On-Chip Micro Temperature Controllers Based on Freestanding Thermoelectric Nano Films for Low-Power Electronics

Multidimensional integration and multifunctional com-ponent assembly have been greatly explored in recent years to extend Moore’s Law of modern microelectronics.However,this inevitably exac-erbates the inhomogeneity of temperature distribution in microsystems,making precise temperature control for electronic components extremely challenging.Herein,we report an on-chip micro temperature controller including a pair of thermoelectric legs with a total area of 50×50μm^(2),which are fabricated from dense and flat freestanding Bi2Te3-based ther-moelectric nano films deposited on a newly developed nano graphene oxide membrane substrate.Its tunable equivalent thermal resistance is controlled by electrical currents to achieve energy-efficient temperature control for low-power electronics.A large cooling temperature difference of 44.5 K at 380 K is achieved with a power consumption of only 445μW,resulting in an ultrahigh temperature control capability over 100 K mW^(-1).Moreover,an ultra-fast cooling rate exceeding 2000 K s^(-1) and excellent reliability of up to 1 million cycles are observed.Our proposed on-chip temperature controller is expected to enable further miniaturization and multifunctional integration on a single chip for microelectronics.

Record-breaking High-temperature Outlook for 2023: An Assessment Based on the China Global Merged Temperature(CMST) Dataset

According to the latest version(version 2.0) of the China global Merged Surface Temperature(CMST2.0) dataset, the global mean surface temperature(GMST) in the first half of 2023 reached its third warmest value since the period of instrumental observation began, being only slightly lower than the values recorded in 2016 and 2020, and historically record-breaking GMST emerged from May to July 2023. Further analysis also indicates that if the surface temperature in the last five months of 2023 approaches the average level of the past five years, the annual average surface temperature anomaly in 2023 of approximately 1.26°C will break the previous highest surface temperature, which was recorded in 2016of approximately 1.25°C(both values relative to the global pre-industrialization period, i.e., the average value from 1850 to1900). With El Ni?o triggering a record-breaking hottest July, record-breaking average annual temperatures will most likely become a reality in 2023.

El Niño and the AMO Sparked the Astonishingly Large Margin of Warming in the Global Mean Surface Temperature in 2023

In 2023,the majority of the Earth witnessed its warmest boreal summer and autumn since 1850.Whether 2023 will indeed turn out to be the warmest year on record and what caused the astonishingly large margin of warming has become one of the hottest topics in the scientific community and is closely connected to the future development of human society.We analyzed the monthly varying global mean surface temperature(GMST)in 2023 and found that the globe,the land,and the oceans in 2023 all exhibit extraordinary warming,which is distinct from any previous year in recorded history.Based on the GMST statistical ensemble prediction model developed at the Institute of Atmospheric Physics,the GMST in 2023 is predicted to be 1.41℃±0.07℃,which will certainly surpass that in 2016 as the warmest year since 1850,and is approaching the 1.5℃ global warming threshold.Compared to 2022,the GMST in 2023 will increase by 0.24℃,with 88%of the increment contributed by the annual variability as mostly affected by El Niño.Moreover,the multidecadal variability related to the Atlantic Multidecadal Oscillation(AMO)in 2023 also provided an important warming background for sparking the GMST rise.As a result,the GMST in 2023 is projected to be 1.15℃±0.07℃,with only a 0.02℃ increment,if the effects of natural variability—including El Niño and the AMO—are eliminated and only the global warming trend is considered.

Extraction of the key infrared radiation temperature features concerning stress and crack evolution of loaded rocks

The infrared radiation temperature(IRT)variation concerning stress and crack evolution of rocks is a critical focus in rock mechanics domain and engineering disaster warning.In this paper,a methodology to extract the key IRT features related to stress and crack evolution of loaded rocks is proposed.Specifically,the wavelet denoising and reconstruction in thermal image sequence(WDRTIS)method is employed to eliminate temporal noise in thermal image sequences.Subsequently,the adaptive partition temperature drift correction(APTDC)method is introduced to alleviate temperature drift.On this basis,the spatial noise correction method based on threshold segmentation and adaptive median filtering(OTSU-AMF)is proposed to extract the key IRT features associated with microcracks of loaded rocks.Following temperature drift correction,IRT provides an estimation of the thermoelastic factor in rocks,typically around 5.29×10^(-5) MPa^(-1) for sandstones.Results reveal that the high-temperature concentrated region in cumulative thermal images of crack evolution(TICE)can elucidate the spatiotemporal evolution of localized damage.Additionally,heat dissipation of crack evolution(HDCE)acquired from TICE quantifies the progressive failure process of rocks.The proposed methodology enhances the reliability of IRT monitoring results and provides an innovative approach for conducting research in rock mechanics and monitoring engineering disasters.

Deep learning-assisted common temperature measurement based on visible light imaging

Real-time,contact-free temperature monitoring of low to medium range(30℃-150℃)has been extensively used in industry and agriculture,which is usually realized by costly infrared temperature detection methods.This paper proposes an alternative approach of extracting temperature information in real time from the visible light images of the monitoring target using a convolutional neural network(CNN).A mean-square error of<1.119℃was reached in the temperature measurements of low to medium range using the CNN and the visible light images.Imaging angle and imaging distance do not affect the temperature detection using visible optical images by the CNN.Moreover,the CNN has a certain illuminance generalization ability capable of detection temperature information from the images which were collected under different illuminance and were not used for training.Compared to the conventional machine learning algorithms mentioned in the recent literatures,this real-time,contact-free temperature measurement approach that does not require any further image processing operations facilitates temperature monitoring applications in the industrial and civil fields.

Effect of sample temperature on femtosecond laser ablation of copper

We conduct an experimental study supported by theoretical analysis of single laser ablating copper to investigate the interactions between laser and material at different sample temperatures,and predict the changes of ablation morphology and lattice temperature.For investigating the effect of sample temperature on femtosecond laser processing,we conduct experiments on and simulate the thermal behavior of femtosecond laser irradiating copper by using a two-temperature model.The simulation results show that both electron peak temperature and the relaxation time needed to reach equilibrium increase as initial sample temperature rises.When the sample temperature rises from 300 K to 600 K,the maximum lattice temperature of the copper surface increases by about 6500 K under femtosecond laser irradiation,and the ablation depth increases by 20%.The simulated ablation depths follow the same general trend as the experimental values.This work provides some theoretical basis and technical support for developing femtosecond laser processing in the field of metal materials.

Temperature inversion enables superior stability for low-temperature Zn-ion batteries

It is challenging for aqueous Zn-ion batteries(ZIBs)to achieve comparable low-temperature(low-T)performance due to the easy-frozen electrolyte and severe Zn dendrites.Herein,an aqueous electrolyte with a low freezing point and high ionic conductivity is proposed.Combined with molecular dynamics simulation and multi-scale interface analysis(time of flight secondary ion mass spectrometry threedimensional mapping and in-situ electrochemical impedance spectroscopy method),the temperature independence of the V_(2)O_(5)cathode and Zn anode is observed to be opposite.Surprisingly,dominated by the solvent structure of the designed electrolyte at low temperatures,vanadium dissolution/shuttle is significantly inhibited,and the zinc dendrites caused by this electrochemical crosstalk are greatly relieved,thus showing an abnormal temperature inversion effect.Through the disclosure and improvement of the above phenomena,the designed Zn||V_(2)O_(5)full cell delivers superior low-T performance,maintaining almost 99%capacity retention after 9500 cycles(working more than 2500 h)at-20°C.This work proposes a kind of electrolyte suitable for low-T ZIBs and reveals the inverse temperature dependence of the Zn anode,which might offer a novel perspective for the investigation of low-T aqueous battery systems.

Properties of Activated Carbons from Sugarcane Leaves and Rice Straw Derived Charcoals by Activation at Low Temperature via KMnO_(4)Pre-Oxidation-Hydrolysis

Activated carbon preparation from sugarcane leaves and rice straw by carbonization(250℃–400℃)and activation at 500℃were studied.The effects of pre-oxidation,hydrolysis of derived charcoals by boiled KMnO4 aqueous solution were evaluated.The derived charcoals products were pretreated using oxidation-hydrolysis with 1–5 wt.%KMnO4 at 100℃and then activated at 500℃.The derived charcoal and activated carbon products were characterized by FTIR,XRD,SEM-EDS and BET.Iodine number and methylene blue number of derived products were also used for the analysis of the products.It was found that fabricated charcoal materials made at 350℃–400℃possess good characteristics with low content of surface functional groups and high carbon content.After pre-oxidation-hydrolysis and activation at 500℃,the resulting derived activated carbon materials from charcoals with 400℃carbonization temperature have high content of oxygen containing surface functional groups such as Mn-O,Si-O,Si-O-Si,C-O,or O-H.In addition,MnO2 accumulated on the surface of the derived activated carbon products.The surface area and pore volume of the activated carbon products have also increased with increasing of KMnO4 concentration from 1 to 3 wt.%and then decreased with 5 wt.%used during activation.Therefore,activated carbon products made by pre-oxidation-hydrolysis with 3 wt.%KMnO4 were used for Fe(Ⅲ)adsorption experiments.It was found that Fe(Ⅲ)adsorption on the activated carbon materials can be fitted with both the Freundlich and the Langmuir models.The calculated maximum Fe(Ⅲ)adsorption capacities of sugarcane leaves derived activated carbon and rice straw derived activated carbon products were 50.00 and 39.37 mg/g,respectively.It was shown that the effect of pre-oxidation-hydrolysis by KMnO4 and activation at 500℃are beneficial for activated carbon preparation with environmentally friendly and low-cost simplified operation.

Design and evaluation of UHPP steel bridge deck pavement for high-temperature and rainy regions

To enhance the serviceability of steel bridge deck pavement(SBDP)in high-temperature and rainy regions,a concept of rigid bottom and flexible top was summarized using engineering practices,which led to the proposal of a three-layer ultra-high-performance pavement(UHPP).The high-temperature rutting resistance and wet-weather skid resistance of UHPP were evaluated through composite structure tests.The internal temperature distribution within the pavement under typical high-temperature conditions was analyzed using a temperature field model.Additionally,a temperature-stress coupling model was employed to investigate the key load positions and stress response characteristics of the UHPP.The results indicate that compared with the traditional guss asphalt+stone mastic asphalt structure,the dynamic stability of the UHPP composite structure can be improved by up to 20.4%.Even under cyclic loading,UHPP still exhibits superior surface skid resistance compared to two traditional SBDPs.The thickness composition of UHPP significantly impacts its rutting resistance and skid resistance.UHPP exhibits relatively low tensile stress but higher shear stress levels,with the highest shear stress occurring between the UHPP and the steel plate.This suggests that the potential risk of damage for UHPP primarily lies within the interlayer of the pavement.Based on engineering examples,introducing interlayer gravel and optimizing the amount of bonding layer are advised to ensure that UHPP possesses sufficient interlayer shear resistance.

The Effects of Thickness and Location of PCMon the Building’s Passive Temperature-Control–A Numerical Study

