Deep Temperature Research Articles

Vitreous Tissue Cryopreservation Using a Blood Vessel Model and Cryomacroscopy for Scale-Up Studies: Observations and Mathematical Modeling

Successful long term cryobanking of multicellular tissues and organs at deep subzero temperatures calls for the avoidance of ice cryoinjury by reliance upon ice-free cryopreservation techniques. However, the quality of the cryopreserved material is the direct result of its ability to survive a host of harmful mechanisms, chief among which is overcoming the trifecta effects of ice crystallization, toxicity, and mechanical stress. This study aims at exploring improved conditions to scale-up ice-free cryopreservation by combining DP6 as a base cryoprotective agent (CPA) solution with an array of synthetic ice modulators (SIMs). This study is conducted by integrating cryomacroscopy techniques, thermal modeling, solid mechanics analysis, and viability and contractility investigation to correlate physical effects, thermal outcomes, and cryobiology results. As an extension of previous work, this study aims at scale-up of established baseline blood vessel models, while comparing the relative toxicity and vitreous stability of 4ml and 10ml samples of DP6 containing either sucrose as a SIM, or the commercial synthetic ice blockers (X1000 and Z1000). Using that established protocol, the addition and removal of DP6+0.6M sucrose and DP6+1%X1000+1%Z1000 were both well tolerated in rabbit carotid and pig femoral artery models, when assessed for metabolic recovery and contractility. Using cryomacroscopy, it was demonstrated that DP6+0.6M sucrose provided a stable vitrification medium under marginal cooling and warming conditions that resulted in >50% survival rate. By contrast, DP6+1%X1000+1%Z1000 was subject to visible ice formation during cooling under the same thermal conditions, resulting in a significantly lower recovery of ∼20%. Thermal modeling is used in this study to verify the actual cooling and rewarming rates in the specimens, while thermo-mechanics analysis is used to explain why fractures were observed using cryomacroscopy when the specimens were contained in glass vials but not in plastic vials.

Wideband CPW fed four port MIMO antenna based microwave sensor for deep brain temperature measurement

This study presents a wideband CPW fed four-port multi-input multi-output (MIMO) antenna-based microwave sensor for measuring deep brain temperature inside human head. The range of frequency is from 5.45 to 8.56 GHz at the resonance frequency of 7.25 GHz. Coplanar Waveguide (CPW) fed approach on a low-cost FR4 substrate is used in designing the planar antenna. The presented antenna is compact, having overall dimensions of 40 × 40 × 1.6 mm3. In the operational frequency range, the obtained diversity performance for MIMO antenna shows Envelope Correlation Coefficient (ECC) < 0.05, Diversity Gain > 9.95, total active reflection coefficient (TARC ) < −13 dB and channel capacity loss (CCL) < 0.125 bits/S/Hz. For deep brain temperature measurements, the proposed sensor provides electric field of 3.92 V m−1 and specific absorption rate (SAR) value of 0.16 W kg−1 at the 9 mm distance inside the human head Phantom. The proposed sensor is capable of measuring 0.1 °C change in temperature at 10 W of input power for 60 s when all four ports are active.

Glacial–interglacial Circumpolar Deep Water temperatures during the last 800 000 years: estimates from a synthesis of bottom water temperature reconstructions

Glacial–interglacial Circumpolar Deep Water temperatures during the last 800 000 years: estimates from a synthesis of bottom water temperature reconstructions

Au-induced crystallization of amorphous Ge thin films: An indication of the existence of a recrystallization process during growth at deep subeutectic temperature

The influence of Au catalyst microstructure on the crystallization behavior of Ge thin films obtained by Au-induced crystallization method is studied. By improving the Au catalyst crystallinity by pre-annealing, the hole mobility of Ge thin film crystallized at 200°C is improved to nearly 150 cm2/Vs thanks to the increase in Ge grain size, which is fairly high for a Ge film as thin as 10 nm. It is found that the increase in Ge grain size originates from the recrystallization process at deep subeutectic temperature. This is attributed to the existence of a metastable AuGe alloy phase surrounding crystalline Ge, which is kinetically stabilized by a continuous flow of Ge atoms through the alloy phase and realizes a solution-like growth. The process using a kinetically stabilized metastable phase will open up the possibility to realize a novel cost-effective and low-temperature process to fabricate high-performance semiconductor devices on plastic substrates.

Deep learning-based temperature prediction during rotary ultrasonic bone drilling

Bone drilling is a common but critical medical procedure in orthopedic surgeries used to treat fractured bones. During this procedure, the temperature of the bone increases due to generation of frictional energy. Temperature control has been a major challenge in bone drilling since its foundation. If this temperature increases over 47°C for 1 min, then it can result in permanent bone damage. To control the temperature elevation this study proposes a deep learning-based robust predictive model which has been trained and tested on data from pig bones. Excessive in-house testing has been done on pig femur bones to gather data and verify the results. Rotary ultrasonic bone drilling was the machining process used for drilling. Four independent variables which were rotational speed, feed rate, abrasive grit size, and vibrational ultrasonic power were varied and the temperature for each set of values was recorded. Multiple deep learning models were made and were compared on different error metrics. It was found that convolutional neural network 1D gave the least error over other models. The error generated by deep learning models was less than mathematical and experimental models.

Harsh Environment-Tolerant, High Performance Soft Pressure Sensors Enabled by Fiber-Segment Structure and Plasma Treatment.

As the demand for specialized and diversified pressure sensors continues to increase, excellent performance and multi-applicability have become necessary for pressure sensors. Currently, flexible pressure sensors are primarily utilized in fields such as health monitoring and human-computer interaction. However, numerous complex extreme environments in reality, including deep sea, corrosive conditions, extreme cold, and high temperatures, urgently require the services of flexible devices. Here, a piezoresistive flexible pressure sensor based on expanded polytetrafluoroethylene/functionalized carbon nanotubes (EPTFE/FCNT) is proposed. Benefiting from the unique fiber-segment architecture, chemical stability, and strong chemical binding force between EPTFE and FCNT, the fabricated sensor exhibits remarkable sensing capabilities and can be employed in multifarious extreme environments. It demonstrates a sensitivity of 862.28kPa-1, a response time of 6-7ms, and a detection limit below 1Pa. Furthermore, it possesses a pressure resolution of 0.0018% under 111kPa and can withstand over 10,000 loading and unloading cycles under 1MPa. Additionally, the EPTFE/FCNT sensor retains its outstanding pressure response and work efficiency in extreme conditions such as an ultra-low temperature of -80°C, high temperature (200°C), acidic and alkaline corrosion, and underwater. These notable attributes enormously broaden the sensors' real-world application range.

Identifying the genesis of hydrothermal activities in the Xiangcheng fault belt, southwestern China: Evidence from hydrochemistry and stable isotopes

Regional faults are beneficial structures for the formation of hydrothermal activities and have thus become target areas for geothermal resource utilization. Numerous hydrothermal activities have been reported along the Xiangcheng fault belt, particularly concentrating in the Batang, Xiangcheng and Shangri-La areas. In this study, hydrochemistry and stable isotopes were used to identify the genesis of hydrothermal activities in the Xiangcheng fault belt. The pH values of the geothermal water in these regions gradually decreases in this order, while the total dissolved solids gradually increase. The δD and δ18O values indicate the geothermal waters are mainly originated from snowmelt water and meteoric water. The recharge elevation of geothermal waters in Batang, Xiangcheng, and Shangri-La was 4415–4904 m, 4585–5038 m, and 3673–3969 m, respectively. Most geothermal waters belong to the hydrochemical type HCO3-Na, however some Batang geothermal water is of the SO4·HCO3-Na type, influenced by deep geothermal gas, and some Shangri-La geothermal water is of HCO3-Ca·Na type, influenced by shallow cold water and dissolution of carbonate rocks. Correlations of major ions suggest that HCO3-Na type geothermal waters are determined by the dissolution of paragonite, K-feldspar and albite as well as positive ion exchange. According to Na-K-Mg triangle diagram and mineral saturation indices, the geothermal waters do not reach full equilibrium and are mixed with shallow cold. Geothermometers, including cationic and SiO2, and geochemical thermodynamic calculations indicate that the deep and shallow reservoir temperatures are 200–240 °C and 169–193 °C for the Batang area, 194–201 °C and 119–131 °C for the Xiangcheng area, and 156–178 °C and 100–109 °C for the Shangri-La area. Conceptual models of the genesis of hydrothermal activities in the Batang, Xiangcheng, and Shangri-La areas were constructed, respectively. CaCO3 scaling is dominated in the study area. The hydrothermal activities of Batang and Xiangcheng areas with enriched deep materials and high reservoir temperatures are beneficial for rare-alkali metal (e.g., Li). The findings of this study provide a scientific basis for the development and utilization of geothermal resources in the high-temperature hydrothermal activity areas of western Sichuan.

