Research on photocatalytic materials has been a field in continuous expansion in the recent decades, as it is evidenced by the large number of articles published every year. So far, more than 190 different semiconductors have been assayed as suitable photocatalysts. To this figure, it is necessary to add the combinations with other functional materials or between different semiconductors, as well as their morphological modifications. Summing up the outcome of these different preparation strategies eventually leads to the enormous number of photocatalytic systems that have been reported in the scientific literature. Dealing with such an amount of information requires updated and educated guidance to select the most significant realizations, and it also calls for critical assessments on how the expectations are being fulfilled. This perspective article intends to assess the state of the art of photocatalysis with regard to materials and systems, considering the well-established results, but also the emerging aspects, and the envisaged new directions of this technology in the near future. In the first part, the most relevant achievements in this area, some of them already in the market while others still in development, will be reviewed according to the current understanding. The second part of the article is devoted to the most innovative and promising photocatalysts and related systems described in the open literature.

Raman spectroscopy is used in a wide variety of fields, and in a plethora of different configurations. Raman spectra of simple analytes can often be analyzed using univariate approaches and interpreted in a straightforward manner. For more complex spetral data such as time series or line profiles (1D), Raman maps (2D), or even volumes (3D), multivariate data analysis (MVDA) becomes a requirement. Even though there are some existing standards for creation, implementation, and validation of methods and models employed in industry and academics, further research and development in the field must contribute to their improvement. This review will cover, in broad terms, existing techniques as well as new developments for MVDA for Raman spectroscopic data, and in particular the use associated with instrumentation and data calibration. Chemometric models are often generated via fusion of analytical data from different sources, which enhances model discrimination and prediction abilities as compared to models derived from a single data source. For Raman spectroscopy, raw or unprocessed data is rarely ever used. Instead, spectra are usually corrected and manipulated, ¹ often by case-specific rather than universal methods. Calibration models can be used to characterize qualitatively and/or quantitatively samples measured with the same instrumentation that was used to create the model. However, regular validation is required to ensure that aging or incorrect maintenance of the instrument does not alter the model’s predictions, particularly when applied in regulated fields such as pharmaceuticals. Furthermore, a model transfer may be required for different reasons, such as replacement or significant repair of the instrumentation. Modeling can also be used to consistently harmonize Raman spectroscopic data across several instrumental designs, accounting for variations in the resulting spectrum induced by different components. Data for Raman harmonization models should be processed in a protocolled manner, and the original data accessible to allow for model reconstruction or transfer when new data is added. Important processing steps will be the calibration of the spectral axes and instrument dependent effects, such as spectral resolution. In addition, data fusion and model transfer are essential for allowing new instrumentation to build on existing models to harmonize their own data. Ideally, an open access database would be created and maintained, for the purpose of allowing for continued harmonization of new Raman instruments using an outlined and accepted protocol.