In the absence of noticeable grain growth, the ultrafine-grained alloy maintains the strength up to 380 MPa after SP deformation, which considerably exceeds the value (250 MPa) for the alloy in the peak-aged coarse-grain state. This finding opens pathways to form high-strength articles of Al-Mg-Si alloys after superplastic forming.The paper reports on the features of low-temperature superplasticity of the heat-treatable aluminum Al-Mg-Si alloy in the ultrafine-grained state at temperatures below 0.5 times the melting point as well as on its post-deformation microstructure and tensile strength. We show that the refined microstructure is retained after superplastic deformation in the range of deformation temperatures of 120–180 °C and strain rates of 5 × 10 Durestation ,6. The iTo solve this problem, several approached have been developed, which include two main procedures. First, it is necessary to achieve equiaxed homogeneous ultrafine-grained (UFG) microstructures in bulk workpieces, which provide features of low-temperature SP behavior. These microstructures can be formed thanks to specific thermal and deformation treatment using, in particular, severe plastic deformation (SPD) techniques . Second,m) of 0.5 observed at 540 °C for the strain rate in the range of 2 × 10−4 to 5 × 10−4 s−1 with the maximum elongation to failure of 375% was reported in [−3 s−1 to 1 × 10−2 s−1, an elongation-to-failure of 350–480% was recorded [A special interest of the aerospace and automotive industries is focused on Al alloys to be used as key engineering materials . In partorted in ,10. The recorded ,12.mT, where mT is the melting point). However, high deformation temperatures lead to degradation of the UFG structure after completing SP process, and hence to a significant decrease in the strength parameters of the final products. This issue limits the potential of Al alloys for industrial application by forming in SP conditions.Extensive studies of UFG Al alloys ,15,16,17mT) of the UFG 6061 Al alloy with a grain size of 400 nm produced by SPD was reported in [mT) and the strain rate of 1.7 × 10−4 s−1, the elongation to failure of 160% was achieved, while the flow stress did not exceed 40 MPa. An increase in the deformation temperature to 540 °C provided elongation of 280% with a flow stress lower than 6 MPa at the strain rate of 3 × 10−4 s−1. However, the UFG structure notably degraded in the latter case: grain coarsening up to 18 µm occurred. The other researchers reported on the low-temperature SP of the UFG 6063 Al alloy produced by ARB rolling [−³ s−¹ to 5 × 10−¹ s−¹. The authors had suggested that the main mechanism responsible for the occurrence of large uniform or SP deformation at 300 °C is associated with dynamic recovery. It was also mentioned that at 350 °C despite lower SP strain rates of 5 × 10−² s−¹, 5 × 10−³ s−¹ and high elongations, the microstructural instability was observed [The low-temperature SP deformation  temperatures [In the meantime, the recent works have shown that the formation of UFG states in Al alloys with a regulated distribution of alloying elements to form secondary hardening phases as well as segregations or interlayers along grain boundaries can lead to a unique combination of properties, when the strength, plasticity and electrical conductivity of the material simultaneously increase ,21,22. Ieratures ,27,28,29T < 0.5 mT) by retaining the UFG structure, which was hardly possible at SP at higher temperatures reported by the other studies summarized in the state of art. We also report on the thorough study of the post-deformation microstructure and texture evaluation in the SP deformed area of tensile tested samples in different ranges of strain rates and temperatures.Thus, the purpose of this work is to study the features of the UFG structure evolution after ultralow-temperature SP and the possibility of maintaining high post-deformation mechanical strength in an Al-Mg-Si alloy. For the first time, we show the possibility of achieving a high-strength state of the 6060 Al alloy after SP forming at ultralow temperature . The chemical composition of the alloy  has beenAs-received specimens of the studied alloy were produced by conventional hot-pressing and had a typical coarse-grained structure of semi-finished products after pressing. The width of the fibers was approximately 20 µm and the length was 250 µm. Following  prior ton) during HPT was 20. The structure and mechanical properties of the HPT processed alloy were precisely studied on the samples representing a region located at the half radius of an HPT disk.The samples of the required geometry were prepared by a high-precision two-coordinate wire-cutting spark erosion machine ARTA-120  in water at room temperature to eliminate the thermal effects. To nanostructure the material, the high pressure torsion (HPT) technique  was applMechanical tests were carried out at the temperatures of 120 °C, 150 °C and 180 °C. The thermal stability of the UFG 6060 alloy microstructure at these temperatures was examined by one-hour annealing . The gra−2, 5 × 10−3, 10−3, 5 × 10−4 and 10−4 s−1. The gauge of the tensile test samples was 3.3 × 1.1 × 0.6 mm3. Strain rate sensitivity coefficient values were calculated according to the formula:Tensile tests were carried out with the help of a universal INSTRON 5982 testing system with Bluehill 3 software and a computer system for evaluating mechanical characteristics . Tensile tests were performed at the strain rates of 10−3 s−1 until it was elongated up to approximately 50%. After such deformation, the sample was cooled in water at room temperature. At the second stage, after control measurements of the gauge, the samples were tensile tested at room temperature with the strain rate of 5 × 10−4 s−1. Vickers hardness (HV) was measured on a Micromet-5101 device  with a diamond pyramid indenter, a load of 1 N and a dwell time of 15 s. Measurements were taken with the increments of 0.3–1.0 mm, with the total number of measurements of at least 10. The hardness values were determined automatically using the OmniMet MHTFS software.In order to evaluate the room temperature mechanical performance of the UFG alloy in the structural state formed after SP deformation, a specific procedure was used for the state exhibiting the best SP performance (tensile deformed at 150 °C). At the first stage, the sample was tested under SP conditions at 150 °C and the strain rate of 5 × 103) GOST 4461 and 80% methanol grade B (CH3OH) GOST 2222.The microstructure was studied using a JEOL JEM-2100  transmission electron microscope (TEM) operated at 200 kV. For the TEM analysis, the disc-shaped samples of 3 mm in diameter and 0.1–0.15 mm in thickness were twin jet electropolished at a temperature of −25–30 °C and a voltage of 10–12 V. Jet polishing of TEM foils was carried out on a Tenupol-5 automatic electrolytic thinning unit in a solution of 20% nitric acid . To produce a suitable surface, each specimen was mounted and mechanically polished using conventional metallographic procedures followed by a final electro-polishing with a suspension of colloidal silica. EBSD was then conducted by FEI Quanta 600  field-emission-gun scanning electron microscope equipped with TSL OIManalysis . Due to Kf) was 1.2, the volume fraction of HAGBs was 91%, the maximum misorientation angle of the grain boundaries was in the range of 50–60°, the average misorientation angle was 37.2°, and the average HAGBs misorientation angle was 38.7°.HPT processing of the 6060 alloy samples resulted in the development of a homogenous UFG structure predominantly formed by HAGBs as demonstrated by EBSD microstructure analysis a,b and c2Si phase (a metastable modification of the Mg2Si-phase) [2Si particles resulted from dynamic precipitation was described in more detail in [TEM analysis revealed separate nanosized precipitates (with the size of less than 2 nm) in the interiors and at the boundaries of ultrafine grains c. Bundlei-phase) . The foretail in ,34, wher0.2) of the alloy to 500 ± 4 MPa and the ultimate tensile strength (σUTS) to 530 ± 4 MPa accompanied with elongation to failure of 6.5 ± 0.2%. The strength of the UFG 6060 alloy by two times exceeds values typical for the coarse-grained alloy after conventional heat treatment (T6) to achieve the maximum strength , 150 °C (0.45 mT), and 180 °C (0.47 mT). The figure indicates that the flow stresses decrease with increasing strain rate and temperature.Mechanical tensile tests to reveal SP features during deformation of the UFG 6060 Al alloy were performed at temperatures of 120–180 °C in the strain rate range of 10nce with . Figure −3 s−1–10−4 s−1, and the m parameter was within 0.29–0.32. Deformation curves at 180 °C did not demonstrate considerable scattering in elongation to failure with variation in the strain rate.For all cases, the curves are characterized by pronounced strain hardening and a smooth decrease in the flow stress after achieving peak values. As it is assumed, the observed strain hardening is related to multiple intragranular slip, with no steady-state flow stage as observed in ,37. As am and achieved elongations to failure, it can be stated that the UFG 6060 alloy demonstrates the features of SP behavior at ultralow temperatures (T < 0.5 mT) [m parameters were observed at every testing temperature.Considering the values of calculated  0.5 Tm) ,16. For In order to reveal the effect of low-temperature SP deformation on the microstructure of UFG 6060 alloy, the EBSD and TEM studies were conducted in the SP-deformed gauge areas of the fractured UFG samples after tensile tests at different temperatures.fK= 1.3.The EBSD analysis of the alloy after SP deformation at 120 °C showed that the fraction of LAGBs and HAGBs did not change, amounting to 9% and 91%, respectively b. The avfK= 1.5  pole figures calculated from by EBSD orientation data represent local texture formed in the UFG 6060 alloy after HPT and subsequent SP deformation at 150°C and 180 °C . For illThe microhardness measured after SP deformation are consistent with the microstructure studies. Higher microhardness of the material was evaluated after tensile tests: at 120 °C-108 ± 5 Hv, while at 150 °C it was 107 ± 5 Hv and at 180 °C it was 85 ± 4 Hv. The hardness after the SP at 180 °C decreases to the level typical for the coarse-grained alloy subjected to T6 treatment .−3 s−1. This post-SP deformation state was selected as exhibiting the most pronounced SP behavior with the maximum elongation to failure (220%) and m = 0.32. The results of mechanical tests at room temperature are shown in To assess the strength of UFG Al alloy 6060 after SP deformation, tensile tests were carried out at room temperature on samples subjected to 50% of SP deformation at 150 °C with a strain rate of 5 × 10mT). Observations suggested that the equiaxial microstructure may have evolved by splitting of elongated grains, which assumes that extensive intragranular sliding has occurred, this observation is consistent with the data reported recently in literature [−4 s−1. The uniform distribution of nanosized metastable Mg2Si particles (2–10 nm in size), as well as grain boundary segregation [m parameter to 0.29, alongside the stability of the grain structure during low-temperature SP deformation being maintained by a high fraction of HAGB (more than 90%) in the UFG state produced by HPT. It can be deduced that the stability of the UFG 6060 alloy under SP conditions is one of the key factors contributing to the observed SP features.As shown in ,34, the terature ,38,39,40terature ,42, fineregation ,42, may mT) superplastic behavior is reported for the UFG 6060 Al alloy demonstrated by tensile tests carried out at 120 °C (0.42 mT), 150 °C (0.45 mT) and 180 °C (0.47 mT) at different strain rates. The microstructure parameters of the UFG 6060 alloy such as ultrafine grain size and a large portion of high-angle boundaries do not drastically change after SP deformation, however, the enhanced formation of low angle boundaries is observed under conditions where the most prominent superplastic behavior occurs. Grain boundary sliding and intragranular slip with subgrain boundary formation appear to notably contribute to the superplastic flow. We show that the strength of the UFG 6060 alloy after SP deformation at the temperature of 150 °C (0.45 mT) and the strain rate of 5 × 10−4 s−1 can be as high as 380 MPa in combination with elongation to a failure of 7%. Such strength parameters notably exceed those typical for the standard T6-treated 6060 alloy. This finding opens pathways to develop high-strength products of Al-Mg-Si alloys by forming in superplastic conditions.Ultralow-temperature (T < 0.5


