ABSTRACT
Organic-inorganic hybrid perovskite solar cells (PSCs) has achieved the power conversion efficiency (PCE) of 25.2% in the last 10 years, and the PCE of inverted PSCs has reached >22%. The rapid enhancement has partly benefited from the employment of suitable hole transport layers. Especially, poly(3,4-ethenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) is one of the most widely used polymer hole transport materials in inverted PSCs, because of its high optical transparency in the visible region and low-temperature processing condition. However, the PCE and stability of PSCs based on pristine PEDOT:PSS are far from satisfactory, which are ascribed to low fitness between PEDOT:PSS and perovskite materials, in terms of work function, conductivity, film growth, and hydrophobicity. This paper summaries recent progress regarding to modifying/remedy the drawbacks of PEDOT:PSS to improve the PCE and stability. The systematically understanding of the mechanism of modified PEDOT:PSS and various characteristic methods are summarized here. This Review has the potential to guide the development of PSCs based on commercial PEDOT:PSS.
ABSTRACT
BACKGROUND: In many research studies, the identification of social determinants is an important activity, in particular, information about occupations is frequently added to existing patient data. Such information is usually solicited during interviews with open-ended questions such as "What is your job?" and "What industry sector do you work in?" Before being able to use this information for further analysis, the responses need to be categorized using a coding system, such as the Canadian National Occupational Classification (NOC). Manual coding is the usual method, which is a time-consuming and error-prone activity, suitable for automation. OBJECTIVE: This study aims to facilitate automated coding by introducing a rigorous algorithm that will be able to identify the NOC (2016) codes using only job title and industry information as input. Using manually coded data sets, we sought to benchmark and iteratively improve the performance of the algorithm. METHODS: We developed the ACA-NOC algorithm based on the NOC (2016), which allowed users to match NOC codes with job and industry titles. We employed several different search strategies in the ACA-NOC algorithm to find the best match, including exact search, minor exact search, like search, near (same order) search, near (different order) search, any search, and weak match search. In addition, a filtering step based on the hierarchical structure of the NOC data was applied to the algorithm to select the best matching codes. RESULTS: The ACA-NOC was applied to over 500 manually coded job and industry titles. The accuracy rate at the four-digit NOC code level was 58.7% (332/566) and improved when broader job categories were considered (65.0% at the three-digit NOC code level, 72.3% at the two-digit NOC code level, and 81.6% at the one-digit NOC code level). CONCLUSIONS: The ACA-NOC is a rigorous algorithm for automatically coding the Canadian NOC system and has been evaluated using real-world data. It allows researchers to code moderate-sized data sets with occupation in a timely and cost-efficient manner such that further analytics are possible. Initial assessments indicate that it has state-of-the-art performance and is readily extensible upon further benchmarking on larger data sets.
