Direct measurement of the adhesion energy of monolayer graphene as-grown on metal substrates is important to better understand its bonding mechanism and control the mechanical release of the graphene from the substrates, but it has not been reported yet. We report the adhesion energy of large-area monolayer graphene synthesized on copper measured by double cantilever beam fracture mechanics testing. The adhesion energy of 0.72 ± 0.07 J m(-2) was found. Knowing the directly measured value, we further demonstrate the etching-free renewable transfer process of monolayer graphene that utilizes the repetition of the mechanical delamination followed by the regrowth of monolayer graphene on a copper substrate.

Insulating layers based on oxides and nitrides provide high capacitance, low leakage, high breakdown field and resistance to electrical stresses when used in electronic devices based on rigid substrates. However, their typically high process temperatures and brittleness make it difficult to achieve similar performance in flexible or organic electronics. Here, we show that poly(1,3,5-trimethyl-1,3,5-trivinyl cyclotrisiloxane) (pV3D3) prepared via a one-step, solvent-free technique called initiated chemical vapour deposition (iCVD) is a versatile polymeric insulating layer that meets a wide range of requirements for next-generation electronic devices. Highly uniform and pure ultrathin films of pV3D3 with excellent insulating properties, a large energy gap (>8 eV), tunnelling-limited leakage characteristics and resistance to a tensile strain of up to 4% are demonstrated. The low process temperature, surface-growth character, and solvent-free nature of the iCVD process enable pV3D3 to be grown conformally on plastic substrates to yield flexible field-effect transistors as well as on a variety of channel layers, including organics, oxides, and graphene.

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Purpose A blood transfusion after total knee arthroplasty (TKA) is associated with an increase in complication and infection rates. However, no studies have been conducted to predict transfusion after TKA using a machine learning algorithm. The purpose of this study was to identify informative preoperative variables to create a machine learning model, and to provide a web‐based transfusion risk‐assessment system for clinical use. Methods This study retrospectively reviewed 1686 patients who underwent TKA at our institution. Data for 43 preoperative variables, including medication history, laboratory values, and demographic characteristics, were collected. Variable selection was conducted using the recursive feature elimination algorithm. The transfusion group was defined as patients with haemoglobin (Hb) < 7 g/dL after TKA. A predictive model was developed using the gradient boosting machine, and the performance of the model was assessed by the area under the receiver operating characteristic curve (AUC). Data sets from an independent institution were tested with the model for external validation. Results Of the 1686 patients who underwent TKA, 108 (6.4%) were categorized into the transfusion group. Six preoperative variables were selected, including preoperative Hb, platelet count, type of surgery, tranexamic acid, age, and body weight. The predictive model demonstrated good predictive performance using the six variables [AUC 0.842; 95% confidence interval (CI) 0.820–0.856]. Performance was also good according to the external validation using 400 data from an independent institution (AUC 0.880; 95% CI 0.844–0.910). This web‐based blood transfusion risk‐assessment system can be accessed at http://safetka.net. Conclusions A web‐based predictive model for transfusion after TKA using a machine learning algorithm was developed using six preoperative variables. The model is simple, has been validated, showed good performance, and can be used before TKA to predict the risk of transfusion and guide appropriate precautions for high‐risk patients. Level of evidence Diagnostic level II.