Herein, we have successfully synthesized
binary Ag2Se,
composite Ag0:Ag2Se, and ternary Cu+:Ag2Se through an ambient aqueous-solution-based approach
in a one-pot reaction at room temperature and atmospheric pressure
without involving high-temperature heating, multiple-processes treatment,
and organic solvents/surfactants. Effective controllability over phases
and compositions/components are demonstrated with feasibility for
large-scale production through an exquisite alteration in reaction
parameters especially pH for enhancing and understanding thermoelectric
properties. Thermoelectric ZT reaches 0.8–1.1
at near-room-temperature for n-type Ag2Se and Cu+ doping further improves to 0.9–1.2 over a temperature range
of 300–393 K, which is the largest compared to that reported
by wet chemistry methods. This improvement is related to the enhanced
electrical conductivity and the suppressed thermal conductivity due
to the incorporation of Cu+ into the lattice of Ag2Se at very low concentrations (x%Cu+:Ag2Se, x = 1.0, 1.5, and 2.0).
DNA sequencing has allowed for the discovery of the genetic cause for a considerable number of diseases, paving the way for new disease diagnostics. However, due to the lack of clinical samples and records, the molecular cause for rare diseases is always hard to identify, significantly limiting the number of rare Mendelian diseases diagnosed through sequencing technologies. Clinical phenotype information therefore becomes a major resource to diagnose rare diseases. In this article, we adopted both a phenotypic similarity method and a machine learning method to build four diagnostic models to support rare disease diagnosis. All the diagnostic models were validated using the real medical records from RAMEDIS. Each model provides a list of the top 10 candidate diseases as the prediction outcome and the results showed that all models had a high diagnostic precision (≥98%) with the highest recall reaching up to 95% while the models with machine learning methods showed the best performance. To promote effective diagnosis for rare disease in clinical application, we developed the phenotype-based Rare Disease Auxiliary Diagnosis system (RDAD) to assist clinicians in diagnosing rare diseases with the above four diagnostic models. The system is freely accessible through http://www.unimd.org/RDAD/.
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