Low Efficiency Research Articles

Digital evolution and twin miracle of sugarcane breeding

ContextSugarcane, as an important economic crop, faces challenges such as long breeding cycles, low genetic improvement efficiency, and complex breeding operations. MethodIn order to address these challenges and improve the economic benefits of sugarcane breeding, this paper proposes an innovative smart sugarcane breeding system driven by artificial intelligence (AI), blockchain and digital twin technologies. ResultsThe system integrates these technologies within a Human-Cyber-Physical System framework to offer a more efficient, secure, and smart strategy for sugarcane breeding. Firstly, AI processes extensive genetic and phenotypic data to enable precise prediction and optimization of sugarcane traits, resulting in shortened breeding cycles and enhanced efficiency and accuracy in selecting elite sugarcane varieties. Secondly, blockchain technology ensures the security and traceability of breeding data, enhancing the reliability and integrity of the breeding process. Thirdly, digital twin technology enables the real-time circulation of lifelike representations of real-world data among breeding-related workers. The system architecture consists of three layers: a physical layer for data collection, a cyber layer responsible for data analysis, storage and circulation managed by AI, blockchain and digital twin, and a human layer comprised of breeders and stakeholders. This multi-layered approach allows for sophisticated interaction and collaboration between the physical and digital realms, enhancing decision-making and breeding outcomes. ConclusionTaken together, the system utilizes AI, blockchain, and digital twin technologies to support sugarcane breeding, offering a promising solution to overcome the limitations of traditional methods and establish a more sustainable and profitable sugarcane breeding system.

Hormetic responses to cadmium exposure in wheat seedlings: insights into morphological, physiological, and biochemical adaptations.

Cadmium is commonly recognized as toxic to plant growth, low-level Cd has promoting effects on growth performance, which is so-called hormesis. Although Cd toxicity in wheat has been widely investigated, knowledge of growth response to a broad range of Cd concentrations, especially extremely low concentrations, is still unknown. In this study, the morphological, physiological, and biochemical performance of wheat seedlings to a wide range of Cd concentrations (0-100 µΜ) were explored. Low Cd treatment (0.1-0.5µM) improved wheat biomass and root development by enhancing the photosynthetic system and antioxidant system ability. Photosynthetic rate (Pn) was improved by 5.72% under lower Cd treatment (1 µΜ), but inhibited by 6.05-49.85% from 5 to 100 µΜ. Excessive Cd accumulation induced oxidative injury manifesting higher MDA content, resulting in lower photosynthetic efficiency, stunted growth, and reduction of biomass. Further, the contents of ascorbate, glutathione, non-protein thiols, and phytochelatins were improved under 5-100 µΜ Cd treatment. The ascorbate peroxidase activity in the leaf showed a hormetic dose-response characteristic. Correlation analysis and partial least squares (PLS) results indicated that antioxidant enzymes and metabolites were closely correlated with Cd tolerance and accumulation. The results of the element network, correlation analysis, and PLS showed a crucial role for exogenous Cd levels in K, Fe, Cu, and Mn uptake and accumulation. These results provided a deeper understanding of the hormetic effect of Cd in wheat, which would be beneficial for improving the quality of hazard and risk assessments.

Hierarchical design of carbon nanofibers wrapped antimony selenide nanorods with heteroatom-doped carbon quantum dots as efficient anode material of high-performance sodium-ion batteries

Researchers have focused on sodium-ion batteries (SIBs) due to low precursor costs and abundant reserves. However, low efficiency of anode materials hinders SIB commercialization, mainly due to large ionic radius and slow kinetics of Na+. Metal selenides such as Sb2Se3 have regarded as promising anode materials of SIBs due to high theoretical capacities. Despite this, the practical application has been limited by the poor rate capability, conductivity, and stability. In this study, one-dimensional Sb2Se3 nanorods and nitrogen-doped carbon quantum dots (N-CQDs) combined with carbon nanofibers (CNFs) have been developed as an effective anode material of SIBs. The uniform distribution of N-CQDs in the Sb2Se3 coated CNFs (Sb2Se3/CNFs) anode results in the highest reversible capacity of 647.9 mAhg−1 at 0.05 Ag-1. Moreover, the Sb2Se3 and N-CQDs coated CNFs (Sb2Se3@N-CQDs/CNFs) anode presents a good rate performance and retains a high reversible capacity of 444.7 mAhg−1 at a high current density of 0.4 Ag-1. Additionally, the Sb2Se3@N-CQDs/CNFs anode demonstrates excellent stability, retaining 78.8% of the initial capacity after 100 cycles. The improved electrochemical performance is attributed to low volume expansion and good conductivity due to carbon coating and addition of N-CQDs. Galvanostatic intermittent titration technique is also used to examine Na+ diffusion coefficients.

Network traffic prediction based on PSO-LightGBM-TM

Network traffic prediction is critical in wireless network management by allowing a good estimate of the traffic trend, which is also an important approach for detecting traffic anomalies in order to enhance network security. Deep-learning-based method has been widely adopted to predict network traffic matrix (TM) though with the main drawbacks in high complexity and low efficiency. In this paper, we propose a traffic prediction model based on Particle Swarm Optimization (PSO) and LightGBM (PSO-LightGBM-TM), which optimizes the LightGBM parameters for each network flow by PSO so that LightGBM can adapt to each of the network traffic flow. Compared with existing commonly used deep learning models, our model has a more straightforward structure and yet outperforms existing deep learning models. Sufficient comparison tests on three real network traffic datasets, Abilene, GÉANT, and CERNET have been conducted, and the results show that our model provides more accurate results and higher prediction efficiency.

Bismuth nanosheets guided zinc deposition enabled long-life aqueous zinc-based flow batteries

The poor cycling durability and low Coulombic efficiency (CE) of zinc (Zn) anodes substantially restrict their further development, which should be attributed to the severe uneven plating and hydrogen evolution reaction (HER) across the repeated plating/stripping processes. Herein, bismuth nanosheets decorated graphite felt (BiNS/GF) is constructed for Zn anode via galvanic replacement reaction. BiNS/GF affords more robust Zn nucleation sites, lower Zn nucleation overpotential and higher HER overpotential to guide even and dense Zn deposition. In addition, a total internal reflection imaging method is employed to detect micro-bubbles generated by HER on the electrode surface, revealing that BiNS prevents the formation of micro-bubbles and thus avoids dead Zn caused by unstable Zn deposition. As a proof of concept, BiNS/GF exhibits an average CE of 99.2 % in 300 cycles and a peak power density of 295.2 mW cm−2 at a current density of 380 mA cm−2 in aqueous neutral zinc-iron flow batteries. Therefore, it is reasonably anticipated that the BiNS/GF may emerge as suitable electrode for other aqueous zinc-based flow batteries.

Microcrystalline Engineering of Anthracite-Based Carbon Via Salt-Assisted Ball Milling for Enhanced Sodium Storage Performance.

Coal-based carbon material, characterized by abundant resources and low cost, has gained considerable interests as a promising anode candidate for sodium-ion batteries (SIBs). However, the coal-based carbon generally shows inferior Na-storage performance due to its highly-ordered microstructure with narrow interlayer spacing. Herein, a salt-assisted mechanical ball-milling strategy is proposed to disrupt the polycyclic aromatic hydrocarbon structure in anthracite molecules, thereby reducing the microcrystalline regularity of the derived carbon during following pyrolysis process. In addition, the induced C─O─C bonds during ball-milling process can alter the pyrolysis behavior of anthracite and restrain the formation of surface defects. Consequently, in contrast to pristine anthracite-based pyrolytic carbon, which exhibits a Na-storage capacity of 198.4 mAh g-1 with a low initial Coulombic efficiency (ICE) of 65.1%, the ball-milling modified carbon assisted by NaCl salt (NAC), with enhanced structural disordering and reduced surface defects, demonstrate significantly improved Na-storage capacity of 332.1 mAh g-1 and ICE value of 82.0%. The NAC electrode also realizes excellent cycle and rate performance, retaining a capacity of 196.0 mAh g-1 at 1 C after 1000 cycles. Furthermore, when coupled with NaNi1/3Fe1/3Mn1/3O2 cathode, the assembled Na-ion full cell deliveres an exceptional electrochemical performance, highlighting its promising prospect as high-performance anode for SIBs.

