Thermoelectric property studies on thallium-doped lead telluride prepared by ball milling and hot pressing

Thallium doping into lead telluride has been demonstrated to increase the dimensionless thermoelectric figure-of-merit (ZT) by enhancing Seebeck coefficient due to the creation of resonant states close to Fermi level without affecting the thermal conductivity. However, the process is tedious, energy consuming, and small in quantities since it involves melting, slow cooling for crystal growth, long time annealing, post-crushing and hot pressing. Here we show that a similar ZT value about 1.3 at 400 °C is achieved on bulk samples with grain sizes of 3–7 μm by ball milling a mixture of elemental thallium, lead, and tellurium and then hot pressing the ball milled nanopowders

Enhancement of thermoelectric figure-of-merit at low temperatures by titanium substitution for hafnium in n-type half-Heuslers Hf0.75−xTixZr0.25NiSn0.99Sb0.01

The effect of titanium (Ti) substitution for hafnium (Hf) on thermoelectric properties of (Hf, Zr)-based n-type half-Heuslers: Hf[subscript 0.75−x]Ti[subscript x]Zr[subscript 0.25]NiSn[subscript 0.99]Sb[subscript 0.01], has been studied. The samples are made by arc melting followed by ball milling and hot pressing via the nanostructuring approach. A peak thermoelectric figure-of-merit (ZT) of ∼1.0 is achieved at 500 °C in samples with a composition of Hf[subscript 0.5]Zr[subscript 0.25]Ti[subscript 0.25]NiSn[subscript 0.99]Sb[subscript 0.01] due to a slight increase in carrier concentration and also a lower thermal conductivity caused by Ti. The ZT values below 500 °C of hot pressed Hf[subscript 0.5]Zr[subscript 0.25]Ti[subscript 0.25]NiSn[subscript 0.99]Sb[subscript 0.01] samples are significantly higher than those of the same way prepared Hf[subscript 0.75]Zr[subscript 0.25]NiSn[subscript 0.99]Sb[subscript 0.01] samples at each temperature, which are very much desired for mid-range temperature applications such as waste heat recovery in automobiles.United States. Department of Energy. Office of Science. Solid-State Solar Thermal Energy Conversion Center (Award DE-SC0001299/DE-FG02–09ER46577

Reinforcement-Learning based Portfolio Management with Augmented Asset Movement Prediction States

Portfolio management (PM) is a fundamental financial planning task that aims to achieve investment goals such as maximal profits or minimal risks. Its decision process involves continuous derivation of valuable information from various data sources and sequential decision optimization, which is a prospective research direction for reinforcement learning (RL). In this paper, we propose SARL, a novel State-Augmented RL framework for PM. Our framework aims to address two unique challenges in financial PM: (1) data heterogeneity -- the collected information for each asset is usually diverse, noisy and imbalanced (e.g., news articles); and (2) environment uncertainty -- the financial market is versatile and non-stationary. To incorporate heterogeneous data and enhance robustness against environment uncertainty, our SARL augments the asset information with their price movement prediction as additional states, where the prediction can be solely based on financial data (e.g., asset prices) or derived from alternative sources such as news. Experiments on two real-world datasets, (i) Bitcoin market and (ii) HighTech stock market with 7-year Reuters news articles, validate the effectiveness of SARL over existing PM approaches, both in terms of accumulated profits and risk-adjusted profits. Moreover, extensive simulations are conducted to demonstrate the importance of our proposed state augmentation, providing new insights and boosting performance significantly over standard RL-based PM method and other baselines.Comment: AAAI 202

Enhancement of thermoelectric figure-of-merit by resonant states of aluminium doping in lead selenide

By adding aluminium (Al) into lead selenide (PbSe), we successfully prepared n-type PbSe thermoelectric materials with a figure-of-merit (ZT) of 1.3 at 850 K. Such a high ZT is achieved by a combination of high Seebeck coefficient caused by very possibly the resonant states in the conduction band created by Al dopant and low thermal conductivity from nanosized phonon scattering centers.United States. Dept. of Energy. Office of Basic Energy Sciences (Solid-State Solar-Thermal Energy Conversion Center Award DE-SC0001299/DE-FG02-09ER46577

Improving Certified Robustness via Statistical Learning with Logical Reasoning

Intensive algorithmic efforts have been made to enable the rapid improvements of certificated robustness for complex ML models recently. However, current robustness certification methods are only able to certify under a limited perturbation radius. Given that existing pure data-driven statistical approaches have reached a bottleneck, in this paper, we propose to integrate statistical ML models with knowledge (expressed as logical rules) as a reasoning component using Markov logic networks (MLN, so as to further improve the overall certified robustness. This opens new research questions about certifying the robustness of such a paradigm, especially the reasoning component (e.g., MLN). As the first step towards understanding these questions, we first prove that the computational complexity of certifying the robustness of MLN is #P-hard. Guided by this hardness result, we then derive the first certified robustness bound for MLN by carefully analyzing different model regimes. Finally, we conduct extensive experiments on five datasets including both high-dimensional images and natural language texts, and we show that the certified robustness with knowledge-based logical reasoning indeed significantly outperforms that of the state-of-the-art

Study of Temperature Dependence of the Deposition Rates of Ni-P Based Sonication-Assisted Nanoscale Electroless Composite Plating

In this study, SiCp/Ni-P nano-composite coatings were fabricated on 45 steel substrates under ultrasonic excitation at temperatures from 85 °C to 45 °C. The micro-morphologies and compositions of the coatings were analyzed using Scanning Electron Microscopy (SEM) and Energy Dispersion Spectrum (EDS). The deposition rates of the different processes were then investigated using the polarization curve method, the weighing method, and the film thickness method. The effects of sonication on the coating process were also investigated. The results reveal that free energy decreased to 16.38 kJ/mol, which can be attributed to the positive effect of sonication on the reaction activity. Furthermore, the deposition rates of the composite plating decreased with the process temperature. Specifically, the deposition rates measured by the polarization curve method, weighing method, and film thickness method decreased from 0.625 mg/(cm2·h) to 0.116 mg/(cm2·h), from 3.9 mg/(cm2·h) to 1.6 mg/(cm2·h), and from 36.64 μm/h to 7.05 μm/h, respectively. DOI: http://dx.doi.org/10.5755/j01.ms.24.4.19128</p