Building energy consumption and building carbon emissions both account for more than 20%of their total national values in China.Building employing phase change material(PCM)for passive temperature control shows a promising prospect in meeting the comfort demand and reducing energy consumption simultaneously.However,there is a lack of more detailed research on the interaction between the location and thickness of PCM and indoor natural convection,as well as indoor temperature distribution.In this study,the numerical model of a passive temperature-controlled building integrating the developed PCM module is established with the help of ANSYS.In which,the actual weather condition of Beijing city is set as the boundary conditions and the indoor natural convection is simulated with the consideration of radiation model.The effects of PCM’s thickness and location on the internal temperature field are analyzed and discussed.The results show that the room could maintain within the human comfort temperature range with the longest ratio of 94.10%and the shortest ratio of 51.04%as integrating PCM.In comparison,the value is only 26.70%without PCM.The room’s maximum temperature fluctuation can also be improved;it could be lowered by 64.4%compared to the normal condition.When the quantity of PCM is sufficient,further increasing the PCM amount results in a temperature fluctuation reduction of less than 0.1°C and does not increase the comfort time.Placing PCM on the wall induces an apparent variation in indoor temperature along the vertical direction.Conversely,placing PCM on the roof can lead to a heat transfer rate difference of up to seven times.The optimal placement of PCM depends on the difference between the environmental and phase change temperatures.If the difference is positive,placing PCM on the roof is more effective;conversely,the opposite holds.According to the results over the entire cycle,PCM application on vertical walls yields better performance.The significant difference in natural convection caused by the same thickness of PCM but different application positions,coupled with the influence of air movement on the melting and solidification of PCM,further impacts indoor temperature fluctuations and comfort.This study can provide guidance for the application location and thickness of PCM,especially for scenarios where temperature regulation is required at a specific time.

New Insight to the Surface Temperature of the Sun

Information is given on thermal radiation from the Sun, considered in practical engineering calculations of heat exchange. It was found that although the surface temperature of the Sun is assumed to be about 5800 K, the solar spectrum data measured by Kondratyev lead to a value of at least 7134 K. Such a higher value can be obtained by interpreting the Planck formula for the black radiation spectrum for the Kondratyev data. In addition, using the Stefan-Boltzmann law, the energetic emissivity of the Sun’s surface was determined to be 0.431. Furthermore, based on Petela’s formulae for exergy of thermal radiation, the exergetic emissivity of the Sun’s surface was also calculated at the level of 0.426.

An Explanation of the Temperature-Dependent Upper Critical Field Data of H3S on the Basis of the Thermodynamics of a Superconductor in a Magnetic Field

Excellent fits to a couple of the data-sets on the temperature (T)-dependent upper critical field (Hc2) of H3S (critical temperature, Tc ≈ 200 K at pressure ≈ 150 GPa) reported by Mozaffari, et al. (2019) were obtained by Talantsev (2019) in an approach based on an ingenious mix of the Ginzberg-Landau (GL), the Werthamer, Helfand and Hohenberg (WHH), and the Gor’kov, etc., theories which have individually been employed for the same purpose for a long time. Up to the lowest temperature (TL) in each of these data-sets, similarly accurate fits have also been obtained by Malik and Varma (2023) in a radically different approach based on the Bethe-Salpeter equation (BSE) supplemented by the Matsubara and the Landau quantization prescriptions. For T TL, however, while the (GL, WHH, etc.)-based approach leads to Hc2(0) ≈ 100 T, the BSE-based approach leads to about twice this value even at 1 K. In this paper, a fit to one of the said data-sets is obtained for the first time via a thermodynamic approach which, up to TL, is as good as those obtained via the earlier approaches. While this is interesting per se, another significant result of this paper is that for T TL it corroborates the result of the BSE-based approach.

Assessment of the Spatio-Temporal Trends of Annual Extreme Temperature Indices over Tanzania during the Period of 1982-2022

