Reducing carbon footprints is an essential requirement in the chemical industry. Researchers are concentrating on creating sustainable products derived from renewable resources or waste materials. Polyethylene terephthalate (PET) waste significantly contributes to carbon footprints; the chemical recycling of PET waste possesses extensive opportunities within the chemical sector. For instance, PET waste can be transformed into valuable alkyd resin, which is utilized in the production of oil-based paints. This research work focuses on the synthesis of long oil alkyd resin using recycled polyethylene terephthalate (rPET). As the incorporation of rPET in alkyd resin has several limitations such as two-step synthesis, inability to produce long oil alkyd, and long drying time. To overcome these limitations, a novel synthesis route has been devised to produce long oil alkyd resin. In this study, three long oil alkyd resins were synthesized, each containing varying amounts of rPET. The presence of rPET in the alkyd resins was confirmed by spectroscopic techniques. To assess the impact of rPET content on alkyd resin, physicochemical properties, performance testing, and instrumental analysis have been conducted. A comparison is made between these resins and the benchmark long oil alkyd resin, and the results are discussed. Furthermore, to synergize the coating applications, viscoelastic behavior and mechanical properties of the dried films were assessed, including exterior durability. Alkyd resin containing 8% rPET shows performance properties that are comparable to the benchmark alkyd resin. This alkyd requires 80 min for surface drying and 4 h to reach a tack-free state. It has a gloss value of 86 at 20° angle. The scratch hardness is recorded as 900 g, while the gloss retention stands at 88.34% following 240 h of QUV exposure. This novel synthesis route helps to incorporate the rPET in the alkyd backbone with reduced carbon footprint to meet the goal of sustainability and the circular economy.
Precision Archaeology leverages advanced technologies, such as unmanned aircraft systems (UAS), for documenting archaeological sites with high spatial resolution and accuracy. This paper presents a reproducible RGB–multispectral (MS) image-fusion workflow for Precision Archaeology, combining PPK-based georeferencing with quantitative assessment of product accuracy and spectral preservation. Within this framework, the repeatability of the results produced by the UAS data fusion method confirms its reliability and establishes it as a valuable documentation tool. Among the experimental applications conducted to date, this paper adds two more: the Sanctuary of Eukleia at Aigai and the funerary ensemble in the Philippi plain, where Aerial Remote Sensing was performed using a UAS equipped with a Post-Processed Kinematic (PPK)–Global Navigation Satellite System (GNSS) receiver. A ground-based GNSS receiver was used to measure control points (CPs) and the base point used to correct the coordinates of the UAS image acquisition centers using the PPK method. For both archaeological sites, RGB and MS stereoscopic images were acquired from flight altitudes of 60 and 100 m, respectively, achieving an overall theoretical solution accuracy of under 2 cm. Digital surface models (DSMs) were generated with spatial resolutions of approximately 2 cm for the RGB and about 14 cm for the MS images, along with orthophotomosaics with spatial resolutions of roughly 1 cm for RGB and 7 cm for MS images. In the final stage, image fusion of the RGB and MS orthophotomosaics was applied, improving the spatial resolution of the MS orthophotomosaics from 7 cm to approximately 1 cm, while simultaneously preserving nearly all the original spectral information in the new fused images. Spectral preservation was quantified via band-wise correlation between the original MS and fused images (≈0.99 average for the Philippi dataset; ≈0.85 average for Aigai, likely influenced by a ~45 min RGB–MS acquisition gap and corresponding shadow/illumination differences). These new images can be used for classification purposes, enabling the identification of different materials and the detection of archaeological feature pathology with optimal spatial resolution and accuracy.
