First, a colloidal solution of Pt NPs (1–2 nm) was prepared by glycol reduction3 at 110 °C for 5 h. CB was directly used without further purification. The same preparation processes were conducted to obtain Pt/CNT and Pt/CB as those for Pt/CoNi@NC and Pt/CoNi@C. The loading amount of Pt for each catalyst was confirmed to be around 4 wt% by inductively coupled plasma–optical emission spectrometry.Commercial CNT  and CB (Vulcan XC-72) were used for comparison. CNT was pretreated in an aqueous solution of 37 wt% HNO23, which allows the reduction and reaction of catalysts with the same parameters of temperature and pressure as for the actual testings described below.Transmission electron microscopy (TEM) images were acquired on an FEI Tecnai F20 microscope operating at 200 kV. Aberration-corrected HAADF-STEM images were acquired on a JEOL ARM200F microscope equipped with a probe-forming aberration corrector operating at 200 kV. XRD measurements were conducted on a Rigaku Ultima IV diffractometer with Cu Kα radiation at 40 kV and 30 mA. X-ray absorption near edge structure spectroscopy (XANES) was measured at the BL14W1 beamline of the Shanghai Synchrotron Radiation Facility. NAP-XPS measurement was carried out by a PHOIBOS 150 NAP 1D-DLD analyzer using monochromatic Al Kα radiation (1486.6 eV). LEIS was obtained using an ion-TOF Qtac100 instrument with helium as an ion source. There is an in situ reaction chamber connecting with the chamber of LEIS2, and 79% He (Messer). A 20 mL/min of mixed gas flowed through 20 mg of catalyst  to achieve a space velocity of 60,000 mL g−1 h−1. The tail gas was analyzed online by gas chromatography equipped with a packed column (TDX-01) and a methane converter in front of a flame ionization detector (FID). The methane converter is a micro high-temperature furnace (330 °C) with Ni-based catalyst that can fully convert CO and CO2 into CH4 for FID detection. Before reactions, all catalysts were pre-reduced in high-purity H2  flow at 240 °C for 2 h. After cooling to room temperature in high-purity Ar  flow, temperature-dependent CO oxidation was performed from 25 to 240 °C (or to 120 °C) at a rate of 1 °C/min.CO oxidation was carried out in a quartz tube (6 mm outer diameter and 4 mm inner diameter) reactor. The temperature was measured with a thermocouple on the external surface of the quartz tube right next to the catalyst bed. The reaction gas consisted of 1% CO, 20% O27. The exchange-correlation functional was described as Perdew–Burke–Ernzerhof generalized gradient approximation28. The interactions between ionic cores and electrons were described using projector augmented-wave pseudopotentials29 with kinetics cut-off energy of 400 eV. The vacuum region was set to 15 Å between the clusters. The models of metal clusters encapsulated into graphene (Co27Ni28@Gr) consist of C240 encapsulating 55 atoms. All structures were fully relaxed to the ground state and spin-polarization was considered in all calculations. The convergences of energy and force were set to 1 × 10−4 eV and 0.05 eV/Å, respectively. The free energies of the intermediates were obtained by ΔG = ΔE + ΔZPE − TΔS, where ΔE is the binding energy of adsorption species, and ΔZPE, ΔS is the zero-point energy changes, entropy changes, respectively. Totally, 298.15 K was chosen as the temperature according to the experimental condition. The transition states were identified via automated relaxed potential energy surface scans method recently developed by Philipp N. Plessow30.DFT calculations were performed on the Vienna ab initio simulation package (VASP)Supplementary InformationPeer Review File


In this work, an easy, green, noncovalent surface modification of pristine graphene (GR) was carried out using a single-step method between sodium carboxymethyl cellulose (CMC) and pristine GR, resulting in the formation of a modified CMC–GR (CGR) dispersion with 15% nanosheet content, the first reported in water. Results obtained from thermogravimetry analysis (TGA), Raman spectroscopy, and atomic force microscopy (AFM) confirm that the CMC modifier is successfully decorated on the pristine GR surface. Analyses of transmittance spectrum, zeta potential and transmittance electron microscopy (TEM) images reveal that the modified CGR has a high degree of dispersion. More importantly, the pristine GR is insoluble, while the modified CGR-3, mixed with 1.1 wt% CMC modifier, is easily well dispersed in water and has good flowability, and no sedimentation is observed after more than 3 months. An easy, green, noncovalent surface modification of pristine graphene (GR) was carried out by using a single-step method, resulting in the formation of a modified CMC-GR dispersion with 15% nanosheet content, the first reported in water. Thus, waterborne coatings with good corrosion resistance are urgently needed to be developed.Waterborne coatings have gained much attention for metal protection on account of their reduced emission of volatile organic compounds (VOCs), avoiding serious workplace accidents.2,3 montmorillonite,4,5 boron nitride,6–8 and graphene,9,10 are considered more efficient materials that enhance the corrosion resistance of coatings by improving their barrier properties. Graphene, a promising two-dimensional (2D) nanosheet with only one-atom thickness, has a series of preeminent characteristics, such as high barrier performance, good mechanical properties, outstanding thermal stability and high chemical stability.11–14 At present, it has received increasing attention in the field of anticorrosion coatings for ultralight metals, and it has been used in waterborne anticorrosion coatings to enhance anticorrosion performance.15,16 However, graphene is not easy to disperse in water due to its high surface area, strong van der Waals forces, and intrinsic hydrophobicity. One effective method is to gain hydrophilic graphene oxide (GO) using chemical oxidation modification. Unfortunately, the defect sites introduced during the process can affect the long-term anticorrosion performance. Furthermore, many studies have been done to improve the wettability of graphene in matrix through facile noncovalent modification. Chang et al.17 constructed polyaniline/graphene waterborne epoxy coatings to enhance