Preparation of Edible Corn Starch Phosphate with Highly Reactive Sodium Tripolyphosphate in the Absence of Catalyst

Purpose: To prepare edible corn starch phosphate under optimized experimental conditions.Methods: Edible corn starch phosphate was prepared via the reaction of starch with active sodium tripolyphosphate. Reaction efficiency and viscosity were used as indices to optimize experimental conditions. Freeze-thaw stability and transparency of starch phosphate and native starch were comparatively studied.Results: Starch phosphate with optimal combined phosphate content (0.39 %) was obtained under optimized conditions: reaction duration, 90 min; temperature, 160 oC; pH, 5.0; and phosphate, 1.5 g. Starch phosphate with optimal viscosity (230 cp) was obtained under different conditions: reaction duration, 120 min; temperature, 140 oC; pH, 6.0; and phosphate, 1.5 g. Significant differences (p < 0.05) were observed in syneresis and paste transparency of starch phosphate and native starch.Conclusion: Edible corn starch phosphate has been successfully prepared under optimized experimental conditions whose freeze-thaw stability and paste transparency has obvious improvement compared with native starch.Keywords: Starch phosphate, Combined phosphate, Sodium tripolyphosphate, Syneresis, Paste efficienc

Experimental Realization of a Quantum Refrigerator Driven by Indefinite Causal Orders

Indefinite causal order (ICO) is playing a key role in recent quantum technologies. Here, we experimentally study quantum thermodynamics driven by ICO on nuclear spins using the nuclear magnetic resonance system. We realize the ICO of two thermalizing channels to exhibit how the mechanism works, and show that the working substance can be non-classically cooled or heated albeit it undergoes thermal contacts with reservoirs of the same temperature. Moreover, we construct a single cycle of the ICO refrigerator, and evaluate its efficiency by measuring the work consumption and the heat energy extracted from the low-temperature reservoir. Unlike classical refrigerators in which the efficiency is perversely higher the closer the temperature of the high-temperature and low-temperature reservoirs are to each other, the ICO refrigerator's efficiency of performance is always bounded to small values due to the non-unit success probability in projecting the ancillary qubit to the preferable subspace. Our experiment demonstrates that the ICO process may offer a new resource with non-classical heat exchange, and paves the way towards construction of quantum refrigerators on a quantum system.Comment: 5 pages, 4 figure

Forest Phenology Dynamics and Its Responses to Meteorological Variations in Northeast China

Based on time series of Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) data (2000–2009), we extracted forest phenological variables in Northeast China using a threshold-based method, which included the start of the growing season (SOS), end of the growing season (EOS), and length of the growing season (LOS). The spatial variation of phenological trends was analyzed using the linear regression method. In Northeast China, SOS was delayed at the rate of <1.5 days per year. The delay trend of EOS was well distributed in the entire region with almost the same rates. LOS increased slightly. The analysis of the relationship between forest phenology and meteorological variations shows that SOS was mainly affected by spring temperature, whereas SOS had a negative relationship with precipitation in the warm-temperate deciduous broadleaf forest region. The EOS in temperate steppe region was affected by temperature and precipitation in August, whereas the others were significantly affected by temperature. Because of the increased temperature in spring, the LOS of the temperate steppe region and temperate mixed forest region increased, and the LOS was positively correlated with the mean temperature of summer in the cool-temperate needleleaf forest region

Review of thermo-physical properties, wetting and heat transfer characteristics of nanofluids and their applicability in industrial quench heat treatment

The success of quenching process during industrial heat treatment mainly depends on the heat transfer characteristics of the quenching medium. In the case of quenching, the scope for redesigning the system or operational parameters for enhancing the heat transfer is very much limited and the emphasis should be on designing quench media with enhanced heat transfer characteristics. Recent studies on nanofluids have shown that these fluids offer improved wetting and heat transfer characteristics. Further water-based nanofluids are environment friendly as compared to mineral oil quench media. These potential advantages have led to the development of nanofluid-based quench media for heat treatment practices. In this article, thermo-physical properties, wetting and boiling heat transfer characteristics of nanofluids are reviewed and discussed. The unique thermal and heat transfer characteristics of nanofluids would be extremely useful for exploiting them as quench media for industrial heat treatment

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery