Hasil untuk "Chemical technology"

Mehmet Zahid Malasli

Since cherries are a seasonal product, it is not possible to obtain them at all times of the year. Due to their high moisture content, they cannot be stored for long periods of time. For these reasons, the drying of sweet cherries is of great importance in preventing product losses and preserving market value. In this study, cherries were dried with different solar energy using passive (without fan; P1), active (with three fan speed; F1, F2, F3) and open (exposed to the sun) methods in order to extend the shelf life and provide access in all seasons. The kinetics of the drying processes, energy consumption parameters, thermophysical properties and their effects on color parameters were investigated. Drying rate in drying processes changed in the range of 6.09–13.98 × 10−4 g moisture/g dry matter min. It was determined that effective moisture diffusion values ranged between 1.43 × 10−8–9.62 × 10−9 m2/s. The highest average specific heat, thermal conductivity, thermal diffusivity and specific mass of the dried samples were obtained at activated type-F1 fan speed. The dry product closest to fresh according to all color values was determined in the open drying method. According to the results, it is recommended that solar drying at a single fan speed (F1) be prioritized as a promising approach for sweet cherry drying in future applications and studies, while further optimization of active systems can improve specific moisture extraction rate (SMER) and specific energy consumption (SEC) performance.

Dhruv Menon, Vivek Singh, Xu Chen et al.

Reticular chemistry has enabled the synthesis of tens of thousands of metal-organic frameworks (MOFs), yet the discovery of new materials still relies largely on intuition-driven linker design and iterative experimentation. As a result, researchers explore only a small fraction of the vast chemical space accessible to reticular materials, limiting the systematic discovery of frameworks with targeted properties. Here, we introduce Nexerra-R1, a building-block chemical language model that enables inverse design in reticular chemistry through the targeted generation of organic linkers. Rather than generating complete frameworks directly, Nexerra-R1 operates at the level of molecular building blocks, preserving the modular logic that underpins reticular synthesis. The model supports both unconstrained generation of low-connectivity linkers and scaffold-constrained design of symmetric multidentate motifs compatible with predefined nodes and topologies. We further combine linker generation with flow-guided distributional targeting to steer the generative process toward application-relevant objectives while maintaining chemical validity and assembly feasibility. The generated linkers are subsequently assembled into three-dimensional frameworks and are structurally optimized to produce candidate materials compatible with experimental synthesis. Using Nexerra-R1, we validate this strategy by rediscovering known MOFs and by proposing the experimental synthesis of a previously unreported framework, CU-525, generated entirely in silico. Together, these results establish a general inverse-design paradigm for reticular materials in which controllable chemical language modelling enables the direct translation from computational design to synthesizable frameworks.

Shuan Chen, Kye Sung Park, Taewan Kim et al.

Accurately predicting chemical reaction outcomes and potential byproducts is a fundamental task of modern chemistry, enabling the efficient design of synthetic pathways and driving progress in chemical science. Reaction mechanism, which tracks electron movements during chemical reactions, is critical for understanding reaction kinetics and identifying unexpected products. Here, we present Reactron, the first electron-based machine learning model for general reaction prediction. Reactron integrates electron movement into its predictions, generating detailed arrow-pushing diagrams that elucidate each mechanistic step leading to product formation. We demonstrate the high predictive performance of Reactron over existing product-only models by a large-scale reaction outcome prediction benchmark, and the adaptability of the model to learn new reactivity upon providing a few examples. Furthermore, it explores combinatorial reaction spaces, uncovering novel reactivities beyond its training data. With robust performance in both in- and out-of-distribution predictions, Reactron embodies human-like reasoning in chemistry and opens new frontiers in reaction discovery and synthesis design.

Seenivasan Hariharan, Sebastian Zur, Sachin Kinge et al.

We lay the foundation for a quantum algorithmic framework to analyse fixed-structure chemical reaction networks (CRNs) using quantum random walks (QRWs) via electrical circuit theory. We model perturbations to CRNs, such as, species injections that shift steady-state concentrations, while keeping the underlying species-reaction graph fixed. Under physically meaningful mass-action constraints, we develop quantum algorithms that (i) decide reachability of target species after perturbation, (ii) sample representative reachable species, (iii) approximate steady-state fluxes through reactions, and (iv) estimate total Gibbs free-energy consumption. Our approach offers new tools for analysing the structure and energetics of complex CRNs, and opens up the prospect of scalable quantum algorithms for chemical and biochemical reaction networks.