Sustained acoustic medicine treatment of discogenic chronic low back pain: A randomized, multisite, double-blind, placebo-controlled trial.

Sustained acoustic medicine (SAM) is a noninvasive long-term treatment that provides essential mechanical and thermal stimulus to accelerate soft tissue healing, alleviate pain, and improve physical activity. SAM increases localized deep tissue temperature, blood flow, cellular proliferation, migration, and nutrition exchange, resulting in reduced inflammation and an increased rate of tissue regeneration. To assess the efficacy of SAM treatment of discogenic back pain in the lower spinal column to reduce pain, improve quality of life, and lower pharmacotherapy use. Sixty-five subjects with chronic low back pain were randomly assigned to SAM (N= 33) or placebo (N= 32) groups. Subjects self-applied SAM device bilaterality on the lower lumbar region for 4 hours daily for 8 weeks and completed daily pain diaries before, during, and after treatment. Subjects recorded pain reduction using a numeric rating scale (NRS), medication use, and physical activity using the Global Rating of Change (GROC) and Oswestry Disability Index (ODI). SAM treatment significantly reduced chronic lower back pain from baseline relative to placebo treatment (p< 0.0001). SAM treated subjects reported significantly lower back pain at 4 weeks, with the highest pain reduction (-2.58 points NRS, p< 0.0001) reported at 8 weeks. Similar trends were observed in improved physical activity (3.48 GROC, p< 0.0001, 69-88% ODI, p< 0.0001) and 22.5% (15.2 morphine milligram equivalent) reduction in the use of opioid medication from baseline to 8 weeks. Daily, home-use SAM treatment significantly improves the clinical symptoms of chronic lower back pain, improves physical mobility, and reduces daily medication use. SAM treatment is well-tolerated by patients and may be considered a safe, non-invasive treatment option for chronic discogenic, lower back pain.

A review of benthic foraminiferal oxygen and carbon isotopes

Stable isotopes in the calcium carbonate of benthic foraminifera provide important paleoenvironmental information about seawater/sedimentary porewaters that these foraminifera live in. Oxygen isotopes provide essential insights about variations in deep water temperatures and sea-level/ice volume changes, while carbon isotopes provide information about sea-water carbon/nutrient cycling. In this review we look into detail at the direct and indirect mechanisms that contribute to stable oxygen and carbon isotope signals in Rotaliid benthic foraminifera. This includes effects from ontogenetic- and calcification mechanisms, and the impact of methane seeps and post-depositional diagenesis. We conclude our review with an overview of current challenges and provide recommendations for future research endeavors aimed at outstanding knowledge gaps in understanding how these biomineralizers control their stable isotopes.

Optimization and evaluation of corrosion inhibitors for N80 steel in sulfamic acid solutions

Sulfamic acid (SA) has a low acid-rock reaction rate and can be used for deep high-temperature acidizing. However, the inhibition effect of existing corrosion inhibitors at deep high temperatures is greatly reduced. Through experiments, it is found that corrosion inhibitors such as imidazoline (OAI), cinnamaldehyde (CA), C14 quaternary ammonium salt (C14-GN), and potassium iodide (KI) have good corrosion inhibition effect, the average corrosion inhibition rate can reach 99 %, and the corrosion rate is less than 2 g/m2 h. The experimental results show that the film formation of a single inhibitor is uneven and the pitting corrosion is serious. In contrast, the mixed-use of many kinds of inhibitors can effectively inhibit the pitting corrosion, and the corrosion rate is less than 1.6 g/m2 h. And it can effectively inhibit pitting corrosion. At 363 K, the corrosion inhibition rate is 99.9 % and the corrosion rate is 1.1 g/m2·h. Through the orthogonal test, it is concluded that the best inhibitor ratio is OAI: CA: C14-GN: KI = 3:3:3:2. The optimized inhibitor has the best synergistic effect, can play an important role at high temperature, make the film uniform and dense, and improve the corrosion inhibition effect of the solution of sulfamic acid with the investigated inhibitor.