Extreme weather and climatic phenomena, such as heatwaves, cold waves, floods and droughts, are expected to become more common and have a significant impact on ecosystems, biodiversity, and society. Devastating disasters are mostly caused by record-breaking extreme events, which are becoming more frequent throughout the world, including Tanzania. A clear global signal of an increase in warm days and nights and a decrease in cold days and nights has been observed. The present study assessed the trends of annual extreme temperature indices during the period of 1982 to 2022 from 29 meteorological stations in which the daily minimum and maximum data were obtained from NASA/POWER. The Mann-Kendall and Sen slope estimator were employed for trend analysis calculation over the study area. The analyzed data have indicated for the most parts, the country has an increase in warm days and nights, extreme warm days and nights and a decrease in cold days and nights, extreme cold days and nights. It has been disclosed that the number of warm nights and days is on the rise, with the number of warm nights trending significantly faster than the number of warm days. The percentile-based extreme temperature indices exhibited more noticeable changes than the absolute extreme temperature indices. Specifically, 66% and 97% of stations demonstrated positive increasing trends in warm days (TX90p) and nights (TN90p), respectively. Conversely, the cold indices demonstrated 41% and 97% negative decreasing trends in TX10p and TN10p, respectively. The results are seemingly consistent with the observed temperature extreme trends in various parts of the world as indicated in IPCC reports.

Temperature-Based Fan Speed Optimization Strategy

As energy efficiency and indoor comfort increasingly become key standards in modern residential and office environments,research on intelligent fan speed control systems has become particularly important.This study aims to develop a temperature-feedback-based fan speed optimization strategy to achieve higher energy efficiency and user comfort.Firstly,by analyzing existing fan speed control technologies,their main limitations are identified,such as the inability to achieve smooth speed transitions.To address this issue,a BP-PID speed control algorithm is designed,which dynamically adjusts fan speed based on indoor temperature changes.Experimental validation demonstrates that the designed system can achieve smooth speed transitions compared to traditional fan systems while maintaining stable indoor temperatures.Furthermore,the real-time responsiveness of the system is crucial for enhancing user comfort.Our research not only demonstrates the feasibility of temperature-based fan speed optimization strategies in both theory and practice but also provides valuable insights for energy management in future smart home environments.Ultimately,this research outcome will facilitate the development of smart home systems and have a positive impact on environmental sustainability.

Study on Temperature Disaster Indicators of Passion Fruit in Southwest Fujian, China

In China, meteorological forecasting relies on meteorological data obtained from regional and national stations. However, there were discrepancies between the data collected from the meteorological station at the passion fruit growing base and the data from regional and national stations. Consequently, the high and low temperature disaster indicators determined by the meteorological station at the passion fruit growing base cannot be applied to meteorological forecasting. To address this issue and facilitate the monitoring and early warning of high and low temperature disasters in passion fruit cultivation in Fujian, China, we used multi-source hourly temperature data (including the data from meteorological observation stations in passion fruit growing bases, the nearest regional stations, and national surface conventional meteorological observation stations) in three cities in southwestern Fujian (Longyan, Sanming, and Zhangzhou) spanning the years 2020 to 2022. By employing comprehensive statistical analysis methods (0.5 interval division and Cumulative frequency), we identified that passion fruit in southwestern Fujian was susceptible to high temperature disasters during the blooming-fruiting period, as well as low temperature disasters during the sprouting period. Consequently, we developed high and low temperature disaster indicators based on data from regional and national stations for different phenological periods of passion fruit in this region.

Development of a RBFNN prediction model for carrot quality based on meteorological temperatures at vegetable stations

To evaluate and predict the quality of carrots during logistics process in North China under extreme temperature conditions,quality indicator changes of carrots were investigated,and temperature-coupled quality prediction models were developed.Seven temperatures were selected from meteorological temperature data by cluster analysis to simulate the changes in extreme temperatures during the short-term transportation of carrots.No carrots rotted during the 48h storage period.Under both isothermal and nonisothermal conditions,weight loss andΔE increased while the firmness and sensory evaluation(SE)decreased.The RBFNN performed better than the Arrhenius model in predicting weight loss andΔE,with R^(2)>0.97,MSE<0.009 and relative errors within±18%.The results of the predictive confidence level and standardized residual indicated the good performance of the RBFNN model.The temperature-coupled prediction models of RBFNN were promising candidates for predicting the quality of vegetable products and therefore reducing economic loss of vegetable industry.