FU and NU are physiosorbed on C6N6 with adsorption energies of −28.14 kcal/mol and −27.54 kcal/mol, respectively. The outcomes of the non-covalent index (NCI) and quantum theory of atoms in molecules (QTAIM) analyses reveal that the FU@C6N6 and NU@C6N6 complexes were stabilized through van der Waals interactions. Natural bond order (NBO) and electron density difference (EDD) analyses show an appreciable charge transfer from the drug and carrier. The FU@C6N6 complex had a higher charge transfer (−0.16 e−) compared to the NU@C6N6 complex (−0.02 e−). Frontier molecular orbital (FMO) analysis reveals that the adsorption of FU on C6N6 caused a more pronounced decrease in the HOMO-LUMO gap (EH-L) compared to that of NU. The results of the FMO analysis are consistent with the NBO and EDD analyses. The drug release mechanism was studied through dipole moments and pH effects. The highest decrease in adsorption energy was observed for the FU@C6N6 complex in an acidic medium, which indicates that FU can easily be off-loaded from the carrier (C6N6) to a target site because the cancerous cells have a low pH compared to a normal cell. Thus, it may be concluded that C6N6 possesses the therapeutic potential to act as a nanocarrier for FU to treat cancer. Furthermore, the current study will also provide motivation to the scientific community to explore new surfaces for drug delivery applications.Continuous studies are being carried out to explore new methods and carrier surfaces for target drug delivery. Herein, we report the covalent triazine framework C Cancer is a leading cause of death worldwide and poses a serious threat to public health. To deal with this daunting disease, several therapies, such as chemotherapy, hormone therapy, and radiotherapy  are in uOwing to the significance of DDSs in medicinal chemistry, the development of innovative and highly effective DDSs with improved therapeutic profiles is inevitable. Arguably, the application of DDSs has gained enormous attention as one of the most promising and efficient methods reported for the treatment of cancer and which abates the severely harmful side effects of the conventional approaches to cancer treatment . One of Gratifyingly, recent progress in the field of nanotechnology has revolutionized almost every aspect of research and has substantially contributed to the diagnosis and treatment of several deadly diseases. In addition, controlled drug delivery has been carried out both experimentally and numerically. In a reported study, the controlled drug release of gliclazide from a polymeric matrix system was studied . Numerou60) surface for temozolomide and carmustine and reported that the release of these chemotherapeutic drugs to biological systems is considerably facilitated by the enhanced polarity of C60 when attached to these anti-cancer drugs [The ubiquitous presence of computational investigation to better understand and elaborate the research findings is beyond any doubt, and the field is attracting serious attention from the scientific community day by day. In-depth theoretical studies have played a pivotal role in the development of DDSs as well, and their contribution is on the rise. For example, Samanta et al. explored the application of a fullerene  as a drug delivery platform for NU and FU. The reports in the literature have revealed that C6N6 has not yet been investigated as a DDS for the FU and NU anti-cancerous drugs. Our proposed system can effectively interact with the targeted drugs because of its unique characteristics related to an electron-rich cavity and high surface area [6N6, which are commonly used for cancer treatments. DFT simulations have been applied to study the mechanism of drug delivery and to provide a detailed investigation of the electronic properties of this class of materials, as well as a better understanding of the nature of the interactions between the studied drug molecules and the triazine framework.As detailed above, the fundamental and challenging issues faced during the delivery of anti-cancer drugs with macrosystems are poor absorption and poor biocompatibility in specific regions due to the lack of the optimal adsorption of the drug molecule onto the surface of the drug carrier. Nanostructures that have been studied experimentally for drug delivery systems are primarily metal alloys and quantum dots, which have low biocompatibility due to their hydrophobic nature . Carbon olecules ,56, hereace area . The cur6N6 and Drug@C6N6 complexes were computed at a ωb97XD/6-31++G  level of theory. ωb97XD is range-separated functional that is considered best for non-covalent interactions [6N6 by different orientations to find the most stable geometry of each complex. Frequency analysis was carried out to confirm the true minimum nature of each optimized complex on the potential energy surface. The adsorption energies of Drug@C6N6 were calculated as:complex is the energy of the Drug@C6N6 complex, EC6N6 is the energy of C6N6, and EDrug is the drug molecule. The nature of the interactions between the drugs and C6N6 was explored by non-covalent interaction (NCI) analysis. NCI analysis mainly comprises a reduced density gradient (RDG), the product of the electron density (ρ), and the sign of λ2. The RDG is a product of the density and its derivative, which is represented as [All the simulations in the current study were carried out by employing Gaussian 09 software. The geometries of the Cractions . In ωb97ractions . Moreoveractions . The eleractions ,59,60,61ented as .s=12(3πρ) is small. Thus, a small change in density gives a noticeable change in the RDG scale. The strength and nature of non-covalent interactions are explored through product (λ2) ρ. So, the nature of the NCI depends on the sign and value of λ2. If the product of (λ2) ρ is large and negative, electrostatic interactions appear in the form of blue spikes in low RDG regions. If (λ2) ρ is negative and small (below −0.02 a.u.), NCIs are projected in the form of green spikes, which indicate the presence of van der Waals interactions. The large and positive values of (λ2) ρ show repulsive interactions and are projected in the form of red spikes [In non-covalent interactions, the electron density , the Laplacian of the electron density (ρ), the potential energy density (V), the local kinetic energy density (G), and the electronic energy density (H) are used to explain the BCPs [ρ is greater than 0.1 a.u. and (ρ) is large and negative. On the other hand, ρ < 0.1 a.u. and a positive (ρ) value reveals close shell interactions [6N6 carrier.Quantum theory of atoms in molecules (QTAIM) analysis provides detailed information about inter- and intra-molecular interactions. Through QTAIM analysis, various types of topological terms, such as bond critical points (BCPs), ring critical points (RCPs), cage critical points (CCPs), and nuclear critical points (NCPs) can be explored. Bond critical points are usually applied to describe the nature of NCIs. Various parameters, such as the electron density  [6N6 makes it a potential candidate for drug loading and delivery at target sites. The geometries of the selected anti-cancerous drugs  . The hig6N6 in different orientations to obtain the most stable Drug@C6N6 complexes for effective drug delivery at a specified target. The resultant most stable geometries of the Drug@C6N6 complexes are reported in 6N6 are shown in the 6N6 complexes are provided in Fluorouracil and nitrosourea were relaxed over C6N6 and NU@C6N6 resulted in adsorption energies of −28.14 kcal/mol and −26.57 kcal/mol, respectively. The stable geometries of both drug complexes were obtained as a result of the interaction of the H-atoms of the drugs with the N-atoms of the C6N6. This happened due to the electron-rich cavity of the C6N6 surface and the electrophilic nature of the H-atoms of the drug molecules. The adsorption energy values of the FU@C6N6 and NU@C6N6 complexes reveal that both drugs had strong binding with the C6N6 surface.The most stable complexes of FU@C6N6 complex  was comparatively higher than that of the NU@C6N6 complex . The stable complex of the FU@C6N6 was obtained due to the shorter interaction distances of 1.97 Å (H5–N1) and 2.13 Å (H6–N3) between the H-atoms of the fluorouracil and the N-atoms of the C6N6. The rest of the H–N interaction distances noticed in the FU@C6N6 were 2.57 Å (H5–N2) and 2.61 Å (H6–N4). The interaction distances between the H-atoms of the NU and the N-atoms of the C6N6 were 2.03 Å (H5–N1) and 2.14 Å (H6–N4), which are slightly higher than those of the FU@C6N6 complex. The adsorption energies and interaction distances show the stability of the FU@C6N6 and NU@C6N6 complexes through physisorption, which is best for the C6N6 surface to act as a drug delivery platform.The adsorption energy of the most stable complex of the FU@C6N6, NCI analysis was performed. The results of the NCI analysis were displayed in 3D topological forms and 2D RDG plots. The topologies and RDG plots of the FU@C6N6 and NU@C6N6 complexes are presented in 6N6 and NU@C6N6 complexes by van der Waals interactions  interacted with C6N6 via different energies.To differentiate between the electrostatic, van der Waals, and repulsive interactions between the drugs and Cions see . The red6N6, the green patches in the 3D plot are not specified between any two atoms of the drug and C6N6, but rather distributed among many atoms. The 3D topology of the FU@C6N6 reveals that the thick and wide green patches between the H-atoms of the FU and the N-atoms of the C6N6 caused stronger interactions and higher stability of the FU@C6N6 compared to the NU@C6N6 complex. Similarly, the mixture of the bluish-green spikes in the 2D RDG plot of the FU@C6N6 increases up to −0.02 a.u., whereas, in the case of the NU@C6N6, the green spikes are in the range of −0.02 to −0.01 a.u., which further justifies the higher stability of the FU@C6N6 complex than that of the NU@C6N6 complex. Therefore, the NCI analysis shows that both complexes were stabilized by van der Waals interactions. These interactions may facilitate the off-loading of the drug to the target site.In the case of the FU@C2ρ). In addition, the potential energy density (V), kinetic energy density, and their sum ) give valuable information about the BCPs. V/G is the ratio of the potential energy density (V) to the kinetic energy density (G). A V/G less than 1 indicates NCIs. In addition, an individual bond interaction energy of >−3.5 kcal/mol (negatively strong) shows shared shell interactions [Through QTAIM analysis, we can capture all the non-covalent interactions that are elusive to other methods. The strength of NCI mainly depends upon the sign and value of the electron density (ρ) and the Laplacian of the electron density  values were comparatively lower than those of the FU@C6N6 complex. Similarly, the rest of the BCP parameter values of the NU@C6N6 were also low compared to those of the FU@C6N6. Thus, the high stability of the FU complex was a result of the high values of ρ and (∇2ρ). These BCP parameters reveal that FU could easily be adsorbed on the carrier surface compared to NU. The results obtained through the QTAIM analysis are in good agreement with those of the interaction energies and NCI analysis.The topologies obtained through the QTAIM analysis are presented in 6N6 was explored through NBO analysis. The EDD was calculated by subtracting the sum of the drug and C6N6 charges from the charges of the Drug@C6N6 complex. The isosurfaces resulting from the EDD analysis consisted of two colors: green and yellow. The green isosurfaces indicate the accumulation of electron density, whereas the