Modification of Li3PO4 layer effectively boosting lithium storage and thermal safety performance for LiCoO2 batteries

To meet the demand for high-performance LiCoO2 batteries, it is necessary to overcome challenges such as interface degradation and rapid capacity degradation caused by changes in bulk structure, especially under deep delithiation and high temperature conditions. The ion conductive coating layer of Li3PO4 has been directly modified on the surface of LiCoO2 particles using magnetron sputtering method, significantly improving the lithium storage performance of LiCoO2@Li3PO4 composites. Compared to pure LiCoO2, the modified LiCoO2 sample exhibits obviously better cycle life and high-temperature performance. Especially, under the conditions of 2 and 1 C, the LiCoO2@Li3PO4 electrode delivers excellent cycling performance at high voltage of 4.5 V, with capacity retention rates of 89.7% and 75.7% at room temperature and high temperature of 45 °C, being far greater than those of 12.3% and 29.1% for bare LiCoO2 electrodes. It is discovered that the Li3PO4 coating layer not only effectively enhances interface compatibility and suppresses the irreversible phase transition of LiCoO2, but also further improves the Li+ transport kinetics and significantly reduces battery polarization, ultimately enabling the modified LiCoO2 electrode to exhibit excellent lithium storage performance and thermal safety characteristics under high voltage conditions. Thus, such effective modified strategy can undoubtedly provide an important academic inspiration for LiCoO2 implication.

Deep Neural Network-Based Temperature Mapping Technique for Heat Sink on Electronic Devices Using Local Thermocouple Sensors

ABSTRACT Heat generated by electronic devices can lead to thermal deformation, damage, and fatigue failure, underscoring the importance of monitoring heat distribution. This study introduces an artificial neural network using two thermocouples for cost-effective temperature distribution prediction. Experimental data from heated systems on chip with attached heat sinks were used for training and validation, integrating thermocouple measurements and infrared camera data. The method’s applicability was verified across four different heat sinks. Additionally, finite element analysis compared stress and strain based on predicted and actual temperature distributions, addressing conventional limitations that focus solely on temperature validation. Furthermore, the temperature of any coordinate could be output by including the coordinate as an input of neural networks, eliminating the hassle of re-constructing or learning the DNN to obtain the temperature of the desired coordinate. Results showed that the temperature distribution could be predicted with high accuracy (over 0.95), and the maximum error rate for stress and strain predictions was 7% in the worst case. This confirmed the feasibility of artificial neural networks to predict the temperature distribution using a minimal number of sensors and ensure robust performance even if the heat sink changes.

Impact of deep cryogenic temperatures on gate stack dual material DG MOSFET performance: Analog and RF analysis

By understanding the potential benefits of Gate Stack Dual Material double gate MOSFET (DG MOSFET), this research aims to contribute to the investigation of its electrical characteristics with improved performance and dependability. A detailed investigation is conducted into the temperature dependent electrical properties at different temperatures. A detailed analysis is conducted on the enhanced gate controllability in lower technology nodes and the protection against short channel effects. Together with the DC performances, this research also analyzes the suggested device's analog performance. The device's performance matrix was presented for temperatures ranging from 70 K to 800 K. The results showsthe improvement the drain current, transconductance, and transconductance generation factor(TGF) with decrease in temperature.

Optimization of Hypothermic Protocols for Neurocognitive Preservation in Aortic Arch Surgery: A Literature Review.

