PEDOT: PSS) has several shortcomings, such as an easily corroded indium-tin-oxide (ITO) interface at elevated temperatures and induced electrical inhomogeneity. Herein, we propose solution-processed nitrogen-doped graphene oxide nanoribbons (NGONRs) as a hole transport layer (HTL) in perovskite solar cells, replacing the conducting polymer
PEDOT: PSS. The conversion efficiency of NGONR-based perovskite solar cells has outperformed a control device constructed using
PEDOT: PSS. Moreover, our proposed NGONR-based devices also demonstrate a negligible current hysteresis along with improved stability. This work provides an effective route for substituting
PEDOT: PSS as the effective HTL.
MATERIALS AND METHODS: A literature search was carried out to gather eligible studies from the following widely sourced electronic databases such as Scopus, PubMed and Google Scholar using the combination of the following keywords: AD, MRS, brain metabolites, deep learning (DL), machine learning (ML) and artificial intelligence (AI); having the aim of taking the readers through the advancements in the usage of MRS analysis and related AI applications for the detection of AD.
RESULTS: We elaborate on the MRS data acquisition, processing, analysis, and interpretation techniques. Recommendation is made for MRS parameters that can obtain the best quality spectrum for fingerprinting the brain metabolomics composition in AD. Furthermore, we summarise ML and DL techniques that have been utilised to estimate the uncertainty in the machine-predicted metabolite content, as well as streamline the process of displaying results of metabolites derangement that occurs as part of ageing.
CONCLUSION: MRS has a role as a non-invasive tool for the detection of brain metabolite biomarkers that indicate brain metabolic health, which can be integral in the management of AD.