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Temperature is a fundamental thermodynamic property that can serve as a probe of biochemical reactions. Extracellular thermometry has previously been used to probe cancer metabolism and thermoregulation, with measured temperature changes of ~1-2 K in tissues, consistent with theoretical predictions. In contrast, previous intracellular thermometry studies remain disputed due to reports of >1 K intracellular temperature rises over 5 min or more that are inconsistent with theory. Thus, the origins of such anomalous temperature rises remain unclear. An improved quantitative understanding of intracellular thermometry is necessary to provide a clearer perspective for future measurements. Here, we develop a generalizable framework for modeling cellular heat diffusion over a range of subcellular-to-tissue length scales. Our model shows that local intracellular temperature changes reach measurable limits (>0.1 K) only when exogenously stimulated. On the other hand, extracellular temperatures can be measurable (>0.1 K) in tissues even from endogenous biochemical pathways. Using these insights, we provide a comprehensive approach to choosing an appropriate cellular thermometry technique by analyzing thermogenic reactions of different heat rates and time constants across length scales ranging from subcellular to tissues. Our work provides clarity on cellular heat diffusion modeling and on the required thermometry approach for probing thermogenic biochemical pathways.

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Magnetostrictive transducers are commonly used as actuators and sonar transducers, and in remote non-destructive evaluation. Their use in wireless thermometry is relatively unexplored. Since magnetostriction-based sensors are passive, they could potentially enable long-term near-field thermometry. While the temperature sensitivity of resonance frequency in magnetostrictive transducers has been reported in previous studies, the origin of the temperature sensitivity has, however, not been elucidated. Here, we identify material properties that determine temperature sensitivity and identify ways to improve sensitivity as well as the detection technique. Using a combination of analytical and computational methods, we systematically identify the material properties that directly influence the temperature coefficient of resonance frequency (TCF). We first experimentally measure the shift in resonance frequency due to temperature changes in a Metglas strip to be 0.03% K-1. Using insights from theory, we then experimentally demonstrate a fivefold improvement to the TCF by using Terfenol in place of Metglas as the magnetostrictive sensor material. We further demonstrate an alternate temperature sensing technique that does not require measuring the resonance frequency, consequently reducing instrument complexity. This work provides a general framework to analyze magnetostrictive materials and the sensing scheme for near-field wireless thermometry.

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Ultrasonic metal welding (UMW) is a solid-state joining technique with varied industrial applications. Despite of its numerous advantages, UMW has a relative narrow operating window and is sensitive to variations in process conditions. As such, it is imperative to quantitatively characterize the influence of welding parameters on the resulting joint quality. The quantification model can be subsequently used to optimize the parameters. Conventional response surface methodology (RSM) usually employs linear or polynomial models, which may not be able to capture the intricate, nonlin-ear input-output relationships in UMW. Furthermore, some UMW applications call for simultaneous optimization of multiple quality indices such as peel strength, shear strength, electrical conductivity, and thermal conductivity. To address these challenges, this paper develops a machine learning (ML)- based RSM to model the input-output relationships in UMW and jointly optimize two quality indices, namely, peel and shear strengths. The performance of various ML methods including spline regression, Gaussian process regression (GPR), support vector regression (SVR), and conventional polynomial re-gression models with different orders is compared. A case study using experimental data shows that GPR with radial basis function (RBF) kernel and SVR with RBF kernel achieve the best prediction accuracy. The obtained response surface models are then used to optimize a compound joint strength indicator that is defined as the average of normalized shear and peel strengths. In addition, the case study reveals different patterns in the response surfaces of shear and peel strengths, which has not been systematically studied in the literature. While developed for the UMW application, the method can be extended to other manufacturing processes.