Shifts from deep to moderate hypothermic circulatory arrest (HCA) in aortic arch surgery necessitate an examination of their differential impacts on neurocognitive functions, especially structured verbal memory, given its significance for patient recovery and quality of life. This study evaluates and synthesizes evidence on the effects of deep (≤20.0 °C), low-moderate (20.1-24.0 °C), and high-moderate (24.1-28.0 °C) hypothermic temperatures on structured verbal memory preservation and overall cognitive health in patients undergoing aortic arch surgery. We evaluated the latest literature from major medical databases such as PubMed and Scopus, focusing on research from 2020 to 2024, to gather comprehensive insights into the current landscape of temperature management during HCA. This comparative analysis highlights the viability of moderate hypothermia (20.1-28.0 °C), supported by recent trials and observational studies, as a method to achieve comparable neuroprotection with fewer complications than traditional deep hypothermia. Notably, low-moderate and high-moderate temperatures have been shown to support substantial survival rates, with impacts on structured verbal memory preservation that necessitate careful selection based on individual surgical risks and patient profiles. The findings advocate for a nuanced approach to selecting hypothermic protocols in aortic arch surgeries, emphasizing the importance of tailoring temperature management to optimize neurocognitive outcomes and patient recovery. This study fills a critical gap in the literature by providing evidence-based recommendations for temperature ranges during HCA, calling for ongoing updates to clinical guidelines and further research to refine these recommendations. The implications of temperature on survival rates, complications, and success rates underpin the necessity for evolving cardiopulmonary bypass techniques and cerebral perfusion strategies to enhance patient outcomes in complex cardiovascular procedures.

Distribution of the acoustobrightness temperature satisfies heat equation

Passive acoustic thermometry (PAT), a method proposed for medical applications, involves direct measurement of the acoustobrightness temperature which is the intensity of the thermal acoustic radiation expressed in degrees. The hypothesis has been proposed that the acoustobrightness temperature measured on the surface of the object under study obeys the 2D heat equation whose parameters coincide or are uniquely connected with the parameters of the 3D heat equation governing the deep temperature of the object. Since in PAT a noise signal is measured, the achievement of an acceptable error level (0.5–1 K) requires a considerable integration time (about a minute). The proposed hypothesis will enable linking together the data obtained by acoustothermometric sensors separated in space, at different points in time. This, in turn, will lower the dimensionality of the problem of temperature distribution reconstruction and enable reducing the integration time without loss of accuracy. The proposed hypothesis has been confirmed experimentally and with the aid of computer simulation for the case where local hyperthermia of soft tissues of the human organism is modeled.

Robust Reconstruction of Historical Climate Change From Permafrost Boreholes

AbstractReconstructing historical climate change from deep ground temperature measurements in cold regions is often complicated by the presence of permafrost. Existing methods are typically unable to account for latent heat effects due to the freezing and thawing of the active layer. In this work, we propose a novel method for reconstructing historical ground surface temperature (GST) from borehole temperature measurements that accounts for seasonal thawing and refreezing of the active layer. Our method couples a recently developed fast numerical modeling scheme for two‐phase heat transport in permafrost soils with an ensemble‐based method for approximate Bayesian inference. We evaluate our method on two synthetic test cases covering both cold and warm permafrost conditions as well as using real data from a 100 m deep borehole on Sardakh Island in northeastern Siberia. Our analysis of the Sardakh Island borehole data confirms previous findings that GST in the region have likely risen by 5–9°C between the pre‐industrial period of 1750–1855 and 2012. We also show that latent heat effects due to seasonal freeze‐thaw have a substantial impact on the resulting reconstructed surface temperatures. We find that neglecting the thermal dynamics of the active layer can result in biases of roughly −1°C in cold conditions (i.e., mean annual ground temperature below −5°C) and as much as −2.6°C in warmer conditions where substantial active layer thickening (&gt;200 cm) has occurred. Our results highlight the importance of considering seasonal freeze‐thaw in GST reconstructions from permafrost boreholes.

Wind‐Induced Quasi‐Seasonal and Quasi‐Monthly Variations of Near‐Bottom Temperature on the Chukchi Slope of the Southwestern Canada Basin