Unmanned Aerial Vehicle (UAV) photogrammetry enables high-resolution mapping and 3D reconstruction, yet operational and processing costs often scale rapidly with conservative mission designs (e.g., high overlap and redundant geometries). This paper presents an experimentally validated, cost-aware network-design study that quantifies cost–quality trade-offs in urban UAV photogrammetry. Five mission strategies—reduced sidelap with increased endlap, cross-flight compensation, partial high-overlap calibration, multi-altitude acquisition, and oblique cross-flight integration—are evaluated using a controlled experimental campaign over two urban test areas (2 × 20 ha), comprising 98 test blocks with overlaps ranging from 60% to 95%, sidelap from 20% to 80%, image counts from 70 to 2961, 7 check points, 15–17 ground control points, and GSD values between 2.6 cm and 4.6 cm, including nadir, oblique, cross-flight, and multi-altitude imagery. Each configuration is assessed using three indicators: (i) cost (flight and processing cost proxies), (ii) completeness, quantified by the number of reconstructed tie points, and (iii) accuracy, defined as a combined image–ground error at check points. Results show that cost reductions of over 50% in both flight and processing proxies can be achieved under the tested conditions while maintaining checkpoint accuracy comparable to a high-overlap reference configuration, provided that reduced overlap is compensated by stronger network geometry (e.g., cross-flight and/or oblique views). The analysis highlights product-dependent recommendations: vector map (MAP) generation can remain reliable even with very low sidelap (down to approximately 20%) when supported by adequate longitudinal overlap, whereas ortho-image mosaic (OIM) production requires at least moderate overlap in both directions (typically ≥60% endlap and sidelap) to ensure radiometric and geometric consistency. In contrast, dense 3D mesh reconstruction demands substantially stronger network geometry, including cross-flight and oblique imagery in addition to nadir views, with overlap levels exceeding 60% and preferably approaching 80%. These findings provide practical mission-planning guidelines that support efficient autonomous and semi-autonomous UAV mapping workflows.
Solvents dominate mass input, energy demand, and environmental impact in pharmaceutical manufacturing, yet solvent selection and recovery are often evaluated using fragmented or non-comparable metrics. Here, we present a solvent-centric sustainability framework that integrates mass-based indicators with life-cycle and energy metrics to enable transparent comparison of conventional and redesigned solvent systems. The framework harmonizes Process Mass Intensity (PMI), circular PMI (cPMI), Global Warming Potential (GWP), and Cumulative Energy Demand (CED) within consistent cradle-to-gate system boundaries, supported by literature-derived data, machine-learning (ML) models, and digital-twin–based sustainability assessment tools. The methodology is demonstrated using Sertraline as a representative solvent-intensive active pharmaceutical ingredient (API). A simplified, literature-based synthesis route contextualizes solvent use across key reaction and isolation steps. Targeted solvent substitutions—most notably replacement of tetrahydrofuran, chlorinated solvents, and dipolar aprotic media with 2-methyltetrahydrofuran and ethanol-based systems—are evaluated alongside enhanced solvent recovery and catalytic hydrogenation. Relative to the solvent-dominant subsequence of the synthesis (PMI ≈ 78 kg·kg−1 API), for which detailed solvent mass-balance data are available, the redesigned solvent strategy reduces PMI to approximately 45 kg·kg−1 API, achieves a cPMI of 6–10 at ≥80% solvent recovery, and consistently decreases GWP and CED. By explicitly mapping solvent redesign outcomes to the 12 Principles of Green Chemistry, this study demonstrates how solvent-focused interventions, supported by predictive digital tools with excellent agreement between modelled and empirical trends, can deliver substantial sustainability improvements without modifying the underlying synthetic route or relying on proprietary process data. While not intended as an industrial benchmark, the Sertraline case study illustrates how harmonized metrics, life-cycle thinking, and AI-enabled digital assessment can support evidence-based solvent selection and sustainability-oriented process development in API manufacturing.
This study investigates the need for the adoption of modern handloom tools, including jacquard and warping drums, and evaluates their impact on income generation, production efficiency, market reach, and women’s empowerment in rural areas of Udalguri District, Assam. A purposive sampling method was used to survey 50 households in total. The findings reveal that the jacquard and warping drums significantly reduced the time required for weaving, mitigating weather dependence and improving productivity. Consequently, beneficiaries reported increased income, leading to independent entrepreneurship. The marketing strategies employed included direct market linkage through Civil Society Organizations (CSOs), participation, and connection with buyers to expand market access. Types of products included Silk and Cotton, and most of the products were sold in local markets. Training initiatives have been conducted to enhance product quality and design diversity. Weavers, who previously worked with limited designs, have now adopted innovative patterns to boost product demand. The study underscores the pivotal role of CSOs in hand-holding support, development of marketing linkage, tracking systems, and development of community resource persons (CRPs) through cluster-based training programs. The modern handloom tools play a transformative role in enhancing productivity, income, and market access, while simultaneously empowering women and strengthening rural economies.