Model-free detection and quantitative assessment of micro short circuits in lithium-ion battery packs based on incremental capacity and unsupervised clustering

Timely diagnosis of micro short circuit (MSC) faults is crucial for ensuring the safe operation of lithium-ion battery energy storage systems. Existing diagnostic methods face limitations such as high dependency on battery models, difficulty in determining accurate diagnostic thresholds, or low computational efficiency. This work presents a model-free approach for the detection and quantitative assessment of MSCs in lithium-ion battery packs, with incremental capacity (IC) and unsupervised clustering. First, the IC is extracted from charging voltage data to effectively characterize MSC faults in lithium-ion batteries. Next, principal component analysis is used to map the high-dimensional feature space onto a two-dimensional plane to facilitate fault detection and result visualization. Then, an unsupervised clustering algorithm is employed for anomaly detection to identify MSC cells within the battery pack. For the detected MSC cells, a method based on the maximum charging voltage difference between adjacent cycles is designed to estimate the MSC resistance, quantitatively assessing the severity and evolution stage of the MSC. Experimental results show that the accuracy of MSC detection is 99.17 % and the minimum relative error of short-circuit resistance estimation is 1.20 %, which demonstrates the effectiveness and feasibility of the proposed method.

Improved Nitrate-to-Ammonia Electrocatalysis through Hydrogen Poisoning Effects.

Electrochemical conversion from nitrate to ammonia is a key step in sustainable ammonia production. However, it suffers from low productive efficiency or high energy consumption due to a lack of desired electrocatalysts. Here we report nickel cobalt phosphide (NiCoP) catalysts for nitrate-to-ammonia electrocatalysis that display a record-high catalytic current density of -702±7 mA cm-2, ammonia production rate of 5415±26 mmol gcat-1 h-1 and Faraday efficiency of 99.7±0.2 % at -0.3 V vs. RHE, affording the estimated energy consumption as low as 22.7 kWh kgammonia-1. Theoretical and experimental results reveal that these catalysts benefit from hydrogen poisoning effects under low overpotentials, which leave behind catalytically inert adsorbed hydrogen species (HI*) at Co-hollow sites and thereupon enable ideally reactive HII* at secondary Co-P sites. The dimerization between HI* and HII* for H2 evolution is blocked due to the catalytic inertia of HI* thereby the HII* drives nitrate hydrogenation timely. With these catalysts, the continuous ammonia production is further shown in an electrolyser with a real energy consumption of 18.9 kWh kgammonia-1.

Highly-selective Electrocatalytic Reduction of NO to NH3 using Cu Embedded WS2 Monolayer as Single-atom Catalyst: A DFT Study.

Electrocatalytic nitric oxide reduction reaction (NORR) is a promising method for generating NH3. However, developing a NORR catalyst for NH3 synthesis with low cost and high efficiency is still challenging. We here report a series of single-atom catalysts (SACs), designed by embedding nine different transition metals from Sc to Cu in S-vacant WS2 monolayer (TM@WS2), and investigate the electrocatalytic performance for NORR process using the dispersion-corrected density functional theory (DFT) calculations. Among them, Cu-based SACshows a strong binding to the WS2 surface and high selectivity towards NORR process, and also it greatly inhibits the competing hydrogen evolution reaction (HER). Through ab initio molecular dynamics (AIMD) simulations, the thermal stability of SAC is assessed and found no structure deformation even at 500 K temperature. All possible reactive pathways including distal and alternating mode at both N- and O-end configurations for NH3 production were explored. We predicted that the Cu@WS2 SAC exhibits remarkable catalytic activity and selectivity with lowest limiting potential of -0.41 V via the N-alternating pathway. Our study emphasize that the transition metal dichalcogenide (TMDC) based SACs are potential candidates for converting NO to NH3, and this opens a new avenue in designing NORR catalysts with high catalytic performance.