AbstractThe time series of near‐bottom temperatures collected from September 2018 until August 2020 from an array of three current‐ and pressure‐recording inverted echo sounders showed quasi‐seasonal and quasi‐monthly (∼28 days) variations at a depth of ∼1,300 m near the Chukchi slope in the western Arctic Ocean. They revealed an increase of ∼0.1°C during the winter‐spring period compared with the summer‐fall period. These variations were observed in the data‐assimilated Hybrid Coordinate Ocean Model (HYCOM) outputs near the observation site (correlation coefficient &gt;0.7). They confirmed that variations in near‐bottom temperature are related to changes in the intensity of the Atlantic Water (AW) boundary current, concurrent with the deepening of the lower AW layer by approximately 50 m. The difference in sea surface height (SSH) between the Canada Basin and the Chukchi Shelf increased because of the negative wind stress curl (WSC) and retarded the AW boundary current according to the geostrophic effect. When the near‐bottom temperature increased during the winter‐spring period, the SSH in the Chukchi Shelf was lower than that in the summer‐fall period because of the less negative WSC. Quasi‐monthly variations were related to SSH on the Chukchi Shelf owing to the negative WSC. HYCOM outputs from 1994 to 2015 showed that the AW boundary current weakened more recently than in the past due to the increased melting of sea ice. The results imply that a longer sea‐ice‐free season in the Arctic amplifies changes in the AW boundary current and deep ocean temperature owing to increased atmospheric forcing.

Monitoring upwelling regions in major coastal zones using deep learning and sea surface temperature images

ABSTRACT The coastal region of northwest Africa experiences a persistent and variable upwelling phenomenon throughout most of the year, contributing to the presence of one of the world’s largest and most productive fishing ports. In this study, we introduce Deep Coas t up -Net a convolutional neural network architecture designed for identifying and extracting upwelling regions from satellite sea surface temperature (SST) images. Our model is trained on a dataset of SST images captured along the Moroccan Atlantic coast, utilizing essential input parameters, including SST, latitudinal position ( LA T pos ), and distance from the coastline ( D coast ). By incorporating these physical parameters as input features and training the model with corresponding masks for the Atlantic coast of Morocco, the Deep Coas t up -Net network learns to accurately segment upwelling regions in major coastal zones and demonstrates its generalization capability. We validate our methodology using a quantitative index. The results of this validation showcase the effectiveness of our approach. Subsequently, we explore seasonal and interannual upwelling trends within a 21-year time series, spanning from 2000 to 2020.

Experimental study of capacity fading mechanism in multiple overdischarge on LiNi0.5Co0.2Mn0.3O2&LiMn2O4/graphite lithium-ion batteries

Batteries can be overdischarged in practical applications, which does not pose safety risks. However, overdischarge can have a significant impact on battery performance. In this work, LiNi0.5Co0.2Mn0.3O2 (NCM)&LiMn2O4 (LMO)/graphite batteries are used as the main body. The failure mechanism of the batteries under different temperatures (−10 °C, 25 °C, 45 °C) and different depth of discharge (DOD) (105 % DOD and 110 % DOD) is studied systematically through the electrochemical methods, scanning electron microscopy (SEM), X-ray diffractometry (XRD), and X-ray photoelectron spectroscopy (XPS). The results show that the deep overdischarge and high temperature accelerate the degradation of electrolyte, separator and collector, and cause rupture and recombination of solid electrolyte interphase (SEI) film. These result in the loss of conductivity, active substances and lithium ions, in which the loss of lithium ions plays a dominant role. Moreover, the attenuation of positive active material mainly comes from NCM. Our research results are of great significance for researchers to understand the failure mechanism of lithium batteries under different conditions and can provide important guidance for the safe design of lithium-ion batteries.

Influence of Microbial Activities on Fluorescent Dissolved Organic Matter in the Dark Canada Basin Waters

AbstractThe fluorescence characteristics of dissolved organic matter (DOM) were measured to determine the distribution and drivers of DOM in the dark Canada Basin waters. We studied the relationship between fluorescent DOM (FDOM) and apparent oxygen utilization (AOU) in the whole water column as well as in the main water masses (Pacific Winter Water, Atlantic halocline (AH), Atlantic water—Fram Strait and Barents Sea branches, Deep Temperature Minimum, and Canada Basin deep water). The relative water mass fractions of different water masses were estimated using a five end‐members mixing model. The distribution of water mass fractions was found to be in good agreement with the distribution based on the traditional temperature‐salinity diagram. The fractions of AH waters were linked with AOU, highlighting the significant role played by mineralization in shaping AH humic‐like levels. The slope of the relationship between in situ FDOM—AOU was greater than those reported in the other oceans worldwide. This suggests that the AH is a hot spot for microbial mineralization of humic‐like DOM. On the other hand, the relationships with the humic‐like intensities differed among the four humic‐like components in the &gt;125 m waters, indicating distinct environmental dynamics and biogeochemical roles for each humic‐like component.