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Condensation heat transfer has been widely studied for various applications such as power generation, water desalination, data centers, chemical and pharmaceutical syntheses, heating and air conditioning systems, and especially, the passive containment cooling system (PCCS) in nuclear power plants. The PCCS is designed to reduce the risk resulting from a loss of AC power or compounding human error. In a hypothetical accident situation, one of the main safety systems, the PCCS, condenses water vapor with gravity-driven force to remove the heat from the containment vessel. Although the condensation heat transfer in the presence of non-condensable gases for predicting the heat transfer performance of the PCCS has been successfully investigated, there is still no generalized model or correlation available. In this work, we focused on the development of universal models for predicting the heat transfer coefficients of free-fall condensation heat transfer on the external surfaces of vertical tubes in the presence of non-condensable gases. For this, we created a consolidated database covering a broad range of geometric values and operating conditions, including tube hydraulic diameter Dh = 10–41.2 mm, tube length L = 0.3–3.5 m, total pressure Ptot = 0.15–2.0 MPa, wall subcooling temperature ΔTsub = 5–71 K, average condensation heat transfer coefficients of 90 ≤ h ≤ 19,400 W/m2K, and Reynolds numbers of the film of 8 ≤ Refilm ≤ 7160. We analyzed the influence of varying the geometric and operational parameter values by using this database. Conventional machine learning techniques such as nonlinear regression and multilayer perceptron (MLP) neural network methods were adopted to predict the condensation heat transfer rate based on the consolidated database. Moreover, the prediction accuracies of the condensation heat transfer rates of the proposed prediction models and 12 relevant correlations were compared by using the consolidated database. The proposed nonlinear regression model exhibited good prediction accuracy with a mean absolute error (MAE) of 12.7 % for average h, which is much lower than those achieved by previously proposed relevant correlations. In addition, the MLP neural network model showed excellent prediction accuracy with an MAE of 4.2 % for the consolidated database.

While accuracy and precision of human serum glucose measurements play a crucial role in determining treatments targeting glycemic control, electrochemical glucose meters widely used for blood sugar monitoring, have been facing issues of inaccuracy, making their application in medical decision-making difficult. Herein, we report a new amperometric glucose biosensor with extreme precision based on a stainless steel 304 (SS304) needle modified with glucose oxidase (GOx), chitosan, and Nafion. GOx and chitosan are immobilized on the surface of the SS304 needle and then covered with the Nafion layer. We focused on reducing electrochemical background signals and securing high signal-to-noise ratios associated with the electrode material, in contrast to most previous studies focusing on detection sensitivity enhancement. The SS304 needle was employed as a sensing electrode with H2O2, a product of GOx-catalysed oxidation reaction of glucose, owing to its convenient electrical properties with extremely shallow non-faradaic background currents under electrochemical conditions. We confirmed that the SS304 needle immobilized with glucose oxidase shows a distinct increase in current upon the addition of glucose at −0.10 V (vs. a saturated calomel electrode). The amperometric signal from the modified SS304 was specific to the serum glucose level without notable interference. It exhibits a strict linear correlation in the diabetic sugar level of 1–20 mM (y = 0.205x, R2 = 1.000) with an outstanding reproducibility (RSD = 4.4–6.8 %) and long-term storage stability. These results demonstrated that the SS304 electrode possesses great potential for electroanalytical biosensors based on the H2O2 content.

Three-dimensional stacked electronics have substantially improved the electrical performance of integrated circuits. However, given the geometrical complexity and high pressure drop they entail, thermal management difficulties and energy requirements are exacerbated owing to the inapplicability of thermal management schemes. In this study, the thermal and hydrodynamic characteristics of various micro-pin fin arrays were investigated to maximize heat dissipation while minimizing the energy consumption. Specifically, a 10 × 10 mm2 micro-pin fin array was fabricated on an eight-inch silicon wafer via microfabrication. A Pyrex cover was bonded anodically with the top side of the micro-pin fins to prevent leakage, and a titanium/gold thin film serpentine heater was used to supply uniform heat flux on the backside of the micro-pin fin array. Subsequently, the heat transfer and pressure drop in the micro-pin fin heat sinks were obtained experimentally with various micro-pin fin geometries having pin diameter Df = 38–100 µm, transverse pin spacing ST = 74–301 µm, longitudinal pin spacing SL = 74–301 µm and pin height Hf = 90–207 µm. Thereafter, the geometrical and operational effects on heat transfer and pressure drop were investigated based on a consolidated database cumulated from the literature. Altogether, 256 data points from 21 geometrical combinations were explored from existing relevant studies to obtain optimized geometric and operating conditions in the micro-pin fin arrays over a wide range of geometrical and operating conditions: Reynolds number Re = 35–491.3, heat flux q" = 0–114 W/cm2, pin diameter Df = 38–559 µm, pin spacing S = 74–800 µm, and pin height Hf = 90–845 µm. Subsequently, new empirical correlations based on the consolidated database were formulated to describe the Nusselt number and fanning friction factor in the micro-pin fin arrays. These correlations provide suitable predictions in comparison with those based on extant correlations.