Manuel Palma Banos, Joel D. Kress, Rigoberto Hernandez et al.

A data-driven computational method is introduced to extract chemical reaction mechanisms from time series chemical concentration data. It is realized through the use of dynamic symbolic regression in which a sparse analytical form for a dynamical system is discoverable from the underlying data. We specifically develop the stoichiometrically-informed symbolic regression (SISR) method to address a standing challenge in complex chemical reaction networks: Given a time-series dataset of concentrations of several components, what is the mechanism and the associated rate constants? SISR finds the optimal mechanism, kinetic equations and rate constants by combining differential optimization with a genetic optimization approach that searches a symbolic space of possible reaction mechanisms. Use of SISR in several paradigmatic examples spanning linear and nonlinear reaction schemes results in excellent agreement between true and predicted mechanisms, including when the method is applied to noisy data. The advantages of a stoichiometrically-informed approach such as SISR to address reaction discovery is illustrated through comparison with the use of generic state-of-the-art data-driven approaches.

Engin Derya Gezer, Abdullah Uğur Birinci, Aydın Demir et al.

The primary aim of this work was to determine the effects of production parameters, such as wood species and timber strength classes, on some mechanical properties of cross-laminated timber (CLT) panels using artificial neural network (ANN) prediction models. Subsequently, using the models obtained from the analyses, the goal was to identify the optimum layer combinations of timber strength classes used in the middle and outer layers that would provide the highest mechanical properties for CLT panels. CLT panels made from spruce and alder timbers, as well as hybrid panels created from combinations of these two wood species, were produced. The strength classes of the timbers were determined non-destructively according to the TS EN 338 (2016) standard using an acoustic testing device. The bending strength and modulus of elasticity values of the CLT panels were determined destructively according to the TS EN 408 (2019) standard. According to ANN results, the optimum timber strength classes and layer combinations were determined for bending strength as C24-C27-C24 for spruce CLT, D18-D24-D18 for alder CLT, C30-D40-C30 and D18-C30-D18 for hybrid panels; and for modulus of elasticity, C22-C27-C22 for spruce, D35-D30-D35 for alder, C16-D24-C16, and D24-C24-D24 for hybrid panels.

Thais N. Guerrero, Ruth M. Fisher, Ademir A. Prata et al.

The beneficial reuse and recovery of biosolids is an attractive option instead of disposal. However, odour emissions present significant challenges to land application of biosolids, increasing operational costs and reducing community acceptance. This study aimed to assess the influence of conveying and storage conditions in wastewater treatment plants on the sensory impact from biosolids. For sensory assessment, samples of anaerobically digested biosolids were collected after centrifuge and during storage out-loading. The emissions were extracted over 15 days using a dynamic flux chamber and sensory analysis conducted using an ODP coupled to a TD-GC-MS. Odour descriptors and intensities (from 1 – weak to 4 – strong) were evaluated by expert panellists, providing insights into the sensory aspects of odour emissions. The ODP results showed variations in the number of occurrences, intensity and modified frequency of odour events across the stages of wastewater solids processing and laboratory storage. Conveying could potentially impact the release of volatile compounds due to the mechanical agitation that can aerate and disturb the structure and surface of the biosolids. On the other hand, storage can accelerate biological and chemical processes as a result of the development of anaerobic conditions leading to subsequent odour generation. The interplay between wastewater treatment processes and odour emissions is complex and requires targeted strategies. The application of sensorial analysis contributes to valuable insights into understanding and managing odour emissions in wastewater treatment plants, offering potential avenues for optimizing operational parameters to benefit biosolids reuse initiatives. Keywords: Wastewater sludge; Anaerobic digestion; Biosolids; Beneficial reuse; Land application; Gaseous emissions; Sensory emissions; Sensory analysis; Odour detection port.

Qingji Zhang, Xiaomei Chao, Yeman Tu et al.