yellow surfaces show the depletion of electron density. So, the existence of both types of isosurfaces reveals the exchange of charges between the drug and C6N6. The isosurfaces executed through the EDD analysis are given in NBO and EDD analyses give valuable information about charge transfer in interacting systems. The magnitude of the charge transfer between the drug and C6N6 and NU@C6N6 complexes were −0.16 e− and −0.02 e−, respectively. The signs of the NBO charges reflect that the charge was transferred from the C6N6 to the drug molecules. This happened due to the electron-rich cavity of the covalent triazine framework C6N6. A higher charge transfer value was noticed in the case of the FU@C6N6, which indicates stronger interactions between the FU and C6N6 compared to the complex of NU@C6N6. The higher NBO value on the FU@C6N6 complex was due to the presence of highly electronegative F and O atoms in the FU. In addition to this, the FU interacted with the C6N6 through shorter interaction distances compared to those of the NU atoms. The NBO values were verified by EDD analysis.The NBO charge values of the FU and NU in the FU@C6N6 and NU@C6N6 complexes, green isosurfaces mainly appeared on the N-atoms of the C6N6, showing an accumulation of electron density, whereas yellow isosurfaces appeared on the H-atoms, showing depletion of the electron density. The yellow isosurfaces on the H-atoms were due to their bonding with the O-atoms of the C6N6 and interactions with the N-atoms of the C6N6. This shows that charges were transferred from the triazine ring of the C6N6 to the FU and NU atoms. However, the drug atoms consist of both types of isosurfaces, which verifies the presence of electrophilic and nucleophilic ends on the drug. The NBO and EDD analyses indicate that the highest charge transfer was in the case of FU (−0.16 e−), which is in good agreement with the NCI, QTAIM, and interaction energy analyses. The higher charge exchange between the FU and C6N6 indicates the balanced loading capacity of the C6N6 to FU compared to that of NU.In the case of the FU@C6N6 before and after complexations with the drug molecules. The energies of the highest occupied molecular orbital (HOMO), the lowest unoccupied molecular orbital (LUMO), and the HOMO-LUMO gaps (EH-L) of pristine C6N6 and Drug@C6N6 are given in FMO analysis was carried out to analyze the changes in the electronic properties of the C6N6 were −9.63 eV and −1.79 eV, respectively. The EH-L gap of the C6N6 was 7.84 eV. A change in the EH-L gap was noticed after the adsorption of drugs on the C6N6. 6N6 and Drug@C6N6 complexes. The EH-L gaps after the adsorption of FU and NU on the C6N6 were 6.71 eV (FU@C6N6) and 7.54 eV (NU@C6N6), respectively.The HOMO and LUMO energies of the CH-L gap were observed for the complexes of FU@C6N6 and NU@C6N6 compared to that of bare C6N6. The conductivity of the complexes depended on the EH-L gap. Complexes with a low EH-L gap showed better conductivity compared to those with a high EH-L gap. This reveals that the complex of FU@C6N6 had better conductance than that of the NU@C6N6 complex. The substantial decrease in the EH-L gap of the FU@CC6N6 happened due to a significant increase in the energy of the HOMO (from −9.63 to −8.63) and a decrease in the energy of LUMO (from −1.79 eV to −1.92 eV), respectively. The EH-L gap for the NU@C6N6 complex was 7.54 eV, which was slightly lower than that of the bare C6N6 (7.84 eV). This happened due to a slight increase in the HOMO energy (−9.32 eV) while the LUMO energy remained almost unchanged (−1.78 eV).Decreases in the E6N6. The orbital densities of the FU@C6N6 and NU@C6N6 are presented in 6N6, the HOMO densities were mainly distributed over the nitrogen atoms of the triazine rings, whereas the LUMO densities were on the carbon atoms. The orbital densities of the complexes show the charge transfer during the excitation from the HOMO to the LUMO. In the case of the FU@C6N6 complex, the HOMO density was majorly distributed over the FU, while a part of the HOMO density can also be seen in the C6N6. However, the LUMO was completely located on the C6N6, and the orbital density distribution pattern of the NU@C6N6 complex was slightly different from the FU@C6N6 complex. The maximum portion of the HOMO density was concentrated on the NU, while a small contribution was shown by the N-atoms of the C6N6, and the LUMO was fully distributed over the C6N6 surface. This shift of orbital densities from the FU (HOMO) to the C6N6 (LUMO) caused a significant decrease in the EH-L gap. Thus, it can be concluded that the adsorption of FU caused an increase in the electrical conductivity of the C6N6 surface. This significant increase in the conductivity of the FU@C6N6 complex makes C6N6 a promising carrier for drug delivery through electrical therapy. The drug delivery mechanism of dexamethasone (DEX) from polydopamine/polypyrrole composites has also been reported on a conductivity basis [The orbital isosurfaces for the complexes were also different compared to the pristine Cty basis ,79 The r6N6 COF is zero prior to the loading of drugs. This may happen due to the symmetry in the structure and the cancelation of the individual dipoles. The adsorption of FU and NU over C6N6 changed the µ values to 5.77 D and 3.25 D, respectively in the resultant complexes. The symmetry of the C6N6 was disturbed by the adsorption of the FU and NU on C6N6. This adsorption created new dipoles on the electron-withdrawing and -donating parts of complexes, where the drug and carrier surfaces interact through specific distances. The increase in the µ of complexes is essential for their solubility in an aqueous medium, which also helps the mobility of drugs in a living system. The substantial increase in the µ of the FU@C6N6 complex reveals the high affinity of FU for biological systems compared to NU. This shows that C6N6 can effectively release FU on a target site compared to NU. The results of the µ are consistent with the FMO, NBO, interaction energy, and NCI analyses.The change in the dipole moment also helps to explain the solubility of the drug and its release at the target site. The µ of C6N6  and NU@C6N6  complexes were also compared with the already studied adsorption energies of FU and NU at different surfaces through different DFT tools. The reported adsorption energies of the drugs on different carriers are comparable to the values in the current study . In some cases, the adsorption energies were too high; FU@NaB40 fullerene and Ti-BNNT showed adsorption energies of −30.0 kcal/mol and −39.8 kcal/mole, respectively (see The adsorption energies of the FU@Cvely see . These v6N6 and FU@C6N6. We carried out DFT simulations of drugs (FU@C6N6 and NU@C6N6) loaded on C6N6 in an acidic environment. In an acidic medium, we protonated the interacted ends (N-atoms) of the C6N6 with H+ and again relaxed the structure at the same level of theory. When comparing the FU@C6N6 and FU@C6N6 complexes in an acidic media, drastic decreases in the adsorption energies  and increases in the interaction distances (1.97 Å to 4.99 Å) were observed in the case of the FU@C6N6 complex. This indicates the easy off-loading of FU from the carrier (C6N6) to the target site compared to NU. In an acidic medium, a number of protons can also be attached to the other ends of the drug and surface. However, these protons may not affect the drug release at the target site.The drug release from the carrier surface to the target cell is one of the most important steps. The surrounding environment of a malignant cell environment usually has a pH of less than 6 compared to normal blood cells (7.35–7.45) . Thus, w6N6 for FU and NU drugs through DFT simulations. Adsorption energies of −28.14 kcal/mol and −27.54 kcal/mol were observed in the most stable complexes of FU@C6N6 and NU@C6N6, respectively. The nature and strength of the non-covalent interactions were explored through NCI and QTAIM analyses. The outcomes of these analyses reveal that the stability of the FU@C6N6 and NU@C6N6 complexes was established through van der Waals interactions. The electronic properties of all the complexes were explored through NBO, EDD, and FMO analyses. Both the NBO and EDD analyses show an appreciable charge transfer between the drug and carrier. The FU@C6N6 complex had the highest charge transfer (−0.16 e−), while the NU@C6N6 complex had the lowest charge exchange (−0.02 e−). The EH-L gaps of the 6.71 eV and 7.54 eV were observed for the FU@C6N6 and NU@C6N6 complexes, which were comparatively lower than that of the bare C6N6 (7.84 eV). The adsorption of the FU on the C6N6 caused a potential decrease in the EH-L gap compared to that of the NU@C6N6. Thus, the loading of the FU on the C6N6 caused enhanced conductivity of the FU@C6N6 complex compared to that of the pristine C6N6. The results of the FMO analysis are consistent with those of the NBO, EDD, NCI, and QTAIM analyses. The drug release mechanisms were further studied through dipole moments and pH effects. Due to the low pH of malignant cells, simulations were performed in an acidic medium. The highest decrease in adsorption energy was observed for the FU@C6N6 complex in an acidic medium. These findings indicate that FU can easily be off-loaded from a carrier (C6N6) to a target site. Thus, it may be concluded that C6N6 is a better carrier of FU compared to NU for drug delivery.To explore new drug delivery carriers, we used the covalent triazine framework C


Background: Mixed-reality sports are increasingly reaching the highest level of sport, exemplified by the first Virtual Tour de France, held in 2020. In road races, power output data are only sporadically available, which is why the effect of power output on race results is largely unknown. However, in mixed-reality competitions, measuring and comparing the power output data of all participants is a fundamental prerequisite for evaluating the athlete’s performance.Objective: This study investigates the influence of different power output parameters (absolute and relative peak power output) as well as body mass and height on the results in mixed-reality competitions.Methods: We scrape data from all six stages of the 2020 Virtual Tour de France of women and men and analyze it using regression analysis. Third-order polynomial regressions are performed as a cubic relationship between power output and competition result can be assumed.Results: Across all stages, relative power output over the entire distance explains most of the variance in the results, with maximum explanatory power between 77% and 98% for women and between 84% and 99% for men. Thus, power output is the most powerful predictor of success in mixed-reality sports. However, the identified performance-result gap reveals that other determinants have a subordinate role in success. Body mass and height can explain the results only in a few stages. The explanatory power of the determinants considered depends in particular on the stage profile and the progression of the race.Conclusion: By identifying this performance-result gap that needs to be addressed by considering additional factors like competition strategy or the specific use of equipment, important implications for the future of sports science and mixed-reality sports emerge. Mixed-reality sports platforms such as Zwift allow the athletic performance provided in the real world to be transferred into a virtual space so that the participating athletes can also physically interact and even compete with each other . Driven In mixed-reality cycling races, the speed of the avatar within the virtual world depends on the physical power generated (in watts), in addition to the athlete’s body mass and height . CyclingGiven the complex interplay of various factors that may be critical to success in cycling races, exercise physiology research can be divided into two domains. The first domain focuses on factors that influence the physical performance of athletes in endurance sports, which in cycling is measured as absolute power in watts or relative power in watts per kilogram body weight. In their review, In the second domain, sports physiology literature discusses numerous factors - like physical performance delivered by the athlete - that can predict the outcome of competitions or the sporting success of an athlete . Race suTo summarize the current state of research in the two domains, we can state that the factors determining an athlete’s physical performance are widely known, particularly in cycling. In contrast, the effect of the physical performance delivered in competition on the competition result requires further research. Studies show that several factors, such as high peak power or high power-to-weight ratio, can influence the result in cycling races. However, the physical performance was mostly determined in laboratory tests with a time lag before or after the competition e.g., . Thus, iIn mixed-reality competitions the performance data, body mass and body height of all participants are recorded and published. This happens for two reasons. First, without the disclosure of the recorded data, the athlete’s avatar would not move in the virtual world, and thus the competition would not be possible. Second, the performance data of all participants are publicly available during and after the race for all participants and spectators and are even integrated into the broadcasts to increase transparency about the race result. It was shown that the relative power output in mixed-reality races in which professional athletes participated is comparable to the relative performances in professional road races . Since mRegarding this research gap, the aim of this study is to analyze the effect of different performance parameters and the rider’s body mass and height on the results of mixed-reality competitions. In doing so, the following research question is addressed by analyzing in-competition data and results from the six stages of the women’s and men’s Virtual Tour de France: What influence do 1) absolute power over the entire distance, 2) relative power over the entire distance, 3) maximum relative power over 20 min, 4) maximum relative power over 5 min, 5) maximum relative power over 1 min, 6) maximum relative power over 30 s, 7) maximum relative power over 15 s, 8) body mass, and 9) body height of a rider have on the competition result (measured in riding time)?The following section describes how the performance data for the different Virtual Tour de France stages 2020 are collected and analyzed. Subsequently, the influence of the in-competition performance parameters and the rider’s body mass and height on the result is determined via polynomial regression analyses. The findings are discussed against the background of insights from sports physiology, and practical implications for the future of  sports are presented. Finally, the main insights are summarized in the conclusion.This study was approved by the Ethics Committee of the School of Business & Economics of the University of Münster, Germany. Since only publicly available data from the Zwift Power platform was used, no informed consent was required from the athletes.Given the novelty of mixed-reality sport for exercise physiology science, we first provide an overview of the four phases of the experimental design .Phase 1 marks the three weekends in July 2020 where the Virtual Tour de France took place. The performance data (absolute and relative power output) as well as body mass and height were integrated into the extensive broadcasting of the competitions and uploaded to the Zwift Power platform and are therefore publicly available, even after the end of the competitions. In phase 2, the athletes’ performance data, body mass and height, and the corresponding results of the six stages of the Virtual Tour de France for women and men were obtained using the web scraping technique from Zwift Power. In phase 3, the different performance data, body mass and height were plotted with the competition result (measured in riding time) to visualize the relationship between the determinants  and the competition result. In Phase 4, a cubic relationship was assumed based on the visualizations in phase 3. The influence of the determinants on the competition outcome was calculated using third order polynomial regressions to account for the cubic relationship. The following subsections are organized according to each of the four phases of the experimental design.In July 2020, the cycling platform Zwift and the Amaury Sport Organisation (ASO) cooperatively hosted the first “Virtual Tour de France” . In thisTo be able to analyze the influence of different performance indicators as well as body mass and height on the race results of the stages of the Virtual Tour de France, we scraped the performance data, body mass, body height, and the corresponding riding times from the platform Zwift Power . To do sThe winning time in the different stages of the men’s race ranged between 41 and 59 min. Accordingly, the length of the virtual stages corresponds more to that of a time trial, which in the Virtual Tour de France was 36.8 km long, and the winning time was 55 min . Women rIn road cycling, riders must overcome the forces of gravity, rolling resistance, and aerodynamic drag to move forward . In partUsing the example of the relationship between the relative power output and the competition result, the scatterplots of the respective stages show that the cubic graph fits better to the observed data points than the linear or the quadratic graph. Furthermore, comparable cubic patterns can be seen in particular for the other power output parameters (absolute and relative) and in an attenuated form for body mass and height.In order to address the research question raised, the influence of the relative and absolute power over the complete duration, the relative power in different time intervals, and the rider’s body mass and height on the competition result were analyzed by performing regression analysis. Initially, we selected the appropriate type of regression analysis. Performing multiple regressions based on multiple parameters is not feasible because the variables depend on each other (absolute vs. relative power output) or represent overlapping time intervals. We tested considering multiple parameters. Thereby, the variance inflation factor was above 10 for almost all variables for all stages for both women and men, which is strong evidence for multicollinearity and should therefore be avoided . For thiSince the scatterplots in Y-intercept of the regression surface, while b1, b2, and b3 are the estimated coefficients of the predictor’s respective polynomial , and e is the random error component.In While multicollinearity is addressed via the mean centering function prior to running the regression analyses, the other assumptions are tested during the regression analysis to ensure the validity of the results. With regard to the second assumption , the autocorrelation of the residuals is determined by performing the Durbin-Watson test . The tesAll regression analyses were performed on the basis of the data sets described in R2 indicates to what extent the variance of the dependent variable (competition result as riding time on the stage) can be explained by the independent variable (regressor). The p-value of the F-statistic indicates the extent to which the calculated R2 differs significantly from 0. In p > 0.05) is not reached, 2) the Durbin-Watson test shows values <1 or >3, which would indicate autocorrelation, or on the basis of the histograms respectively the scatterplots 3) heteroscedasticity or 4) no normal distribution of the residuals is observed. Furthermore, the strongest predictors (largest R2) for each women’s and men’s stage are highlighted in bold. All other regression results  also meet the validity criteria. Due to limited interpretability, standardized regression coefficients, associated p-values, regression functions, and confidence intervals are presented in The results of the third-order polynomial regression analyses for the women’s and men’s Virtual Tour de France stages are summarized in R2 = 0.765; p < 0.001). Relative power is less important the shorter the time interval considered. In men’s race, relative power output over the entire stage  is also the strongest predictor of success. The relative performances over time intervals of 15 s  and 30 s  also still show a high explanatory power.The stages of the Virtual Tour de France have varying characteristics. Stage one was held on a hilly course. 1.5 km before the finish, the last mountain classification had to be mastered, followed by a descent to the finish. In the women’s race, the results show that relative power output over the entire stage duration (W/kg) has the greatest influence on the race result  and over 20  and 5 min  are particularly decisive for the race outcome. This result indicates that it was important to get over the mountain in the first group, while in the final part of the race, sprinting skills were less critical as the peloton was split into small groups. The men’s race developed in a similar way. Here the relative performance over the entire distance  also explains most of the success.Stage two led over one high mountain (>500 m of elevation gain) in the middle of the stage and ended with a flat section. In the women’s race, the relative performances over the entire distance . In the men’s race, relative power over the entire duration  is also the strongest predictor.Stage three was an undulating course with a slightly hilly finish. In the women’s race, the strongest predictor is relative power over the entire distance  is the strongest predictor, while shorter time intervals of the relative peak power (<5 min) do not significantly affect the competition result. However, the progression in the men’s race was different. Here, a comparatively large group sprinted for the win, which is indicated by the fact that relative peak power over 30 s  is almost as critical for success as the strongest predictor relative peak power over 20 min . Notably, body mass  significantly affects competition results.Stage four led over two smaller and two medium-high mountains (>100 m of elevation gain), with the last medium-high mountain about 3 km from the finish. In the women’s race, relative performance over the entire distance  and men , relative power output over the entire distance is the strongest predictor of success due to the height of the mountain. For both genders, relative peak power over 1 minute still has a relatively high explanatory power . Overall, this is also reflected in the race progressions, which included a few accelerations on the uphill, but it was particularly important to keep relative peak power high to defend the advantage gained all the way to the finish. Remarkably, despite the mountainous course profile, body mass alone has no effect on race performance, neither in women (p = 0.512), nor in men (presence of autocorrelation due to Durbin-Watson statistic <1).The fifth stage, after a short flat section, led up to a mountain top finish, where a difference in altitude of over 1,000 m had to be overcome. For both women  is the strongest predictor of success and the explanatory power decrease constantly for shorter peak power intervals. In the men’s race, a bunch sprint occurred for the win. Here, relative power over the entire stage  is the strongest predictor for the competition results, while relative peak power interval of 1 minute  also has a notable impact.The sixth and final stage followed a slightly hilly route, with the last kilometers being flat. In the women’s race, relative peak power over the entire stage  ranges between 77% and 98% for women and between 84% and 99% for men depending on the stage. Accordingly, for some stages, other performance indicators (regressors) explain success almost as well as - or in individual cases even better than (see men’s stage 4) - relative power output over the entire stage, which will be addressed in the following discussion.Overall, it appears that relative power output over the entire stage can explain the highest proportion of the variance in the riding time, and thus success in the race, for both women and men. However, the explanatory power . For mountain stages, the explanatory