Empirical investigation and performance evaluation of flat-plate solar water heating systems: a comparative analysis with and without heat exchangers

Abstract Harnessing solar energy through flat-plate solar water heating systems offers an efficient and sustainable solution for residential water heating needs. This study aims to assess and compare the thermal efficiency of two flat-plate solar water heating systems designed for residential use: one with a heat exchanger and one without. Each system features a flat-plate collector with a dark absorber, a transparent cover to reduce heat losses, and a heat-transport fluid. A thin copper sheet with a selective coating forms the absorber. According to the standards of the Indian Ministry of New and Renewable Energy (MNRE), the evaluation revealed that the system without a heat exchanger achieved a higher efficiency (50 %) compared to the system with a heat exchanger (46 %). This device demonstrated notably lower thermal loss efficiency relative to previous studies, as evidenced by heat loss coefficients of 2.89, 2.66, 1.77, and 2.13 W/m2 k. Despite the higher efficiency of the system lacking a heat exchanger, the importance of heat exchangers in mitigating issues related to water hardness must be acknowledged. This study offers novel insights into the design and optimization of solar water heating systems, highlighting the trade-offs between efficiency and practical considerations.

The effect of chess on cognition: a graph theory study on cognitive data

ObjectivesWe aimed to advance our understanding of the effect of chess on cognition by expanding previous univariate studies with the use of graph theory on cognitive data. Specifically, we investigated the cognitive connectome of adult chess players.MethodWe included 19 chess players and 19 controls with ages between 39 and 69 years. Univariate analysis and graph theory included 27 cognitive measures representing multiple cognitive domains and subdomains. Graph analysis included global and nodal measures of integration, segregation, and centrality. We also performed an analysis of community structures to gain an additional understanding of the cognitive architecture of chess players.ResultsThe analysis of global graph measures showed that chess players had a higher local efficiency than controls at the cost of a lower global efficiency, which did not permeate segregation aspects of their connectome. The nodal graph measures showed that executive/attention/processing speed and visuoconstructive nodes had a central role in the connectome of chess players. The analysis of communities showed that chess players had a slightly reorganized cognitive architecture into three modules. These graph theory findings were in the context of better cognitive performance in chess players than controls in visuospatial abilities.ConclusionWe conclude that the cognitive architecture of chess players is slightly reorganized into functionally and anatomically coherent modules reflecting a distinction between visual, verbal, and executive/attention/processing speed-related functions, perhaps reminiscent of right hemisphere and left hemisphere subnetworks orchestrated by the frontal lobe and its white matter connections.

Visual Target Interaction Modeling with Multichannel Data Fusion

Facing the demand for accurate, fast, and natural human–computer interaction in the multi-screen control environment, the traditional exchange method of frequent switching control of multi-screen through mouse, keyboard, and other manual methods has problems of low efficiency, weak flexibility, and poor experience. In this paper, we research multi-screen precision control technology based on eye movement to break through the difficulties of precise recognition of human vision under complex environments and poor stability of vision estimation under frequent switching control of multi-screen. This study explores the technology for controlling multiple screens with high precision by tracking eye movements. It overcomes the challenges associated with reliably discerning human visual focus in intricate settings and the instability of visual assessments during rapid transitions between multiple screens. The experimental results of 1143 eye-movement capture tasks are studied, including the accurate recognition technology of human head posture and pupil gaze direction under complex backgrounds, illumination, and different visual field environments, and the theoretical modeling method of multi-screen precise target interaction based on eye-movement is explored. The expression data will be processed using convolutional neural networks, and the dataset will be trained through Python programming and tested on test samples collected by simulated flight crews in the laboratory with an accuracy rate of more than 85%.