Abstract This study investigates the relationship between labor values and two forms of envy—benign and malicious—as well as the potential mediating role of mindfulness using a mindfulness reperceiving model. Two thousand three hundred sixty three Chinese teenagers participated in a longitudinal study over an eight-month period, completing questionnaires measuring labor values, benign envy, malicious envy, and mindfulness. The cross-sectional data showed that labor values had an immediate negative effect on malicious envy, with mindfulness partially mediating this relationship. Additionally, labor values had an immediate positive effect on benign envy, but mindfulness did not mediate this relationship. Longitudinal data analysis revealed that the delayed effect of labor values on later benign/malicious envy was similar to its immediate effect. However, mindfulness only played a mediating role in the relationship between labor values and later malicious envy. Cross-gender stability was found in both the immediate effect model and the delayed effect model. Overall, this study sheds light on the influence of labor values on the development of social emotions and the potential mediating role of mindfulness in the Chinese cultural context.

Marianna Dedousi, Eirini-Maria Melanouri, Dimitris Karayannis et al.

Five agro-industrial residues, their corresponding spent mushroom substrates (SMS), commercial fresh and spent mushroom compost of Agaricus (SMC) and Pleurotus ostreatus (SMS GZ) cultivation, Pleurotus and Agaricus waste (stipes/mishappen mushrooms) were combined and re-utilized as novel substrates for the cultivation of P. ostreatus, P. eryngii and A. bisporus mushrooms. SMSs/ SMC were used up to 40% (treatment 1 and 2), while 20% of mushroom waste were added in fresh and spent substrates (treatment 3 and 4). The impact of different substrates on mycelial growth rate and biomass production was examined. Then, Pleurotus spp. were cultivated on the most promising substrates and essential cultivation aspects (earliness, total mushroom yield, biological efficiency-BE) and carposomes’ quality parameters (weight, morphological characteristics) were evaluated. Laccase and endoglucanase production by Pleurotus species were also determined at 50 and 100% of colonization stages. All species showed their fastest mycelial growth rate (up to 5 mm/day) on substrates consisted of SMC, whereas many combinations of species/substrate enhanced biomass production. The SMS GZ supplementation positively affected laccase activity; in the cultures of P. ostreatus and P. eryngii the highest values were 62,539 and 17,584 U/g d.w., respectively. On the contrary, small amounts of endoglucanase were produced (0.007 to 0.322 U/g d.w.); the greatest production was recorded for P. ostreatus at full colonization. Regarding fermentation in bags, significant amount of total mushroom yield was produced in all substrates and those with SMS GZ supported the fastest earliness period and the highest BE for both Pleurotus species. BE values ranged from 54 to 133% for P. ostreatus and from 53 to 121% for P. eryngii. Concerning morphological characteristics, mushroom waste addition seemed to affect them positively. The data included in this paper support the effective re-utilization of different types of SMS and mushroom waste for fungal mass and enzymes’ production and for new high quality Pleurotus spp. carposomes.

A. S. Zoricheva, E. A. Zvonova, L. S. Agapova et al.

INTRODUCTION. The current cell-based cartilage repair methods, such as autologous chondrocyte transplantation, are not sufficiently effective, and the surgery is painful and traumatic. Therefore, there is a need for a more effective cell therapy product with a minimally invasive surgical procedure for its implantation into the patient.AIM. This study aimed to develop a manufacturing technology for the production of an autologous cell-based medicinal product (CBMP) comprising three-dimensional structures (3D-spheroids) based on chondrocytes isolated from the patient’s cartilage tissue, as well as to evaluate its clinical efficacy.MATERIALS AND METHODS. Autologous chondrocytes isolated from the patient’s cartilage biopsy were propagated in monolayer culture to obtain the required number of cells. Subsequently, the chondrocytes were cultivated on plates with a non-adhesive coating to form 3D spheroids. All CBMP production steps were performed under aseptic conditions in cell culture isolators. The authors used phase-contrast microscopy and immunohistochemical staining with specific fluorescence-labelled antibodies to characterise chondrocyte phenotypes at different stages of cultivation. Genetic stability was controlled by karyotyping. The efficacy of Easytense® was evaluated in a clinical trial using specialised functional tests and the Magnetic Resonance Observation of Cartilage Repair Tissue (MOCART) score. The primary efficacy endpoint was a change in the overall score on the Knee Injury and Osteoarthritis Outcome Score (KOOS).RESULTS. A manufacturing technology without using animal sera, growth factors, cytokines, or other additives was developed for the production of the autologous CBMP Easytense®. Karyological data confirmed that the chondrocytes retained genetic stability for 3 passages in monolayer culture. When cultured as 3D spheroids, the chondrocytes produced cartilage extracellular matrix proteins (type II collagen, aggrecan), thus acquiring the ability to repair damaged cartilage. The clinical trial demonstrated a statistically significant improvement in knee cartilage 12 months after the transplantation of 3D spheroids derived from autologous chondrocytes. The mean change in the overall KOOS score was 23.8±15.9.CONCLUSIONS. The clinical trial results indicate that Easytense® is highly effective for cartilage repair. Based on these results, the CBMP has been granted marketing authorisation and introduced into clinical practice in the Russian Federation. Easytense® has the potential to replace endoprosthetics and expensive surgeries abroad.

Wenhao Gao, Shitong Luo, Connor W. Coley

We introduce SynFormer, a generative modeling framework designed to efficiently explore and navigate synthesizable chemical space. Unlike traditional molecular generation approaches, we generate synthetic pathways for molecules to ensure that designs are synthetically tractable. By incorporating a scalable transformer architecture and a diffusion module for building block selection, SynFormer surpasses existing models in synthesizable molecular design. We demonstrate SynFormer's effectiveness in two key applications: (1) local chemical space exploration, where the model generates synthesizable analogs of a reference molecule, and (2) global chemical space exploration, where the model aims to identify optimal molecules according to a black-box property prediction oracle. Additionally, we demonstrate the scalability of our approach via the improvement in performance as more computational resources become available. With our code and trained models openly available, we hope that SynFormer will find use across applications in drug discovery and materials science.

Wenjiao Zai, Lisha Yan

Unmanned Aerial Vehicle (UAV) inspection of transmission channels in mountainous areas is susceptible to non-homogeneous fog, such as up-slope fog and advection fog, which causes crucial portions of transmission lines or towers to become fuzzy or even wholly concealed. This paper presents a Dual Attention Level Feature Fusion Multi-Patch Hierarchical Network (DAMPHN) for single image defogging to address the bad quality of cross-level feature fusion in Fast Deep Multi-Patch Hierarchical Networks (FDMPHN). Compared with FDMPHN before improvement, the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) of DAMPHN are increased by 0.3 dB and 0.011 on average, and the Average Processing Time (APT) of a single picture is shortened by 11%. Additionally, compared with the other three excellent defogging methods, the PSNR and SSIM values DAMPHN are increased by 1.75 dB and 0.022 on average. Then, to mimic non-homogeneous fog, we combine the single picture depth information with 3D Berlin noise to create the UAV-HAZE dataset, which is used in the field of UAV power assessment. The experiment demonstrates that DAMPHN offers excellent defogging results and is competitive in no-reference and full-reference assessment indices.

Jaume Gomez-Caturla, Nestor Montanes, Luis Quiles-Carrillo et al.

The present work reports on the development of environmentally friendly, completely biodegradable wood plastic composites based on polylactide (PLA) and tangerine peel flour (TPF), plasticized by α-terpinyl acetate (TA). The TPF varied in the 10–30 wt% while the PLA to TA (wt%/wt%) was set to 4 (i.e., 25 wt% TA plasticizer was added with regard to the PLA wt%). The developed composites were processed by extrusion and injection molding. The composites presented excellent elongation at break, achieving values of 300% for the PLA+TA sample. Elongation at break values of 200% for the PLA composite with 10 wt% TPF and plasticized with TA were obtained. Those results were confirmed by the appearance of filament-like structures observed in field emission scanning electron microscopy images. Differential scanning calorimetry and dynamic mechanical thermal analysis revealed a remarkable decrease in the glass transition temperature of PLA as a result of the plasticizing effect of TA. Glass transition was reduced from 63°C down to 41°C approximately. This implied an increase in the ductility of the material. The samples with TPF exhibited a dark brown color, making them perfect for wood plastic composite applications. Water contact angle results show that TA and TPF change the wetting properties of the obtained composites. A general decrease in the water contact angle was observed with the addition of TPF and TA. Finally, disintegration tests proved that the developed composites are fully biodegradable. All the samples except for neat PLA achieved 100% disintegration in controlled compost soil conditions after 5 weeks, while neat PLA reached complete disintegration in 6 weeks.

Serena Lima, Antonino Biundo, Giuseppe Caputo et al.

As known, microalgae are an appealing source of chemicals and high-value compounds which find application in nutraceuticals, cosmetics and pharmaceutics. Fatty acids (FA), in particular, have drawn attention to the possibility of employing them as a source of biodiesel alternatively to fossil fuels. In addition, several lipid derivatives have been found in microalgae and may be employed in several biotechnological applications. Hydroxy fatty acids can be substrates for several industrial applications thanks to their functionalization, which increases their reactivity and, for this reason, can be used as functional building blocks to produce a multitude of bio-based materials. Recently, a promising method for the chemical modification of unsaturated-FAs (U-FA) has appeared. In fact, U-FA may be modified by members of the hydratase enzyme family to produce saturated and unsaturated hydroxy fatty acids with high stereo- and regio-selectivity. These enzymes are able to introduce a water molecule to the double bond present in the free fatty acids (FFA) Oleic Acid (OA), Linoleic Acid (LA), producing 10-hydroxy fatty acids (10-hydroxy-FAs). Furthermore, the carbohydrate component of the microalgal biomass may be converted into furfuryl compounds and, in particular in 5-hydroxyl methyl furfural (5-HMF). This is one of the chemical bio-compound different from petroleum-derived ones with the highest added value and may be obtained through lignocellulosic biomasses or hexoses sugars through acid catalysis. It is defined platform molecule because it is the precursor of several compounds for the chemical industry. In this work, we aimed to optimize a circular bioprocess by performing, starting from the same biomass, two different processes: the biotransformation of microalgal FFAs through the employment of a genetically modified E. coli on one side, and the conversion of the remaining biomass in furfuryl products. The first process allowed the production of very interesting lipid derivatives with biotechnological applications, including 10 hydroxy-stearic acid and 10-hydroxy-octadecenoic acid. The second process was obtained through heterogeneous catalysis based on niobium phosphate. This procedure represents a high-innovative application of microalgal biomass and allows the simultaneous exploitation of FAs and carbohydrates. This may result in an increase in the commercial value of microalgal biomass.

Yanggeun Joo, Eunji Hwang, Heemyoung Hong et al.

Abstract Memory devices are an essential part of modern electronics. Efforts to move beyond the traditional “read” and “write” of digital information in volatile and non‐volatile memory devices are leading to the rapid growth of neuromorphic technology. There is a growing demand for memory devices with continuous memory states with various retention times and greater integration density with more energy‐efficient mechanisms. Two types of memory devices (i.e., non‐volatile digital memory and neuro‐synaptic devices) have been extensively investigated with emerging materials. Among numerous materials for such memory devices, in this review, the authors focus on 2D ferroelectric materials for promising memory and synaptic devices. Three types of memory devices based on 2D ferroelectric materials are classified and discussed here: 1) ferroelectric gating of semiconducting channels, 2) active ferroelectric channels, and 3) ferroelectric tunnel junction devices. It is known that atomically thin geometry competes with ferroelectricity, which can degrade the quality of the devices based on atomically thin ferroelectric materials. Various efforts to resolve the fundamental issue with emerging 2D ferroelectric materials and how they can be used as a critical element for memory and synaptic devices are surveyed.

Tanvira Malek, Mangilal Choudhary

The agriculture sector has many issues such as reductions of agricultural lands, growing population, health issues arising due to the use of synthetic fertilizers and pesticides, reduction in soil health due to extreme use of synthetic chemicals during farming, etc. The quality and quantity of foods required for living things are affected by many factors like scarcity of nutrient-rich soils, lack of suitable fertilizers, harmful insects and bugs, climate change, etc. There is a requirement to supply the proper nutrients to plants/crops for obtaining a high crop yield. Synthetic chemical fertilizers provide nutrients (macro and micro) to plants for their growth and development but the excess use of them is not good for a healthy lifestyle as well as for the environment. In recent years, non-thermal plasma (NTP) is considered as an advanced green technology for enhancing productivity in agriculture sectors. In this report, we provided the details of nutrients and their functions in the growth and development of plants/crops. How plasma technology can resolve many future challenges in the agriculture sector is discussed in detail. A few experiments on seed germination and plant growth (root and shoot length) were performed in the laboratory to explore the effect of plasma-activated water on the growth and development of plants. These primary results demonstrate the great potential of plasma technology in the agriculture sector.

Stefan Hödl, William Robinson, Yoram Bachrach et al.