the corrosion resistance of steel against O2 and H2O. He et al.18 used tannic acid (TA) as intercalator to disperse graphene in water via π–π noncovalent bonds, forming graphene–TA hybrids and obtaining highly efficient anticorrosive epoxy coatings. Yu et al.19 prepared waterborne epoxy coatings combined with dispersed sandwiched polyvinyl butyral@graphene@polyvinyl butyral composites to improve the corrosion resistance of commercial aluminum alloys, and the effect can last 120 days even when exposed to simulated seawater.Polymer nanocomposites, including nanofillers such as silica,In recent years, most works have only focused on graphene dispersion through covalent and noncovalent modification using a low graphene content, between 0.1% and 5%. However, it is difficult to use this kind of graphene dispersion as a corrosion resistance additive for industrial waterborne coatings. Based on the demand for corrosion resistance and mass production requirements, graphene dispersions with high solid content and good dispersibility in water need to be designed and developed.In this paper, we report an easy, green modification of graphene by using a noncovalent strategy between CMC and graphene, as shown in This method for green surface modification of pristine GR can be used to prepare GR/waterborne polymer nanocomposites. The entanglements between the long chains of CMC and the polymer matrix substantially enhance the interactions between them and accordingly improve their compatibility. Importantly, this strategy is available and economical due to its easy operation in industry. We also believe that this work will accelerate the development of industrial anticorrosion coatings for ultralight metals.2.2.12 g−1) were obtained from Sixth Element (Changzhou) Materials Technology Co. Ltd Sodium carboxymethyl cellulose  was purchased from Hefei Aigo Additives Technology Co. Ltd. Deionized water was prepared using the appropriate equipment.Pristine graphene (GR) nanosheets  g) and CMC modifier (n g) water were added to a three-necked flask and stirred with a high-speed stirrer at 900 rpm for 15 minutes. Then, crude GR (15 g) was added to the preprepared mixture and sonicated for 1 hour to obtain a uniform CGR dispersion. For the sake of comparing different CGR dispersions, four different contents of CMC modifier  were designed to prepared CGR-x , correspondingly. Then, the products were purified after each modification by ultrafiltration. The resulting CGR solid powders were dried overnight at 80 °C in vacuum.First, deionized water . The transmittance of the graphene dispersion was investigated with an ultraviolet spectrophotometer (UV2600). Morphology of the graphene dispersion was investigated by field emission scanning electron microscopy  and transmittance electron microscopy . Atomic force microscopy  was used to characterize the thickness of the GR nanosheets.Thermogravimetry analysis (TGA) of pristine GR and CGR was performed under nitrogen on a STA409PC thermogravimetric analyzer (Netzsch instruments) with a temperature range of 25–1000 °C at a heating rate of 10 °C min3.3.1−1, respectively. The first stage of mass loss terminates at approximately 180 °C, which is due to the rejection of the adsorbed water from the interlayers of the materials. The TGA plot of CMC indicates a gradual mass loss of around 58.4% as the temperature reached 600 °C. Furthermore, we noted that CGR showed a significant weight loss in the range of 260–600 °C, corresponding to pyrolysis of the CMC modifier. The results indicate that the CMC modifier can be adsorbed on the surface of graphene.Supporting evidence for the noncovalent attachment of CMC modifier on the pristine GR surfaces comes from the thermogravimetry analysis (TGA). 3.218 The D band and G band are represented in the in-plane vibration of sp2 carbon atoms and the vibration of sp3 carbon atoms from the functional groups, respectively. It can be seen from −1 (D band) and 1586 cm−1 (G band), ID/IG = 1.03. The ID/IG ratio presents a slight increase from 1.03 for pristine GR to 1.06 for CGR-4, indicating that no more defects are introduced after the modification of pristine GR with CMC modifier, and the graphene preserves its basic structural properties.Raman spectroscopy is usually employed to distinguish the ordered and disordered carbon structures of graphene.3.3The diluted dispersions of pristine GR and CGR with obvious Tyndall effect in water were prepared through mechanical dispersion for 15 min. On the basis of the dynamic light scattering (DLS) principle, the average diameter and size distribution of the graphene dispersions were analyzed using a zeta potential analyzer, as shown in 50) is about 4560 nm, as shown in According to the result, the pristine GR was dispersed poorly in water, and serious agglomeration was formed because of the high surface area, strong van der Waals forces, and intrinsic hydrophobicity of GR. Its average diameter (D3.4For quickly evaluating the dispersion effect of graphene, it is necessary to compare the optical transmittance. 3.5The most important parameter defining surface properties of electrostatically stabilized nanomaterials in aqueous solutions is the zeta potential value. The relation between zeta potential and CMC modifier content in the GR nanosheets is shown in The inset photo in 3.618,20 which is attributed to the poor dispersion of GR nanosheets (The surface morphologies of pristine GR and modified GR dispersed in water were examined by SEM, as shown in nosheets . In compFurthermore, TEM results confirmed the dispersibility of pristine GR and modified CGR-3 with the best dispersion stability in 4.In summary, a CMC-modified CGR dispersion was successfully prepared in aqueous solution by single-step noncovalent functionalization technique. The results show that the optimum content of CMC modifier coated on the surface of graphene is 1.1 wt%. The modified CGR possesses good dispersibility and good flowability in water. The prepared CGR-3 dispersion, with no sedimentation after more than 3 months, has potential application as a functional corrosion resistance additive for waterborne coatings, providing a new strategy for the high-value-added use of graphene.There are no conflicts to declare.