Thermally conductive but electrically insulating polymer composites are highly desirable for thermal management applications because of their wide range of utilization, ease of processing, and low cost. However, the traditional approaches to thermally conductive polymer composites usually suffer from the low thermal conductivity enhancement and/or the deterioration of electrical insulating property. In this study, using cellulose nanofiber-supported 3D interconnected boron nitride nanosheet (3D–C–BNNS) aerogels, a novel method for highly thermally conductive but electrically insulating epoxy nanocomposites is reported. The nanocomposites exhibit thermal conductivity enhancement of about 1400% at a low BNNS loading of 9.6 vol%. In addition, the epoxy nanocomposites are still highly insulating, having a volume electrical resistivity of 1015 Ω cm. The strong potential application for thermal management has been demonstrated by the surface temperature variations of the nanocomposites with time during heating and cooling.

Cellulose Nanofiber Supported 3D Interconnected BN Nanosheets for Epoxy Nanocomposites with Ultrahigh Thermal Management Capability

Polymers are widely used in industry and in our daily life because of their diverse functionality, light weight, low cost and excellent chemical stability. However, on some applications such as heat exchangers and electronic packaging, the low thermal conductivity of polymers is one of the major technological barriers. Enhancing the thermal conductivity of polymers is important for these applications and has become a very active research topic over the past two decades. In this review article, we aim to: 1). systematically summarize the molecular level understanding on the thermal transport mechanisms in polymers in terms of polymer morphology, chain structure and inter-chain coupling; 2). highlight the rationales in the recent efforts in enhancing the thermal conductivity of nanostructured polymers and polymer nanocomposites. Finally, we outline the main advances, challenges and outlooks for highly thermal-conductive polymer and polymer nanocomposites.

Thermal conductivity of polymers and polymer nanocomposites

Thermally conductive yet electrically insulating polymer composites are urgently required for thermal management applications of modern electrical systems and electronic devices because of their multifunctionality and ease of processing. However, the thermal conductivity enhancement of polymer composites is usually at the price of the loss of lightweight, the deterioration of flexibility, and electrical insulation. Here we report advanced polymer nanocomposites containing orientated boron nitride nanosheets (BNNSs), which simultaneously exhibit high thermal conductivity enhancement, excellent electrical insulation, and outstanding flexibility. These nanocomposite films can be easily constructed by electrospinning polymer/BNNSs nanocomposite fibers, vertically folding the electrospun nanocomposite fibers and the subsequent pressing. The nanocomposite films exhibit thickness-dependent in-plane thermal conductivity, which can reach 16.3 W/(m·K) in the 18 μm thick nanocomposite film with 33 wt % BNNSs. In add...

Highly Thermally Conductive Yet Electrically Insulating Polymer/Boron Nitride Nanosheets Nanocomposite Films for Improved Thermal Management Capability.

PDMS with designer functionalities—Properties, modifications strategies, and applications

With the current development of modern electronics toward miniaturization, high-degree integration and multifunctionalization, considerable heat is accumulated, which results in the thermal failure or even explosion of modern electronics. The thermal conductivity of materials has thus attracted much attention in modern electronics. Although polymer composites with enhanced thermal conductivity are expected to address this issue, achieving higher thermal conductivity (above 10 W m–1 K–1) at filler loadings below 50.0 wt % remains challenging. Here, we report a nanocomposite consisting of boron nitride nanotubes and cellulose nanofibers that exhibits high thermal conductivity (21.39 W m–1 K–1) at 25.0 wt % boron nitride nanotubes. Such high thermal conductivity is attributed to the high intrinsic thermal conductivity of boron nitride nanotubes and cellulose nanofibers, the one-dimensional structure of boron nitride nanotubes, and the reduced interfacial thermal resistance due to the strong interaction betwe...