Rapid motion planning of manipulator in three-dimensional space under multiple scenes

Most working scenes of industrial robots are static scenes and from a previous static scene to a current static scene is a multi-scene. Finding optimal paths with limited time is difficult for motion planning in a high-dimensional space or in multiple scenes. The low efficiency of motion planning of rapidly-exploring random tree star in high-dimensional spaces, low adaptability of global replanning to multiple scenes are addressed by proposing the local replanning based on goal dynamically-guiding rapidly-exploring random tree star (LR-GD-RRT*). The algorithm contributes to fast path tree exploration and multi-scene motion planning. For path tree exploration, sampling points heuristically generating and new nodes growth by goal dynamically-guiding are proposed to reduce the blind and ineffective searches. Moreover, dynamic adjustment of size of new node neighborhood according to density of the obstacles is proposed to search for more neighbor nodes to optimize the path and also to reduce ineffective computation to improve efficiency. For multi-scene, three steps of trimming, re-exploration, and reconnection for local replanning are proposed. For path tree exploration, simulations in 2D plane, 3D space, and the manipulator show that GD-RRT* improves convergence speed, shortens path length and search time, compared with RRT*. For multi-scene, simulations in 3D space and with the manipulator show that the local replanning of the current scene has both lower path cost and higher planning efficiency compared with the global replanning of the previous scene. Motion of the six-degree-of-freedom robot end in a real scene also verifies the effectiveness of the LR-GD-RRT*.

Future healthcare professionals on working conditions in Poland: perspective of medical university students.

Students of medical universities, future employees, will have an impact on the shaping healthcare system. It is important to know and understand their opinions on the factors affecting working conditions and, consequently, changes necessary to improve effectiveness of health care. Students' expectations can contribute to the changing working conditions for graduates and bring added value to health system redefinition. The study used factor analysis to check whether its use was justified. Reliability analysis was performed and structure indicators were determined for each question. The anonymous survey was conducted from September 2017 until March 2018; 1205 students were randomly selected for the sample. Eighty percent of the students declared interest in healthcare changes, >50% reported that the main factors influencing the work were competencies, financing, medical equipment and organization. Over 90% of the respondents indicated too long wait times for an appointment with a specialist and admission to hospital as the reason for the low efficiency of healthcare, whereas >80% of the survey participants considered insufficient funding to be a barrier. The need for changing the financing scheme was underlined by >90% of the students, and of health priorities by about 80%. Approximately 71% of the respondents were in favour of limiting the role of government in decision-making processes and introducing changes into the education system. Students' views on organizational and financial factors of working conditions can contribute to improvement in systemic solutions at both micro and macro levels. Limiting the role of politicians in planning and implementing reforms can motivate employees to be more creative and decisive. Analysis of opinions can bring added value to health policy and systemic changes and should be extended by further research results after the COVID-19 pandemic. Students' interest in healthcare reform encourages reflection on enriching education with managerial skills. Med Pr Work Health Saf. 2024;75(4):321-332.

Performance of a novel CLOU oxygen carrier (CuO/ZrO2/TiO2/MgO) in the combustion of high-rank coal: Anthracite and bituminous

Coal is the largest contributor to global CO2 emissions, and current reserves are projected to last approximately 150 years, with 71.6 % being high-rank coals (anthracite and bituminous). Chemical looping combustion is an economical CO2 capture method that uses oxygen carrier materials to supply oxygen for combustion. Copper is the most reactive due to its oxygen uncoupling capability, though it has poor stability. In our research, we developed a novel modified copper-based oxygen carrier with enhanced stability in oxygen uncoupling cycles by increasing the ZrO2 ratio at the expense of TiO2 to reduce phase interactions. The modified oxygen carrier (40 % CuO, 30 % ZrO2, 15 % TiO2, 15 % MgO) was used to combust bituminous and anthracite coal in a fixed-bed system. While bituminous coal had a high combustion rate, its efficiency was 63 %, yielding 18 mol/kg of CO2, compared to 90 % efficiency and 56 mol/kg CO2 for anthracite. The lower efficiency for bituminous was due to the rapid release of volatiles. Adding steam improved bituminous combustion efficiency to 75 % and CO2 yield to 21 mol/kg, with no significant change for anthracite. A blend of 25 % bituminous and 75 % anthracite showed better combustion efficiency, rate, and CO2 yield. This blend test was stable over 10 cycles, as confirmed by SEM, XRD, and BET analyses.

