Techno-economic Analysis of High-Temperature Thermal Energy Storage for On-Demand Heat and Power

Herein we present a concept of a high-temperature, thermal energy storage (HT-TES) system for large-scale long duration energy storage (>10 hours) applications. The system relies on tunable composite ceramic materials with high electrical conductivity and can output the stored energy flexibly in the form of heat at 1100 degrees C or higher, and as electricity. We model the performance and cost of the system in a techno-economic analysis to identify key material and system properties influencing viability. For applications with daily operation (12 hours storage duration), we find achieving levelized storage costs below US Department of Energy’s 5 ₵/kWhe (1-2.5 ₵/kWhth equivalent) target by 2030 is possible. Candidate materials should have above 600-900 high-temperature cycle stability while offering at least 104 S/m of electrical conductivity. Our results suggest this system can be economical for longer storage durations (weeks to months) when coupled with intermittent charging using surplus renewable energy sources.

Effect of Ni(P) Layer Thickness on Interface Reaction and Reliability of Ultrathin ENEPIG Surface Finish

Electroless Ni(P)/electroless Pd/immersion Au (ENEPIG) is a common surface finish in electronic packaging, while the Ni(P) layer increases the impedance of solder joints and leads to signal quality degradation in high-frequency circuits. Reducing the thickness of the Ni(P) layer can balance the high impedance and weldability. In this paper, the interfacial reaction process between ultrathin ENEPIG substrates with different Ni layer thicknesses (0.112 and 0.185 μm) and Sn–3.0Ag–0.5Cu (SAC305) solder during reflow and aging was studied. The bonding ability and reliability of solder joints with different surface finishes were evaluated based on solder ball shear test, drop test and temperature cycle test (TCT), and the failure mechanism was analyzed from the perspective of intermetallic compound (IMC) interface growth. The results showed that the Ni–Sn–P layer generated by ultrathin ENEPIG can inhibit the growth of brittle IMC so that the solder joints maintain high shear strength. Ultrathin ENEPIG with a Ni layer thickness of 0.185 μm had no failure cracks under thermal cycling and drop impact, which can meet actual reliability standards. Therefore, ultrathin ENEPIG has broad prospects and important significance in the field of high-frequency chip substrate design and manufacturing.

The Cyclical Sine Model Explanation for Climate Change

This paper will show that the global warming/climate change underway on Earth today is a totally natural occurrence with solid scientific and historical support. The Earth is currently in the upswing part of its normal temperature cycle. Very warm (Medieval Warming) and very cold (Little Ice Age) cycles have been historically documented on Earth for at least the last 3,000 years. This cyclicity has a repeated period of approximately every 1,500 years [1]. The explanation for the Earth’s temperature increases since 1850 is captured in a mathematical model called the Cyclical Sine Model. This model fits past climate cycles, measured temperatures since 1850, and correlates closely with the thousand year cyclicity of solar activity from 14C/12C ratio studies [2], and Bond [3] Atlantic drift ice cycles. This model also agrees with sunspot history, the Atlantic Multidecadal Oscillation, and the Pacific Decadal Oscillation. In addition, this model quantitively explains the time span 1945-1975 when an impending ice age was feared [4]. Earth temperatures are controlled by three solar cycles of approximately 1,000, 70, and 11 years. The Cyclical Sine Model is the best explanation for the Earth’s recent temperature increases.

Effect of Particle Size and Transparent Solar Dryer Cover on the Proximate Analysis of Dried Onion

In this work, we investigate effect of particle size and transparent solar dryer cover on the proximate analysis of dried onion. A solar drying unit was developed and constructed for drying the of red onion slices in order to determine the proximate composition of fresh and dried red onion using multi-crop direct solar dryer. Also, the evaluation and effect of particles size and multi-crop transparent solar dryer cover on the proximate analysis of red onion during drying. Consequently, the higher efficiency of the solar collector was obtained at the higher airflow rate. The moisture content of dried onion slices was strongly affected by the thickness of the onion slices and the density of the polyethylene. The final moisture content of dried onion slices ranged from 10.85% to 13.01%, 4.95% to 6.01% ash, 4.69% to 5.26% fibre, 11.17% to 13.09% fat, 6.70% to 5.60% protein and 68.64% to 68.03% carbohydrate for particle sizes of 3 mm, 5 mm and 7 mm dry-basis depending on drying temperature cycle for low density polyethylene cover. While the final moisture content of dried onion slices ranged from 9.85% to 12.01%, 5.96% to 6.01% ash, 3.69% to 4.26% fibre, 13.17% to 12.09% fat, 5.70% to 6.60% protein and 61.64% to 58.03% carbohydrate for particle sizes of 3 mm, 5mm and 7mm dry-basis depending on drying temperature cycle for high density polyethylene cover.

Deciphering Temperature Seasonality in Earth's Ancient Oceans

Ongoing global warming due to anthropogenic climate change has long been recognized, yet uncertainties regarding how seasonal extremes will change in the future persist. Paleoseasonal proxy data from intervals when global climate differed from today can help constrain how and why the annual temperature cycle has varied through space and time. Records of past seasonal variation in marine temperatures are available in the oxygen isotope values of serially sampled accretionary organisms. The most useful data sets come from carefully designed and computationally robust studies that enable characterization of paleoseasonal parameters and seamless integration with mean annual temperature data sets and climate models. Seasonal data sharpen interpretations of—and quantify overlooked or unconstrained seasonal biases in—the more voluminous mean temperature data and aid in the evaluation of climate model performance. Methodologies to rigorously analyze seasonal data are now available, and the promise of paleoseasonal proxy data for the next generation of paleoclimate research is significant. Expected final online publication date for the Annual Review of Earth and Planetary Sciences, Volume 50 is May 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