power of relative power output over the entire distance is considerably higher than for absolute power output . Body mass alone is a significant predictor of athletic success only on the mountain stages . The effect of the progression of the race on the relevance of different performance parameters for the competition’s result can be seen in the fact that on identical courses in the women’s and men’s races, different performance parameters explained success substantially differently. For example, stage 1 of the women’s race was ridden offensively, and the winner was decided in the sprint of a leading group. This shows that the relative performance over the entire distance has the strongest explanatory power (R2w/kg = 0.765) and decreases with the observation of shorter time intervals , since it is initially critical for success to be in the leading group. Stage 1 of the men was decided in a mass sprint. Here, the relative power over the entire distance (R2w/kg = 0.945) also has the highest explanatory power, which initially also decreases with shorter time intervals of the relative peak power , but then increases again substantially for the shortest time intervals considered , which might represent the critical performance in the mass sprint. Body height cannot explain the success in any stage. This result is notable because participants are required to report their height and current body mass prior to the competition. Furthermore, the scatterplots and the polynomial regressions showed that in mixed-reality competitions, there is a cubic relationship between power output and speed (riding time over a defined distance). The cubic relationship is also present in the real world, where aerodynamic drag is especially important at high speeds, in addition to gravity and rolling resistance (We contribute to sports physiology science by indicating that the (relative) in-competition power output is the strongest predictor of success in mixed-reality competitions. The explanatory power of the examined power output, body mass, and body height depends on the course characteristics and the progression of the race. The role of course characteristics is indicated by the fact that for flat stages, the explanatory power of absolute and relative power output over the entire duration are comparable  are the main predictors of success in mixed-reality competitions. Nevertheless, success in any stage of the Virtual Tour de France could not be explained entirely by only one of the performance parameters, resulting in a performance-result gap, which varies in size between 1% and 23% , dependiBased on our findings, recommendations for action can be offered to different stakeholder groups in sports. First, our results provide athletes and coaches insights into which performance parameters are critical for success in mixed-reality sports. Accordingly, training programs can be tailored to prepare athletes to deliver the relevant critical performances. Since we provide extensive knowledge on the performance profile of mixed-reality competitions, they can also be systematically integrated into the preparation for real-world competitions to train in a competition-specific manner. This is especially beneficial when no competitions occur in the off-season, when factors such as unfavorable weather at outdoor competitions pose an increased risk of injury or infection.Our analysis offers coaches, sports directors, or team managers a solid basis for decision-making to deploy athletes in mixed-reality competitions according to the respective course characteristics. For example, performance data from training or performance diagnostics can be compared with the required performance profiles of mixed-reality competitions. This allows the selection of athletes whose performance best matches the required performance profile. Furthermore, due to the wide availability of performance data, mixed-reality competitions offer scouts the opportunity to track and recruit athletes based on their performance and body composition.For mixed-reality sports platform operators, it is valuable to show that physical performance is the most critical factor for success in the competitions on their platform. Accordingly, mixed-reality sports competitions host relevant and authentic athletic performances, which underlines the relevance of mixed-reality events. It is also notable that success is not solely determined by physical performance in mixed-reality competitions. This uncertainty about the outcome of the competition makes mixed-reality competitions interesting for spectators.Finally, we provide key insights for sports federations on how to deal with mixed-reality sports platforms. Mixed-reality-specific factors might explain the performance-result gap, which raises the question for sports federations as to the extent to which they want to establish mixed-reality competitions as an independent discipline. In this regard, federations need to decide whether the mixed-reality events should be as realistic as possible or whether they deliberately enhance the presence of gamified elements and mechanisms. While discipline-specific skills and strategies are relevant in traditional cycling disciplines such as road cycling, mountain-biking, cyclo-cross, or track racing, the same is true for mixed-reality cycling. Mixed-reality cycling has the potential to introduce people with an affinity for esports to traditional sports and thus represents a promising extension to appeal to a younger target group. At the same time, it will be interesting to observe whether professionals from road cycling will also participate in mixed-reality cycling competitions in the future, as in the Virtual There are potential limitations to this study. First, the stages of the Virtual Tour de France were conducted in a decentralized manner due to the Covid-19 pandemic, so the organizers could not monitor the calibration of the smart trainer or the body mass and height of the riders to the same extent as that would have been the case if the event had been conducted in a centralized manner. In their analysis of the Virtual Tour de France, Second, the ability to apply the insights gained here from mixed-reality cycling races to road races might be limited. First, the competition mode differs, as the Virtual Although essential aspects from the real world were simulated in the Virtual Tour de France such as elevation profiles and slipstream effect, there are still differences, such as power-ups or the non-inclusion of steering skills, as the avatar steers autonomously. Furthermore, some environmental factors, such as rain or crosswinds, which can affect the race result in road races, are not considered in mixed-reality races. Follow-up studies should aim to investigate the extent to which gamification elements such as power-ups are used strategically by riders and what effect the power-up use has on competition outcomes and thus explain the performance-result gap. In view of the increasing importance of mixed-reality sports, the results presented here nevertheless show a high relevance for sports physiology science see . The widThis study shows that success in mixed-reality sports particularly depends on athletes’ physical performance. Exemplified by the performance data, rider’s body mass and height as well as results of the Virtual Tour de France, we demonstrate that the relative power output over the entire stage distance explains most of the variance in the riding time. However, depending on the course characteristics and the race progression, the explanatory power of the considered performance indicators differs in some cases considerably. While relative peak performances over a long period are most critical for success in races with longer climbs, relative peak performances of around 1 minute are critical for success in flatter races ending in a bunch sprint. The relevance of the relative performance indicates that the body mass of the riders has some importance. However, body mass alone can predict race success only occasionally when a mountain lies just before the finish. There was no significant effect for body height on the race result observed.Overall, insights from sports science can be applied to train specific critical performances e.g., . Althoug


Ordered two-dimensional covalent organic frameworks (COFs) have generally been synthesized using reversible reactions. It has been difficult to synthesize a similar degree of ordered COFs using irreversible reactions. Developing COFs with a fused aromatic ring system via an irreversible reaction is highly desirable but has remained a significant challenge. Here we demonstrate a COF that can be synthesized from organic building blocks via irreversible condensation (aromatization). The as-synthesized robust fused aromatic COF (F-COF) exhibits high crystallinity. Its lattice structure is characterized by scanning tunneling microscopy and X-ray diffraction pattern. Because of its fused aromatic ring system, the F-COF structure possesses high physiochemical stability, due to the absence of hydrolysable weak covalent bonds. Two-dimensional covalent organic frameworks (2D COFs) are commonly synthesised through dynamic covalent chemistry, as it allows for thermodynamic ‘error correction' which enhances crystallinity. Here a crystalline 2D COF with amine and hydroxyl functional groups within the pores is synthesised through kinetically-controlled reactions. Unlike other more widely studied crystalline materials , the COFs have a unique aspect in that their skeletons and pores can be precisely engineered, like those of metal organic frameworks (MOFs)9. Two-dimensional (2D) COFs can be divergently expanded into a π-conjugated structure by periodically integrating the building blocks12. Because of their ordered nature and 2D topology, COFs are seen as a dynamic and robust platform for the design of advanced functional materials, including a wide range of semiconductors11, and proton conductors14 for gas adsorption17, catalysis21 and energy conversion and storage applications25. The ability to generate crystalline organic materials with precise control of the framework at a molecular-level has been one of the most important recent developments in chemistry and materials science9.Covalent organic frameworks (COFs) are a class of crystalline macromolecular structures whose periodically linked building units extend into a framework with uniform topology and pores26. However, this beneficial intrinsic reversibility limits the COFs practical applications, because it leads to physiochemical instability27. COFs with boroxine or boronate linkages are prone to amorphization or disintegration in water or protic solvents28.In order to achieve high crystallinity in COFs, current methods typically rely on unstable bond formation using a reversible reaction to link monomer units by thermodynamic equilibria29. Because of their reversible nature, however, under vigorous acidic conditions the chemical stability of most imine based COFs against hydrolysis is far from satisfactory30. To address the stability issue, and enable the fabrication of physiochemically robust COFs, a number of methodologies have been explored35. However, further exploration of new reticular chemistry is needed to synthesize stable COFs as reliable functional materials, and crucial to widen their practical applications.The prevailing class of COFs with relatively stable linkages, such as imine (-C=-) based COFs, exhibit enhanced hydrothermal stability36. In COFs, π–π interactions form porous layered frameworks37. However, typical COFs lack π–π interlayer interactions, and chemical stability, which limits their applications. Imine and boroxine based COFs, for example, are inferior when it comes to promoting π electron delocalization between the connecting units38. Despite a few examples of π-conjugated COFs, obtained using surface or interface-assisted synthesis40, the design and synthesis of fused aromatic ring-based π-conjugated COFs, which produces high crystallinity and structural stability in corrosive environments, remains a crucial challenge41.The intrinsically important feature of COFs is