Explainability techniques are crucial in gaining insights into the reasons behind the predictions of deep learning models, which have not yet been applied to chemical language models. We propose an explainable AI technique that attributes the importance of individual atoms towards the predictions made by these models. Our method backpropagates the relevance information towards the chemical input string and visualizes the importance of individual atoms. We focus on self-attention Transformers operating on molecular string representations and leverage a pretrained encoder for finetuning. We showcase the method by predicting and visualizing solubility in water and organic solvents. We achieve competitive model performance while obtaining interpretable predictions, which we use to inspect the pretrained model.

Eduardo Soares, Emilio Vital Brazil, Karen Fiorela Aquino Gutierrez et al.

We present a novel multimodal language model approach for predicting molecular properties by combining chemical language representation with physicochemical features. Our approach, MULTIMODAL-MOLFORMER, utilizes a causal multistage feature selection method that identifies physicochemical features based on their direct causal effect on a specific target property. These causal features are then integrated with the vector space generated by molecular embeddings from MOLFORMER. In particular, we employ Mordred descriptors as physicochemical features and identify the Markov blanket of the target property, which theoretically contains the most relevant features for accurate prediction. Our results demonstrate a superior performance of our proposed approach compared to existing state-of-the-art algorithms, including the chemical language-based MOLFORMER and graph neural networks, in predicting complex tasks such as biodegradability and PFAS toxicity estimation. Moreover, we demonstrate the effectiveness of our feature selection method in reducing the dimensionality of the Mordred feature space while maintaining or improving the model's performance. Our approach opens up promising avenues for future research in molecular property prediction by harnessing the synergistic potential of both chemical language and physicochemical features, leading to enhanced performance and advancements in the field.

Bhaskar J. Choudhury, Vijayanand S. Moholkar

Commercial application of supercapacitors (SCs) requires high mass loading electrodes simultaneously with high energy density and long cycle life. Herein, we have reported a ternary multi-walled carbon nanotube (MWCNT)/MnO2/reduced graphene oxide (rGO) nanocomposite for SCs with commercial-level mass loadings. The ternary nanocomposite was synthesized using a facile ultrasound-assisted one-pot method. The symmetric SC fabricated with ternary MWCNT/MnO2/rGO nanocomposite demonstrated marked enhancement in capacitive performance as compared to those with binary nanocomposites (MnO2/rGO and MnO2/MWCNT). The synergistic effect from simultaneous growth of MnO2 on the graphene and MWCNTs under ultrasonic irradiation resulted in the formation of a porous ternary structure with efficient ion diffusion channels and high electrochemically active surface area. The symmetric SC with commercial-level mass loading electrodes (∼12 mg cm−2) offered a high specific capacitance (314.6 F g−1) and energy density (21.1 W h kg−1 at 150 W kg−1) at a wide operating voltage of 1.5 V. Moreover, the SC exhibits no loss of capacitance after 5000 charge−discharge cycles showcasing excellent cycle life.

Max Pascher, Kirill Kronhardt, Til Franzen et al.

Nowadays, robots are found in a growing number of areas where they collaborate closely with humans. Enabled by lightweight materials and safety sensors, these cobots are gaining increasing popularity in domestic care, where they support people with physical impairments in their everyday lives. However, when cobots perform actions autonomously, it remains challenging for human collaborators to understand and predict their behavior, which is crucial for achieving trust and user acceptance. One significant aspect of predicting cobot behavior is understanding their perception and comprehending how they “see” the world. To tackle this challenge, we compared three different visualization techniques for Spatial Augmented Reality. All of these communicate cobot perception by visually indicating which objects in the cobot’s surrounding have been identified by their sensors. We compared the well-established visualizations <i>Wedge</i> and <i>Halo</i> against our proposed visualization <i>Line</i> in a remote user experiment with participants suffering from physical impairments. In a second remote experiment, we validated these findings with a broader non-specific user base. Our findings show that <i>Line</i>, a lower complexity visualization, results in significantly faster reaction times compared to <i>Halo</i>, and lower task load compared to both <i>Wedge</i> and <i>Halo</i>. Overall, users prefer <i>Line</i> as a more straightforward visualization. In Spatial Augmented Reality, with its known disadvantage of limited projection area size, established off-screen visualizations are not effective in communicating cobot perception and <i>Line</i> presents an easy-to-understand alternative.