High-Resolution Spatiotemporal Dynamics of Harmful Algae in the Indian River Lagoon (Florida)—A Case Study of Aureoumbra lagunensis, Pyrodinium bahamense, and Pseudo-nitzschia

The Indian River Lagoon (IRL), located on the east coast of Florida, is a complex estuarine ecosystem that is negatively affected by recurring harmful algal blooms (HABs) from distinct taxonomic/functional groups. Enhanced monitoring was established to facilitate rapid quantification of three recurrent bloom taxa, Aureoumbra lagunensis, Pyrodinium bahamense, and Pseudo-nitzschia spp., and included corroborating techniques to improve the identification of small-celled nanoplankton (<10 μm in diameter). Identification and enumeration of these target taxa were conducted during 2015–2020 using a combination of light microscopy and species-specific approaches, specifically immunofluorescence flow cytometry as well as a newly developed qPCR assay for A. lagunensis presented here for the first time. An annual bloom index (ABI) was established for each taxon based on occurrence and abundance data. Blooms of A. lagunensis (>2 × 108 cells L–1) were observed in all 6 years sampled and across multiple seasons. In contrast, abundance of P. bahamense, largely driven by the annual temperature cycle that moderates life cycle transitions and growth, displayed a strong seasonal pattern with blooms (105–107 cells L–1) generally developing in early summer and subsiding in autumn. However, P. bahamense bloom development was delayed and abundance was significantly lower in years and locations with sustained A. lagunensis blooms. Pseudo-nitzschia spp. were broadly distributed with sporadic bloom concentrations (reaching 107 cells L–1), but with minimal concentrations of the toxin domoic acid detected (<0.02 μg L–1). In summer 2020, multiple monitoring tools characterized a novel nano-cyanobacterium bloom (reaching 109 cells L–1) that coincided with a decline in A. lagunensis and persisted into autumn. Statistical and time-series analyses of this spatiotemporally intensive dataset highlight prominent patterns in variability for some taxa, but also identify challenges of characterizing mechanisms underlying more episodic yet persistent events. Nevertheless, the intersect of temperature and salinity as environmental proxies proved to be informative in delineating niche partitioning, not only in the case of taxa with long-standing data sets but also for seemingly unprecedented blooms of novel nanoplanktonic taxa.

ACCURACY ASSESSMENT OF FY-4A FIRE/HOTSPOT (FHS) PRODUCT IN WILDFIRE DETECTION

The Fengyun-4A (FY-4A) is a relatively new geostationary satellite launched by the National Satellite Meteorological Center of China in 2016. With its Advanced Geosynchronous Radiation Imager (AGRI) instrument, FY-4A was able to provide a Fire and Hotspot product (FHS). This study explored the use of the FHS product in detecting wildfires and was compared to the similar fire detection product of the Visible Infrared Imaging Radiometer Suite (VIIRS) with the goal of assessing its effectiveness in the early detection and monitoring of wildfires. The FY-4A FHS and the VIIRS fire detection products have spatial resolutions of 2 km and 375 m, and temporal resolutions of 15 minutes and 12 hours, respectively. The results of the comparative study showed that the FY-4A FHS product generated false negative results for detecting wildfires smaller than 20 pixels of VIIRS data (∼2.82 km2), at less than 4 MW of radiative power, and brightness temperature lower than 330 K. The FY-4A FHS product was also shown to be 50% accurate (1 correct and 1 false negative out of 2 samples) in detecting large wildfires (>2.5 km2) with high radiative power (>4 MW) and high brightness temperature (>330 K). Lower accuracy may also be attributed to the presence of clouds that tend to obscure satellite images leading to an even lower accuracy of wildfire detection. For future studies, it is recommended that a comparison of the FY-4A FHS product be made with a more similar instrument, for example, the Advanced Himawari Imager 8/9 (AHI 8/9). It is also recommended to improve the fire and hotspot algorithm by incorporating a Normalized Brightness Temperature Difference Index (NBTDI) or by incorporating diurnal temperature cycle modelling for the older FY-2G data. Lastly, if available, a more reliable accuracy assessment can be done using FHS products of higher spatial resolution (at least 500 m).

The Cyclical Sine Model Explanation for Climate Change

Climate Change due to excessive buildup of greenhouse gasses in the atmosphere from hydrocarbon combustion is one explanation (NASA greenhouse effect) for the Earth’s temperature increase since 1850. If this is true, then eliminating fossil fuel use is the only way to preserve our planet. However, there is another explanation for Global Warming/Climate Change that leads to the opposite conclusion – hydrocarbon energy will be needed well into the future to cope with future, excessively hot and cold temperature cycles. This paper will show that the Global Warming/Climate Change underway on Earth today is a totally natural occurrence with solid scientific and historical support. The Earth is currently in the upswing part of its normal temperature cycle. Very warm (Medieval Warming) and very cold (Little Ice Age) cycles have been historically documented on Earth for at least the last 3,000 years. This cyclicity has a repeated period of about every 1,500 years (Singer 2008). This explanation for the Earth’s temperature increases since 1850 is captured in a mathematical model called the Cyclical Sine Model. This model fits past climate cycles, measured temperatures since 1850, and correlates closely with the cyclicity of Bond Atlantic Drift Ice Cycles (Bond 1997), the Atlantic Multidecadal Oscillation (NASA AMO), and the Pacific Decadal Oscillation (NASA PDO). This model also quantitively explains the time span 1945-1975 when an impending ice age was feared (Time Magazine 1974). The Cyclical Sine Model is the best explanation for the Earth’s recent temperature increases.