Epigenetic Dynamics in Reprogramming to Dopaminergic Neurons for Parkinson's Disease.

Direct lineage reprogramming into dopaminergic (DA) neurons holds great promise for the more effective production of DA neurons, offering potential therapeutic benefits for conditions such as Parkinson's disease. However, the reprogramming pathway for fully reprogrammed DA neurons remains largely unclear, resulting in immature and dead-end states with low efficiency. In this study, using single-cell RNA sequencing, the trajectory of reprogramming DA neurons at multiple time points, identifying a continuous pathway for their reprogramming is analyzed. It is identified that intermediate cell populations are crucial for resetting host cell fate during early DA neuronal reprogramming. Further, longitudinal dissection uncovered two distinct trajectories: one leading to successful reprogramming and the other to a dead end. Notably, Arid4b, a histone modifier, as a crucial regulator at this branch point, essential for the successful trajectory and acquisition of mature dopaminergic neuronal identity is identified. Consistently, overexpressing Arid4b in the DA neuronal reprogramming process increases the yield of iDA neurons and effectively reverses the disease phenotypes observed in the PD mouse brain. Thus, gaining insights into the cellular trajectory holds significant importance for devising regenerative medicine strategies, particularly in the context of addressing neurodegenerative disorders like Parkinson's disease.

Grouping-maintenance of multi-components production system under ecological background

Purpose: The purpose of this paper is to propose an opportunistic group maintenance model for a multi-component series system considering reducing CO2 emission and increasing system efficiency, which establishes a maintenance policy to minimize the cost rate of the system life cycle.Design/methodology/approach: Structural dependence and economic dependence between the components in a multi-component series system are analyzed to make a condition-based maintenance policy. To minimize the cost rate of the system life cycle, clustering theory and two decision variables, which include the preventative maintenance cycle multiplier and basic preventive maintenance interval of each component, is utilized to make an opportunistic policy for system optimal maintenance under the background of increasing energy efficiency and reducing CO2 emissions.Findings: It can be concluded that the government imposes fines on excessive CO2 emissions and energy consumption indicators, which can influence the maintenance decisions of enterprises, promote enterprises to shorten the cycle of preventive maintenance, and avoid excessive CO2 emissions of various components and exceed the indicators as much as possible, so as to enable enterprises to actively save energy, reduce emissions and control carbon emissions. Furthermore, when the single preparation cost of repair maintenance increases, enterprises need to shorten the maintenance period to avoid frequent component failures. As the cost of a single prep for preventive maintenance rises, organizations need to extend maintenance cycles and avoid frequent parts downtime - minimizing their own repair costs.Practical implications: Considering the high maintenance cost and low energy efficiency of multi-component systems, this model assists production managers to have better maintenance of these systems.Originality/value: 1.Model innovation: comprehensive consideration of two variables in the selection of decision variables for preventive maintenance or opportunistic maintenance; The selection and synthesis of ecological factors when making ecologically conscious maintenance decisions for multi-component systems make up for the gaps of single variables and one-sided indicators in previous studies and models. 2. Methodological innovation: In the identification of opportunity maintenance, the idea of clustering is first combined; In the simulation analysis, genetic algorithm is used to obtain the optimal parameters quickly and accurately. A blend of management, statistics, biology and computer science.

Tailoring Pseudo-Graphitic Domain by Molybdenum Modification to Boost Sodium Storage Capacity and Durability for Hard Carbon.