their ability to form covalently linked stable aromatic ring systems, which are the basis of their exceptional electronic and magnetic properties42.To tackle this issue, a physiochemically robust yet fully conjugated fused aromatic COFs is highly desired, particularly to address long-standing challenges in semiconductor technology2) and hydroxyl (−OH) functionalities in the pores. The fused aromatic COF allows inherently periodic ordering with extended π-electron delocalization and thus physiochemical stability. The process relies on the powerful polycondensation (aromatization) of pentaaminophenol (PAP) and hexaketocyclohexane (HKH) in trifluoromethanesulfonic acid (TFMSA) to yield fused aromatic phenazine-linked 2D COF .Here, we report a fully conjugated fused aromatic 2D COF with aromatic amine  confirmed the formation of structure and exhibited experimental values that were very close to the theoretical values , nitrogen (N 1s), and oxygen (O 1s)  and C–NH2  in the structure is assigned to residual C=O (531.49 eV) at the edges and the phenolic C–OH (532.96 eV) in the structure . The XPS survey spectra revealed the presence of only three peaks, related to carbon  spectroscopy was exploited to investigate the chemical structure of the F-COF. Two intense peaks centered at 138.50 and 176.40 ppm are, respectively, related to the carbon atoms attached to nitrogen and edge ketonic groups  spectroscopy, which revealed the aromatic C=N, C=C stretching vibration at 1627 cmθ) can be assigned to the (100) plane of a crystalline hexagonal arrangement. The relatively strong peak at 27.05° is assignable to the (001) plane, which is related to the interlayer π–π stacking. The relatively broad PXRD peaks of the F-COF are associated with the less-ordered edges and extremely large molecular size of the F-COF along with poor stacking due to irreversibility of the reaction48  analyses in combination with theoretical XRD simulations. The peak at 6.42°  using the geometrical energy minimization of the structure by a universal force field. The PXRD pattern of the F-COF coincided well with the AA-stacking model . These results demonstrate the hexagonal ordering of the extended 2D structure along the x and y directions with 3.30 Å layer to layer distance in the z-axis.The unit cell parameters and simulated PXRD patterns were acquired  study was performed to visualize the atomic structure of the F-COF. A single sheet of F-COF was prepared on a Cu(111) substrate under ultrahigh vacuum (UHV) by thermal evaporation at 600 K. The STM measurements were performed in UHV at a low temperature (77 K). Figure The electronic structure of the F-COF was examined with scanning tunneling spectroscopy (STS) using lock-in detection mode. Two broad peaks appeared at −1.41 and 0.59 eV in the valence band and conduction band zones, respectively Fig. . The dirAs the solution synthesized materials by polymerization have a wide range of molecular weight distribution from small to very large flakes sizes. It is almost impossible to synthesize macromolecular materials with the same molecular size and uniform molecular weight distribution. Thus, under UHV condition, it is possible to sublimate relatively smaller size flakes onto the cleaned Cu(111) substrate. From the STM we were able to observe different sizes of flake Fig.  on Cu11.Fig. 4STThe stability of the F-COF was investigated by soaking the sample for 24 h in concentrated hydrochloric acid (HCl) and 6 M sodium hydroxide (NaOH). The PXRD results clearly indicate stability of the structure, showing no changes in the peak position before and after acid and base treatment  in each hole. This suggests there is further modification potential  was charged in a three-necked round bottom flask under argon atmosphere and placed in a cold bath at −40 °C, and freshly distilled trifluoromethanesulfonic acid  was added. Then, HKH  was slowly added while stirring at −40 °C for 4 h. The reaction flask was slowly allowed to warm up to room temperature for 3 h. The ice bath was replaced with oil bath and heated to 175 °C for 8 h. Then, the flask was cooled to room temperature and poured into water. The solid product that precipitated was collected by suction filtration using a polytetrafluoroethylene  membrane. The resultant dark solid was further Soxhlet extracted with methanol and water, respectively, for 3 days each and freeze-dried at −120 °C under reduced pressure (0.05 mm Hg) for 3 days.+ sputtering and annealing. After obtaining a cleaned Cu(111) substrate, an F-COF monolayer was deposited on the precleaned Cu(111) substrate by in situ thermal evaporation under UHV condition. The temperature of the Cu(111) was kept at room temperature and the F-COF evaporation temperature was about 600 K.The STM experiments were performed in low-temperature HV at 77 K (SPECS JT-STM). The cleaned single crystal Cu(111) surface was prepared with a few cycles of ArAll the chemicals, reagents, and solvents were purchased from Aldrich Chemical Inc., unless otherwise stated. Solvents were degassed with nitrogen purging prior to use. All reactions were accomplished under nitrogen atmosphere using oven dried glassware.1H) and carbon thirteen (13C) nuclear magnetic resonance (NMR) spectra were recorded on an AVANCE III HD 400 MHz FT-NMR (Bruker) spectrometer for the monomer characterization. Solid-state NMR spectrum of F-COF was measured using powder sample on 600 MHz VARIAN FT-NMR (Agilent) at a spinning rate of 20 kHz. NMR spectra can be found in Supplementary Figs. λ = 1.5418 Å) Rigaku, Japan at 40 kV and 200 mA at room temperature. Scanning electron microscope (SEM) images were obtained with Pt- coated samples on carbon tape by a Field Emission Scanning Electron Microscope Nanonova 230 . HR-TEM images were taken by a JEM-2100F microscope  under an operating voltage of 200 keV. The samples were prepared by drop casting of dispersed ethanol on holey carbon TEM grid and dried in oven at 50 °C under vacuum. The TGA was carried using a Thermogravimetric Analyzer Q200  at a heating rate of 10o min−1 in nitrogen and dry air atmosphere. Fourier-transform infrared (FT-IR) spectra were conducted on a Spectrum 100  with KBr pellet. XRD simulation and Pawley refinement were carried out using Materials studio modeling V. 7.0 . Pawley refinement was carried out to optimize the lattice parameter iteratively. The pseudo-Voigt function was exploited for whole profile fitting and the Berrar–Baldinozzi function was employed for to correct asymmetry during the refinement processes, until the Rp and Rwp values converged.EA was performed with a Thermo Scientific Flash 2000 Analyzer. Proton (Supplementary InformationPeer Review File


This retrospective study aimed to find suitable source domain data in cross-domain transfer learning to extract robust image features and build a model to preoperatively distinguish LGN from LAC in SPSNs. The experiment showed that, compared with other source domains (such as ImageNet and LIDC), the transfer learning signature based on lung whole slide images as the source domain could extract more robust features (Wasserstein distance: 1.7108). Finally, a cross-domain transfer learning radiomics model combining transfer learning signatures based on lung whole slide images as the source domain, clinical factors and subjective CT findings was constructed. According to the validation cohort results of five centres (AUC range: 0.9074–0.9442), the cross-domain transfer learning radiomics model that combined multimodal data could assist physicians in preoperatively differentiating LGN from LAC in SPSNs.Purpose: This study aimed to find suitable source domain data in cross-domain transfer learning to extract robust image features. Then, a model was built to preoperatively distinguish lung granulomatous nodules (LGNs) from lung adenocarcinoma (LAC) in solitary pulmonary solid nodules (SPSNs). Methods: Data from 841 patients with SPSNs from five centres were collected retrospectively. First, adaptive cross-domain transfer learning was used to construct transfer learning signatures (TLS) under different source domain data and conduct a comparative analysis. The Wasserstein distance was used to assess the similarity between the source domain and target domain data in cross-domain transfer learning. Second, a cross-domain transfer learning radiomics model (TLRM) combining the best performing TLS, clinical factors and subjective CT findings was constructed. Finally, the performance of the model was validated through multicentre validation cohorts. Results: Relative to other source domain data, TLS based on lung whole slide images as source domain data (TLS-LW) had the best performance in all validation cohorts (AUC range: 0.8228–0.8984). Meanwhile, the Wasserstein distance of TLS-LW was 1.7108, which was minimal. Finally, TLS-LW, age, spiculated sign and lobulated shape were used to build the TLRM. In all validation cohorts, The AUC ranges were 0.9074–0.9442. Compared with other models, decision curve analysis and integrated discrimination improvement showed that TLRM had better performance. Conclusions: The TLRM could assist physicians in preoperatively differentiating LGN from LAC in SPSNs. Furthermore, compared with other images, cross-domain transfer learning can extract robust image features when using lung whole slide images as source domain data and has a better effect. The detection rate of solitary pulmonary solid nodules (SPSNs) has greatly improved with the popularization of CT . Lung adIn recent years, artificial intelligence (AI) techniques coupled with radiological imaging have played an essential role in automatically predicting the nature of tumours . MultivaIn contrast, advanced artificial intelligence models can overcome these problems through self-learning strategies, such as convolutional neural networks (CNNs). Deep learning features extracted using hierarchical convolution operations from the raw medical image will contain more abstract information about the lesion and may provide greater predictive insights . CNN modCNN based on transfer learning has been widely used because it does not require a precise delineation of lesions and can automatically extract features related to the target task in the case of small data ,17. TranHowever, the source domain data have a certain influence on the effect of cross-domain transfer learning. In particular, transfer learning based on fine-tuning is easily introduces redundant features when the source domain data are quite different from the target domain data, which leads to negative transfer and overfitting ,21,22. TTo eliminate the adverse effects of redundant features in the source network on the target model, an adaptive source domain feature selection network in cross-domain transfer learning was used to select the features of the source network that are conducive to the learning of the target network to constrain the training of the target model. Then, we investigated the impact of the cross-domain transfer learning signature (TLS) based on different source domain data  and CT images of the lung) on distinguishing LGN from LAC in SPSNs. In addition, a metric was introduced to measure the cross-domain transfer learning value of different source domain data to the target task. This technique was first applied to the preoperative differential diagnosis of LAC and LGN with SPSNs. Finally, a cross-domain transfer learning radiomics model (TLRM) combining TLS, clinical factors and subjective CT findings was constructed to assist clinicians in the preoperative diagnosis of LAC and LGN with SPSNs. Multicentre data were used for verification.This study was approved by the institutional review board. The need for informed consent was waived because this was a retrospective study using preexisting imaging data.The enrolled SPSN patients with complete medical information and CT images were collected from five medical centres from March 2013 to December 2020. The inclusion criteria were as follows: (1) radical surgical resected SPSNs with final histopathological diagnosis confirmed LAC and LGN; (2) the diameter of the SPSNs ≤ 30 mm; (3) primary thoracic CT images with slice