Competitive oxidation behavior of Ni-based superalloy GH4738 at extreme temperature

A high thrust-to-weight ratio poses challenges to the high-temperature performance of Ni-based superalloys. The oxidation behavior of GH4738 at extreme temperatures has been investigated by isothermal and non-isothermal experiments. As a result of the competitive diffusion of alloying elements, the oxide scale included an outermost porous oxide layer (OOL), an inner relatively dense oxide layer (IOL), and an internal oxide zone (IOZ), depending on the temperature and time. A high temperature led to the formation of large voids at the IOL/IOZ interface. At 1200℃, the continuity of the Cr-rich oxide layer in the IOL was destroyed, and thus, spallation occurred. Extension of oxidation time contributed to the size of Al-rich oxide particles with the increase in the IOZ. Based on this finding,the oxidation kinetics of GH4738 was discussed, and the corresponding oxidation behavior at 900-1100℃ was predicted.

Alkyl dimethyl betaine activates the low-temperature collection capacity of sodium oleate for scheelite

The impact of alkyl dimethyl betaine (ADB) on the collection capacity of sodium oleate (NaOl) at low temperatures was evaluated using flotation tests at various scales. The low-temperature synergistic mechanism of ADB and NaOl was explored by infrared spectroscopy, X-ray photoelectron spectroscopy, surface tension measurement, foam performance test, and flotation reagent size measurement.The flotation tests revealed that the collector mixed with octadecyl dimethyl betaine (ODB) and NaOl in a mass ratio of 4:96 exhibited the highest collection capacity. The combined collector could increase the scheelite recovery by 3.48% at low temperatures of 8–12℃. This is particularly relevant in the Luanchuan area, which has the largest scheelite concentrate output in China. The results confirmed that ODB enhanced the collection capability of NaOl by improving the dispersion and foaming performance. Betaine can be introduced as an additive to NaOl to improve the recovery of scheelite at low temperatures.

Machine learning-assisted efficient design of Cu-based shape memory alloy with specific phase transition temperature

The martensitic transformation temperature is the basis for the application of shape memory alloys(SMAs),and the ability to quickly and accurately predict the transformation temperature of SMAs has very important practical significance.In this work,machine learning(ML)methods were utilized to accelerate the search for shape memory alloys with targeted properties(phase transition temperature).A group of component data was selected to design shape memory alloys using reverse design method from numerous unexplored data.Component modeling and feature modeling were used to predict the phase transition temperature of the shape memory alloys.The experimental results of the shape memory alloys were obtained to verify the effectiveness of the support vector regression(SVR)model.The results show that the machine learning model can obtain target materials more efficiently and pertinently,and realize the accurate and rapid design of shape memory alloys with specific target phase transition temperature.On this basis,the relationship between phase transition temperature and material descriptors is analyzed,and it is proved that the key factors affecting the phase transition temperature of shape memory alloys are based on the strength of the bond energy between atoms.This work provides new ideas for the controllable design and performance optimization of Cu-based shape memory alloys.

Low-energy-consumption temperature swing system for CO_(2) capture by combining passive radiative cooling and solar heating

Temperature-swing adsorption(TSA)is an effective technique for CO_(2) capture,but the temperature swing procedure is energy-intensive.Herein,we report a low-energy-consumption system by combining passive radiative cooling and solar heating for the uptake of CO_(2) on commercial activated carbons(CACs).During adsorption,the adsorbents are coated with a layer of hierarchically porous poly(vinylidene fluoride-co-hexafluoropropene)[P(VdF-HFP)HP],which cools the adsorbents to a low temperature under sunlight through radiative cooling.For desorption,CACs with broad absorption of the solar spectrum are exposed to light irradiation for heating.The heating and cooling processes are completely driven by solar energy.Adsorption tests under mimicked sunlight using the CACs show that the performance of this system is comparable to that of the traditional ones.Furthermore,under real sunlight irradiation,the adsorption capacity of the CACs can be well maintained after multiple cycles.The present work may inspire the development of new temperature swing procedures with little energy consumption.