Hard carbon (HC) stands out as the most prospective anode for sodium-ion batteries (SIBs) with significant potential for commercial applications. However, some long-standing and intractable obstacles, like low first coulombic efficiency (ICE), poor rate capability, storage capacity, and cycling stability, have severely hindered the conversion process from laboratory to commercialization. The above-mentioned issues are closely related to Na+ transfer kinetics, surface chemistry, and internal pseudo-graphitic carbon content. Herein, constructing molybdenum-modified hard carbon solid spheres (Mo2C/HC-5.0), both the ion transfer kinetics, surface chemistry, and internal pseudo-graphitic carbon content are comprehensively improved. Specifically, Mo2C/HC-5.0 with higher pseudo-graphitic carbon content provides a large number of active sites and a more stable layer structure, resulting in improved sodium storage capacity, rate performance, and cycling stability. Moreover, the lower defect density and specific surface area of Mo2C/HC-5.0 further enhance ICE and sodium storage capacity. Consequently, the Mo2C/HC-5.0 anode achieves a high capacity of 410.7mA h g-1 and an ICE of 83.9% at 50mA g-1. Furthermore, the material exhibits exceptional rate capability and cycling stability, maintaining a capacity of 202.8mA h g-1 at 2 A g-1 and 214.9mA h g-1 after 800 cycles at 1 A g-1.

Achieving Closed-Loop Recycling of a Semi-Conjugated Polymer through Incorporation of Ester Linkers.

Design strategies to achieve degradation and ideally closed-loop recycling of organic semiconductors have attracted great interest in order to minimize the electronic waste (E-waste). In this work, three ester-incorporated monomers were synthesized by the names of Thiophene-Ester-Ethylene-Thiophene (TEET), Thiophene-Ester-Methylene-Thiophene (TEMT), and Thiophene-Ester-Thiophene (TET), which were co-polymerized via Stille polycondensation with a benzodithiophene (BnDT) pi-conjugated unit to yield a series of ester-incorporated polymers: PBnDT-TEET, PBnDT-TEMT, and PBnDT-TET. While the ester-only linker can maintain some extended conjugation in PBnDT-TET, the other two ester linkers having conjugation breaking units result in isolated conjugated segments in PBnDT-TEET and PBnDT-TEMT, evidenced by UV-Vis and CV results. This yields an improved photovoltaic performance of PBnDT-TET compared to PBnDT-TEET. While all three polymers can degrade under methanolysis, the alternating co-polymer PBnDT-TEET demonstrates the highest recyclability potential with a single dimethyl ester-functionalized product with an excellent 92% isolated yield, which can then be repolymerized to obtain the repolymerized PBnDT-TEET with a 36% yield. This work provides a framework towards achieving recyclable organic semiconductors to reduce E-waste. Although the incorporation of ester linkers allowed for closed-loop recycling, the low solar cell efficiency of PBnDT-TEET highlights the significant challenges in achieving both recycling and high device performance.

PENERAPAN METODE SUPPLY CHAIN MANAGEMENT PADA SISTEM INFORMASI PENDISTRIBUSIAN SCRAP PT. PACIFIC MEDAN INDUSTRI BERBASIS WEBSITE

Pacific Medan Industri, a leading producer of palm oil, faces significant challenges with its scrap distribution system, which currently relies on manual processes. This manual system has been identified as having several major issues, including inaccuracies in data recording and management, delays in the shipping process, and ineffective coordination between various units within the supply chain. These issues not only lead to low operational efficiency but also risk disrupting customer satisfaction and diminishing the company's competitive edge in an increasingly competitive market. This study aims to address these problems by optimizing the scrap distribution system through the application of Supply Chain Management (SCM) principles. Using a qualitative approach that includes literature review, in-depth interviews with stakeholders, and relevant documentation analysis, this research explores the various weaknesses present in the current manual scrap distribution system. The analysis reveals significant shortcomings, such as inefficiencies in data management, lack of integration between units resulting in poor coordination, and slow shipping processes that hinder productivity and customer satisfaction. As a solution, the study proposes the development of a SCM-based information distribution system. This system is designed to address the identified weaknesses by leveraging current technology to automate distribution processes, improve data accuracy, expedite shipping times, and enhance communication and coordination between units in the supply chain. The implementation of this system is expected to enhance overall operational efficiency, improve scrap management, and ensure better coordination across the entire supply chain. It is anticipated that PT. Pacific Medan Industri will benefit from increased operational efficiency, improved customer satisfaction, and a strengthened competitive position in the market through the adoption of this SCM-based information distribution system. Keywords: Supply Chain Management, Scrap, PT. Pacific Medan Industri.