thickness 0.625-3.0 mm in the axial section; and (4) interval between preoperative thoracic CT examination and operation of less than 1 month. The exclusion criteria were as follows: (1) calcified nodules or solid nodules with a satellited patchy opacity that represented chronic inflammatory disease; (2) subsolid nodules in the nodule attenuation subtype; (3) thoracic CT images with artifacts that did not meet the diagnostic requirements; and (4) patients with a malignant tumour history. The flowchart of participants is shown in Finally, 841 patients were included and divided into a training cohort that was used to train the model and four validation cohorts that were used to assess the performance of the models .https://portal.gdc.cancer.gov, accessed on 11 October 2020), ImageNet  and CT images of pulmonary nodules from LIDC-IDRI  were collected and separately used as source domain data for cross-domain transfer learning.In addition, to study the impact of source domain data on cross-domain transfer learning, the WSIs of lung cancer from the Cancer Genome Atlas , 64-detector-row CT scanner Aquilion One , and 64-detector-row CT scanner GE Discovery . The scanned direction was caudocranial with the patient in the supine position. The scanned filed was from the bilateral lung tip to base with deep inhalation breath holding. Scanned parameters: tube voltage, 120 kVp; automated mAs technique; collimation, 16 × 0.75 mm or 64 × 0.5 mm; pitch 0.875–1.5; and matrix, 512 × 512. Primary axial CT images were obtained in standard (B40f) and high resolution (B70f) algorithms with slice thicknesses of 0.625–3.0 mm and coronal and sagittal planner images with slice thickness of 3.0 mm were reconstructed in the postprocess workstation.Thoracic CT images were reviewed and recorded by two experienced radiologists from centre one (one with 12 years’ and another with 25 years’ experience in thoracic radiology); both were blinded to the medical information and pathological results. Agreement was reached through consultation when different opinions occurred. Thoracic CT images of each patient were reviewed in the radiologist workstation using a lung window ; level, −600 HU) and mediastinal window . Radiological CT manifestations were recorded according to the glossary of terms for thoracic imaging by the Fleischner Society were as follows: (1) location; (2) diameter; (3) regular margin (presence or absence); (4) lobulated shape (presence or absence); and (5) spiculated sign (presence or absence) ,25,26.An adaptive cross-domain transfer learning model was used to extract the robust transfer learning features of SPSNs from CT images. As shown in When the target network was well trained, the convolution kernels of the target network were used as feature extractors to extract transfer learning features from the CT images of SPSNs . FinallyFirst, the Mann-Whitney U test was used to select transfer learning features that were significantly different. Second, the sparse Bayesian extreme learning machine  was propTo comprehensively evaluate the TLS under different source domain data, four TLS were constructed and compared to each other. These TLS were based on lung cancer WSIs (TLS-LW), ImageNet (TLS-ImageNet) and LIDC-IDRI (TLS-LIDC) as source domain data. In addition, to evaluate the effect of cross-domain transfer learning, TLS under different source domain data were compared with a nontransfer learning signature (Non-TLS). For Non-TLS, we only trained the target network using CT images of the training cohort to extract features without pretraining.To comprehensively analyse patient information, the clinical factors (including gender and age), subjective CT findings  and the best performing TLS were combined to build the TLRM. First, the Cohen’s kappa test was used to analyse interreader agreements (Reader 1 and Reader 2) of subjective CT findings. Second, the Wilcoxon rank-sum test, Pearson’s chi-square test or Fisher’s exact test were performed to identify significantly different features. Third, the sparse Bayes-based least absolute shrinkage and selection operator  was usedTo comprehensively evaluate the TLRM under a multicentre scenario, we compared the TLRM with two other methods: (1) a clinical model combining clinical factors and subjective CT findings , and (2)To further validate the performance of the model, a prospective validation cohort of 99 cases from medical centre 1 between January 2021 and December 2021 was collected to evaluate the robustness of the model.The receiver operating characteristic curve, area under the curve (AUC), sensitivity, specificity, accuracy, positive probability value (PPV), and negative probability value (NPV) were calculated to evaluate the performance of the models. The DeLong test was used to evaluate significant differences between the AUCs of the models.p value < 0.05 was statistically significant.The Wasserstein distance between the source domain data and target domain data was calculated for each cross-domain transfer learning model to assess the similarity of the distribution between the two-domain data. The integrated discrimination improvement (IDI) was used to evaluate whether the new model could outperform the old model. Decision curve analysis was used to calculate the net benefit of the clinical utility of the model. All statistical analyses were two-tailed. A p < 0.05).The demographics and subjective CT findings of all cohort data are presented in p < 0.05; IDI = 0.0312 (p < 0.05), The model details of TLS-LW, TLS-ImageNet, TLS-LIDC and Non-TLS are shown in p values of the DeLong test were all less than 0.05, and the IDI values were 0.0162 and 0.0341, with p values were all less than 0.05  were fed into TLS-LW, and different responses were observed . The posFurthermore, the Wasserstein distance was used to assess the similarity between the source domain and target domain data. A smaller Wasserstein distance represented a more similar distribution between the two-domain data . The speThe results of the sparse Bayesian least absolute shrinkage and selection operator showed that age, lobulated shape, spiculated sign and TLS-LW were independent risk factors in distinguishing LGN and LAC lesions, and were used to develop the TLRM. The calculation formula of the risk prediction value based on the TLRM is shown in p value range: 0.7584–0.9391; The Hosmer-Lemeshow test yielded no significant difference between the predictive calibration curve and the ideal curve for risk status prediction in the four validation cohorts and whole validation data , and Delong test, p < 0.05; IDI = 0.0222 (p < 0.05) The diagnostic performances of the clinical model, TLS-LW, and TLRM are shown in Specifically, the false-positive rate of the clinical model was 28.40% (46/162) across whole validation data, while the false-positive rate of TLRM was only 17.90% (29/162), TLRM had the best effect.p > 0.05). Stratification analysis was performed based on patient characteristics (gender and age) and CT slice thickness to evaluate the robustness of the TLRM. The AUC of TLRM in the overall cohort (n = 841) was 0.9248 (95% CI: 0.9071–0.9424).Using stratified analysis, the TLRM performance was found to be unaffected by the gender, age and image slice thickness of patients  and men (n = 432). The AUCs were 0.9301 (95% CI: 0.9042–0.9560) and 0.9167 (95% CI: 0.8914–0.9419), respectively, and the p values were 0.7382 and 0.6075, respectively, when the two groups and the overall cohort were compared using the DeLong test. The ROC curves are shown in Part 1: Stratified analysis of gender. The patients were divided into two groups: women (n = 433) and age ≥ 60 years (n = 408), with respective AUCs of 0.9229 (95% CI: 0.8987–0.9471) and 0.9182 (95% CI: 0.8899–0.9466). The p values were 0.9047 and 0.7014, respectively, when the two groups and overall cohort were compared using the DeLong test. The ROC curves are shown in Part 2: Stratified analysis of age. The patients were divided into two groups: age < 60 years (n = 536) and 1.5 < thickness ≤ 3 mm (n = 305), with AUCs of 0.9308 (95% CI 0.9091–0.9525) and 0.9091 (95% CI: 0.8772–0.9409), respectively. The p values were 0.6736 and 0.3978, respectively, when the two groups and overall cohort were compared using the DeLong test. The ROC curves are shown in Part 3: Stratified analysis of CT slice thickness. The patients were divided into two groups: thickness ≤ 1.5 mm  are listed in n = 99), the AUC was 0.9076, which was better than the results of Non-TLS, TLS-LIDC and TLS-ImageNet  than those with LAC (mean age: 60.13 ± 10.08 years), similar to previous studies ,30. Prevn = 221 cases) and validation test (n = 91 cases) were 0.92 and 0.84, respectively. Zhou et al. [n = 220 cases) and validation test (n = 93 cases) were 1.00 and 0.99, respectively. Although combined radiomics and radiomics nomograms have shown good performance, their data are from a single centre and therefore cannot be used to evaluate model performance in the case of multicentre data. In addition, radiomics features, where predesigned features are extracted from a region of interest, lack the specificity and sensitivity required to differentiate LGN from LAC in patients with SPSNs.In recent years, some researchers have used artificial intelligence techniques coupled with radiological imaging to preoperatively differentiate LGN from LAC in SPSNs. Zhang et al.  selectedtest n = 1 cases wCNN has excellent feature learning ability that can have specific features for tasks according to learning and does not require time-consuming tumour boundary annotations. However, CNNs are prone to overfitting on small datasets. In this study, we developed a Non-TLS. Unfortunately, the Non-TLS had the problem of overfitting , perhaps because the training cohort data were relatively small when building the Non-TLS (3283 CT images).p < 0.05) and 0.0162 (p < 0.05) in the whole validation data, respectively. Interestingly, the Wasserstein distance of TLS-LW was minimal. In contrast, TLS-LIDC had the largest Wasserstein distance. These results indicate that for a small training dataset to truly take advantage of the transfer of learning the source domain data should be as similar as possible to the target domain data [p < 0.05) in whole validation data. The results showed that the transfer learning strategy helps to alleviate the overfitting problem of CNNs in the case of small samples.Therefore, this study combined a convolutional neural network with cross-domain transfer learning. A source domain feature selection network was proposed to adaptively select features that were beneficial to target task learning to constrain training of the target network. In addition, we developed three cross-domain transfer learning signatures based on different source domain data: TLS-LW, TLS-LIDC and TLS-ImageNet. The results showed that the performances of TLS-LIDC and TLS-ImageNet were lower than that of TLS-LW. Compared with TLS-LIDC and TLS-ImageNet, the IDI of TLS-LW was 0.0341 (ain data ,34. In aFinally, age, lobulated shape, spiculated sign and TLS-LW were independent risk factors for distinguishing LGN and LAC lesions, which were used to develop TLRM. The decision curve analysis and IDI also indicated that TLRM had better performance than TLS-LW and the clinical model. Therefore, the TLRM combined with multimodal data has better diagnostic performance than image-based TLS alone and a clinical model based on clinical factors and subjective CT findings.Despite encouraging results, the current study has several limitations. First, a selection bias may exist because of the retrospective nature of the study. Second, we only evaluated the difference between LAC and LGN, and other pathological types of lung nodules also need further investigation, such as lung squamous cell carcinoma, even metastatic lesions and other benign lesions . FurtherThe TLRM combined with multimodal data can assist physicians in preoperatively differentiating LGN and LAC presenting as SPSNs. We also found in this study that, compared with using other images as source domain data, cross-domain transfer learning has a better effect when using lung WSIs as source domain data.