A Combination of Boron Nitride Nanotubes and Cellulose Nanofibers for the Preparation of a Nanocomposite with High Thermal Conductivity

Thermal Conductivity of Polymer-Based Composites: Fundamentals and Applications

Objective We investigated the effect of dexmedetomidine anesthesia on postoperative cognitive function after pulmonary surgery. Methods A blinded, prospective, randomized, placebo-controlled study was performed on 60 patients (age range 65–74 years) undergoing lobectomy by video-assisted thoracoscopic surgery (29 in the dexmedetomidine group; 31 in the placebo group). Dexmedetomidine group patients received dexmedetomidine, and placebo group patients received an equal volume of physiological saline 20 minutes before anesthesia induction. Cognitive function was evaluated using the Montreal Cognitive Assessment 1 day before surgery and on postoperative day (POD)1, POD3, and POD7. The regional cerebral oxygen saturation (rSO2) was monitored continuously by near-infrared spectroscopy before anesthesia. Results The Montreal Cognitive Assessment score was significantly different between the two groups on POD1 (dexmedetomidine 26.4  ±  0.73 vs. placebo 25.5 ± 0.96) and POD3 (dexmedetomidine 27.1 ± 0.79 vs. placebo 26.6 ± 0.80). Specifically, attention and orientation scores were increased in the dexmedetomidine group on POD1 and POD3. The rSO2 was not significantly different between the dexmedetomidine and placebo groups. Conclusion Dexmedetomidine given before induction of anesthesia could reduce the risk of postoperative cognitive dysfunction and might not decrease rSO2. Hence, dexmedetomidine could be employed in pulmonary surgical procedures, especially for older patients with a high risk of cognitive dysfunction.

Effect of dexmedetomidine on the Montreal Cognitive Assessment in older patients undergoing pulmonary surgery

Abstract Background To investigate the risk factors for delayed neurocognitive recovery in elderly patients undergoing thoracic surgery. Methods A total of 215 elderly patients who underwent thoracic surgery between May 2022 and October 2022 were recruited in this prospective observational study. Cognitive function was tested by MoCA tests that were performed by the same trained physician before surgery, on postoperative day 4 (POD4), and on postoperative day 30 (POD30). Univariate and multivariate logistic regression models were used to analyze the risk factors for DNR. Results A total of 154 patients (55.8% men) with an average age of 67.99 ± 3.88 years were finally included. Patients had an average preoperative MoCA score of 24.68 ± 2.75. On the 30th day after surgery, 26 (16.88%) patients had delayed postoperative cognitive recovery, and 128 (83.12%) had postoperative cognitive function recovery. Diabetes mellitus (OR = 6.508 [2.049–20.664], P = 0.001), perioperative inadvertent hypothermia (&lt; 35℃) (OR = 5.688 [1.693–19.109], P = 0.005), history of cerebrovascular events (OR = 10.211 [2.842–36.688], P &lt; 0.001), and VICA (sevoflurane combined with propofol anesthesia) (OR = 5.306 [1.272–22.138], P = 0.022) resulted as independent risk factors of delayed neurocognitive recovery. On the POD4, DNR was found in 61 cases (39.6%), and age ≥ 70 years (OR = 2.311 [1.096–4.876], P = 0.028) and preoperative NLR ≥ 2.5 (OR = 0.428 [0.188–0.975], P = 0.043) were identified as independent risk factors. Conclusions The risk factors for delayed neurocognitive recovery in elderly patients undergoing thoracic surgery include diabetes, perioperative inadvertent hypothermia (&lt; 35℃), VICA (sevoflurane combined with propofol anesthesia), and history of cerebrovascular events. 

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Risk factors for delayed neurocognitive recovery in elderly patients undergoing thoracic surgery

Due to the excellent wind power probabilistic prediction performance, Mixture Density Network (MDN) is used in short-term wind power forecasting, but the density leakage problem the Not a Number (NaN) loss problem and the choice of hyperparameters in the MDN seriously affect the model performance. GA-TDMDN is proposed in this paper for wind power probabilistic forecasting. GA-TDMDN uses truncated distribution as kernel function to solve density leakage. For the NaN loss problem that occurs during model training, different output layer activation methods and improved loss function are used for different mixture component parameters, so that the shape of the truncated normal distribution can be better controlled. Genetic Algorithms (GA) is used to optimize key hyperparameters in the MDN structure. The experimental results show that it is feasible to use truncated distribution to solve the density leakage problem, and using the GA algorithm to optimize the model structure can improve the model performance

Bin Chen

Papers

Thermal Conductivity of Polymer-Based Composites: Fundamentals and Applications

Effect of dexmedetomidine on the Montreal Cognitive Assessment in older patients undergoing pulmonary surgery

Risk factors for delayed neurocognitive recovery in elderly patients undergoing thoracic surgery

Mixture Density Network Based on Truncated Distribution and Genetic Algorithm For Wind Power Forecasting