This review explores perovskite crystallization in scalable deposition techniques, including blade, slot-die, spray coating, and inkjet printing.

The textile and apparel industry faces significant sustainability challenges due to the high volume of waste it generates and the limitations of current recycling systems. Automation in textile waste management has emerged as a promising solution to enhance material recovery through accurate and efficient sorting. This systematic literature review, conducted using the PRISMA-guided PSALSAR methodology, examines recent advancements in computer-based sorting technologies applied in textile recycling. This study identifies and evaluates major technological methods often integrated with machine learning, deep learning, or computer vision models. The strengths and limitations of these approaches are discussed, highlighting their impact on classification accuracy, reliability, and scalability. This review emphasizes the need for further research on blended fiber detection, data availability, and hybrid models to advance automated textile waste management and support a sustainable circular economy.

Purpose: The purpose of this study is to identify US textile and apparel (TAP) products and categories that demonstrate export comparative advantage and their respective country destinations.Design/methodology/approach: Revealed comparative advantage (RCA) and normalized revealed comparative advantage (NRCA) in the long term (1996-2016) and short term (2010-2016) using two-digit Harmonized Commodity Description and Coding System codes (HS codes) provide an insight into export advantage of TAP products. Non-parametric rank correlation (i.e. Spearman rank correlation) tests consistency between RCA and NRCA. Subsequently, NRCA using four-digit HS codes identify textile sub-categories with export comparative advantage. Bilateral trade data identify export destinations for sub-categories that indicate US export comparative advantage in the short term.Findings: Aggregated product-level comparison of RCA and NRCA ranking indicates a consistent positive correlation between the two indices. The disaggregated findings reveal cotton fiber HS5201, artificial filament tow HS5502, non-wovens HS5603, cotton yarn HS5205, carpet and other floor coverings HS5703 and worn clothing HS6309 as sources of US TAP export advantage.Originality/value: This research provides a comprehensive, current analysis of the US competitive position within the TAP global environment. Incorporation of NRCA into the study’s design demonstrates the flexibility of this measure in a new industry context.

This review discusses the impact of various laminatiion methods on the photovoltaic characteristics and stability of perovskite and organic photovoltaics as well as the future challenges toward roll-to-roll production.

The COVID-19 pandemic is a public health crisis that has triggered a global economic recession. We investigated COVID-19’s effect on firm performance through the case of the U.S. textile and apparel industry and showed that textile and apparel firms experienced a sharp decline in firm performance following the COVID-19 outbreak. Specifically, the textile and apparel industry has been affected more adversely than have other industries, including transportation, entertainment, and hospitality. Furthermore, COVID-19’s adverse effect on firm performance in the textile and apparel industry is greater than that of the 9/11 terrorist attacks and the 2008 recession. We additionally found that the Coronavirus Aid, Relief, and Economic Security (CARES) Act benefited the textile and apparel industry significantly more than other industries immediately following its announcement. The empirical findings of this study contribute to the existing literature by providing insight into the textile and apparel firms’ financial performance during the COVID-19 outbreak.