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Heat conduction through bonded metal-polymer interfaces often limits the overall heat transfer in electronic packaging, batteries, and heat recovery systems. To design the thermal circuit in such systems, it is essential to measure the thermal interfacial resistance (TIR) across ∼1 µm to 100 µm junctions. Previously reported TIR of metal-polymer junctions utilize ASTM E1530-based two-block systems that measure the TIR by applying pressure across the interface through external heating and cooling blocks. Here, we report a novel modification of the ASTM-E1530 technique that employs integrated heaters and sensors to provide an intrinsic TIR measurement of an adhesively bonded metal-polymer junction. We design the measurement technique using finite element simulations to either passively suppress or actively compensate the lateral heat diffusion through the polymer, which can minimize the systematic error to ≲5%. Through proof-of-concept experiments, we report the TIR of metal-polymer interfaces made from DuPont's Pyralux double-side copper-clad laminates, commonly used in flexible printed circuit boards. Our TIR measurement errors are <10%. We highlight additional sources of errors due to non-idealities in the experiment and discuss possible ways to overcome them. Our measurement technique is also applicable to interfaces that are electrically insulating such as adhesively joined metal-metal junctions and sputter-coated or welded metal-polymer junctions. Overall, the technique is capable of measuring TIR ≳10-5 m2 KW-1 in bonded metal-polymer foils and can be tailored for in situ measurements in flexible electronics, circuit packaging, and other hybrid metal-polymer systems.

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Scale formation presents an enormous cost to the global economy. Classical nucleation theory dictates that to reduce the heterogeneous nucleation of scale, the surface should have low surface energy and be as smooth as possible. Past approaches have focused on lowering surface energy via the use of hydrophobic coatings and have created atomically smooth interfaces to eliminate nucleation sites, or both, via the infusion of low-surface-energy lubricants into rough superhydrophobic substrates. Although lubricant-based surfaces are promising candidates for antiscaling, lubricant drainage inhibits their utilization. Here, we develop methodologies to deposit slippery omniphobic covalently attached liquids (SOCAL) on arbitrary substrates. Similar to lubricant-based surfaces, SOCAL has ultralow roughness and surface energy, enabling low nucleation rates and eliminating the need to replenish the lubricant. To enable SOCAL coating on metals, we investigated the surface chemistry required to ensure high-quality functionalization as measured by ultralow contact angle hysteresis (<3°). Using a multilayer deposition approach, we first electrophoretically deposit (EPD) silicon dioxide (SiO2) as an intermediate layer between the metallic substrate and SOCAL. The necessity of EPD SiO2 is to smooth (<10 nm roughness) as well as to enable the proper surface chemistry for SOCAL bonding. To characterize antiscaling performance, we utilized calcium sulfate (CaSO4) scale tests, showing a 20× reduction in scale deposition rate than untreated metallic substrates. Descaling tests revealed that SOCAL dramatically decreases scale adhesion, resulting in rapid removal of scale buildup. Our work not only demonstrates a robust methodology for depositing antiscaling SOCAL coatings on metals but also develops design guidelines for the creation of antifouling coatings for alternate applications such as biofouling and high-temperature coking.

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Non-shivering thermogenesis through mitochondrial proton uncoupling is one of the dominant thermoregulatory mechanisms crucial for normal cellular functions. The metabolic pathway for intracellular temperature rise has widely been considered as steady-state substrate oxidation. Here, we show that a transient proton motive force (pmf) dissipation is more dominant than steady-state substrate oxidation in stimulated thermogenesis. Using transient intracellular thermometry during stimulated proton uncoupling in neurons of Aplysia californica, we observe temperature spikes of ~7.5 K that decay over two time scales: a rapid decay of ~4.8 K over ~1 s followed by a slower decay over ~17 s. The rapid decay correlates well in time with transient electrical heating from proton transport across the mitochondrial inner membrane. Beyond ~33 s, we do not observe any heating from intracellular sources, including substrate oxidation and pmf dissipation. Our measurements demonstrate the utility of transient thermometry in better understanding the thermochemistry of mitochondrial metabolism.

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