Unsupervised domain adaptation, which aims to alleviate the domain shift between source domain and target domain, has attracted extensive research interest; however, this is unlikely in practical application scenarios, which may be due to privacy issues and intellectual rights. In this paper, we discuss a more challenging and practical source-free unsupervised domain adaptation, which needs to adapt the source domain model to the target domain without the aid of source domain data. We propose label consistent contrastive learning (LCCL), an adaptive contrastive learning framework for source-free unsupervised domain adaptation, which encourages target domain samples to learn class-level discriminative features. Considering that the data in the source domain are unavailable, we introduce the memory bank to store the samples with the same pseudo label output and the samples obtained by clustering, and the trusted historical samples are involved in contrastive learning. In addition, we demonstrate that LCCL is a general framework that can be applied to unsupervised domain adaptation. Extensive experiments on digit recognition and image classification benchmark datasets demonstrate the effectiveness of the proposed method. Deep neural network ,2,3,4 haIn the last decade, many scholars have conducted extensive research on domain adaptation, especially in the scene of unsupervised domain adaptation ,6. The mIn recent years, due to privacy issues and intellectual rights, the training data cannot be directly accessed. These existing unsupervised domain adaptation often requires source data, which may violate the policy of data privacy protection. In this paper, we discuss a practical and challenging source-free unsupervised domain adaptation, which uses the model trained in the source domain to adapt to the target domain. Specifically, only the model trained by the source domain and the unlabeled data of the target domain is provided. Our goal is to obtain knowledge from the source domain model and target domain data, so as to adapt the model to the target domain and obtain competitive performance.In source-free unsupervised domain adaptation, most methods are influenced by SHOT  and use In view of this, we introduce LCCL, a simple but effective contrastive learning framework in source-free unsupervised domain adaptive scenarios. Given the source domain model and target domain data, due to the discrepancy between domains, we use information maximization to alleviate domain differences. Owing to the lack of labels in the target domain, we use pseudo labeling technology to give pseudo labels, so as to promote the self-training process. In order to make full use of trusted pseudo labels, we select the features of samples with consistent network prediction and pseudo labels to store in the memory bank. We minimize the distance between samples and samples of the same class in the memory bank and maximize the distance between samples of different classes. Through this mechanism, we can fully explore the structural information of the target domain and better adapt the source domain model to the target domain.In brief, we highlight our three-fold contributions.An adaptive contrastive learning framework that works at the class level for source-free unsupervised domain adaptation is proposed.The proposed method introduces a memory bank that stores reliable samples with consistent labels and encourages samples in the target domain to learn discriminative features at the class level.Comprehensive experiments show that our method is competitive with existing methods in a series of source-free unsupervised domain adaptation scenarios.Unsupervised domain adaptation (UDA) has been widely studied in recent years. Most of the existing methods ,13,14,15Source-free unsupervised domain adaptation (SFUDA) aims to adapt the network to the target domain without the source domain data. There are two main methods at present, one is the pseudo labeling method represented by SHOT , and theContrastive learning (CL) is a self-supervised learning method, which helps the model learn the discriminative feature between samples. Generally speaking, it is to make the distance between similar samples smaller and the distance between different samples larger in the feature space. Recently, various works ,23 have Comparison with existing work. For the classical SFUDA, it is obvious that we differ from the existing work, because we propose a new framework to introduce contrastive learning into the scene of source-free methods. Compared with the existing contrastive learning  in the tProblem definition. In unsupervised domain adaptation, there are two domains with different distributions: source domain and target domain. Here, we consider a K-class classification task in which the source domain and the target domain share the same label space. In source-free unsupervised domain adaptation, the data in the source domain are invisible, and only the model trained in the source domain can be provided. Our goal is to train a network, which can be divided into feature extractor G and classifier C. For a sample x, the feature after passing through the feature extractor is Information maximization loss. In UDA, many classical methods try to align different domains through matching data distribution, which use maximum mean discrepancy [crepancy  or domaicrepancy . In SFUDPseudo labeling loss. Although maximizing the loss of information can achieve a more reliable prediction of the target domain, it will inevitably be affected by wrong pseudo tag matching. In order to solve this problem, a general method is to use pseudo tag technology for self-training and select more accurate pseudo labels to further promote the migration effect of the network. In fact, we learn from the idea of k-means. Specifically, we first calculate the centroid of each class by weighted k-means.a and b.Then we can give the sample pseudo label through the centroid of the nearest neighbor.The process of obtaining the centroid by clustering and re-assigning the pseudo label will last for multiple rounds. Finally, our pseudo label can be obtained through the final class centroid.Given the pseudo label, the loss function can be calculated by the standard cross-entropy loss.Label consistency contrastive learning loss. Due to the lack of source domain data and target domain labels, the proposed label consistency contrastive learning learns the distinguishability relationship with historical model samples from unlabeled target samples. The loss we use is the standard infoNCE loss, and the formula is defined as:In order to obtain a more reliable key set, so as to improve the performance of contrastive learning, under the influence of DTFLC , in eachOnly when the conditions of the formula are met, we add these samples to the memory bank to learn better feature representation.Overall, the total loss function can be formulated as follows:Algorithm 1 LCCL algorithm for SFUDA task.Input: source model Initialization: Freeze the final classifier layer fortodo  Obtain self-supervised pseudo labels via Equation for todo      # min-batch optimization    Sample a batch from target data and get the corresponding pseudo labels.    Update the parameters in Equation .    Select label consistency samples and add them into memory bank.end for  end forIn order to better understand our algorithm, we also list the flow of our algorithm in Algorithm 1.In order to prove the effectiveness of LCCL, we conducted experiments on the following popular visual benchmarks.VisDA-2017  is a larDigits is a benchmark dataset for domain adaptation that focuses on digit recognition. It contains three domains, each of which consists of 10 categories. The three domains are: SVHN (S); MNIST (M); USPS (U). Following DANN , We use Office-31 [Network architecture. We ensure that the Source-only model used is the same as SHOT [ as SHOT , which i as SHOT  for digi as SHOT  model foWe compared LCCL with three types of baseline methods: 1) Source-only: ResNet  Source-o, MCD 1616, ADR , CyCADA Network hyper-parameters. We implement our method under the PyTorch framework [ramework . The souResults. For data recognition, as shown in the Ablation experiment. In order to explore the impact of parts Parameter sensitivity analysis. As shown in the figure, we studied the sensitivity of our method to parameters Effect of memory bank size. We conducted experiments on the VISDA dataset to explore the impact of memory bank size on adaptation, and reported the results in the Beyond SFUAD. Our method can be used not only in source-free unsupervised domain adaptation, but also in traditional unsupervised domain adaptation. We add our method to the method DANN [hod DANN , and repConvergence analysis. In order to explore the convergence speed of network training and the influence of contrast learning on class aggregation. We show that the accuracy of the model and average distance between sample and centroid with the epoch of training time on the A→D task in office-31. From the Feature visualization. In order to more clearly show that our method can adapt to the target domain very well, we further use t-sne [se t-sne  technoloImpact of label consistency (LC). In order to explore the impact of label consistency on the contrastive learning module, we added all features to the memory bank during the training process, and the results are shown in the In this paper, we propose a simple yet effective framework LCCL to address a practical setting called source-free unsupervised domain adaptation. LCCL merely needs the well-trained source model and offers the feasibility of unsupervised DA without access to the source data, which may be private issues. Specifically, LCCL learns the target-specific model by exploiting the information maximization and pseudo labeling, and introduces a memory bank that stores reliable samples with consistent labels for encouraging learn discriminative features at the class level. Extensive experiments on multiple tasks verify that LCCL achieves competitive and even state-of-the-art performance.Future plan will address the limitations of the present work. The main limitation is that the proposed method is based on the contrastive learning. Due to the lack of source domain data and target domain labels, the proposed label consistency contrastive learning learns the distinguishability relationship with the historical model samples from the unmarked target samples. As a result, a memory bank is used to store historical sample feature, which increases the burden of memory to a certain extent. In the future, new source domain data and target domain labels will be collected using some specially designed experiments.