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Atom manufacturing has become a blooming frontier direction in the field of material and chemical science in recent years, focusing on the fabrication of functional materials and devices with individual atoms or with atomic precision. Framework nucleic acids (FNAs) refer to nanoscale nucleic acid framework structures with novel properties distinct from those of conventional nucleic acids. Due to their ability to be precisely positioned and assembled at the nanometer or even atomic scale, FNAs are ideal materials for atom manufacturing. They hold great promise for the bottom-up construction of electronic devices by precisely arranging and integrating building blocks with atomic or near-atomic precision. In this review, we summarize the progress of atom manufacturing based on FNAs. We begin by introducing the atomic-precision construction of FNAs and the intrinsic electrical properties of DNA molecules. Then, we describe various approaches for the fabrication of FNAs templated materials and devices, which are classified as conducting, insulating, or semiconducting based on their electrical properties. We highlight the role of FNAs in the fabrication of functional electronic devices with atomic precision, as well as the challenges and opportunities for atom manufacturing with FNAs. [ABSTRACT FROM AUTHOR]

During development, many aspects of behaviour, including partner preferences and sexual behaviour, are "organized" by neural aromatization of androgen and oestrogen. This study aimed to analyse these processes in the mandarin vole (Microtus mandarinus); this is a novel mammalian model exhibiting strong monogamous pair bonds. Male pups were treated daily with a sesame oil only (MC) or the oestrogen receptor blocker‐clomiphene citrate sesame oil mixture (MT) from prenatal day 14 to postnatal day 10. Female pups were treated daily with sesame oil only (FC). Partner preferences, sexual behaviour, and the expression of androgen receptor (AR) and arginine vasopressin (AVP) were examined when animals were 3 months old. The MT and FC groups exhibited male‐directed partner preferences and feminized behaviour. AR‐immunoreactive neurons (AR‐IRs) in the medial preoptic area (mPOA), bed nucleus of stria terminalis (BNST), and medial amygdaloid nucleus (MeA) were reduced in MT males compared to MC males, and there was no significant difference in the number of AR‐IRs between MT males and FC females. AVP‐immunoreactive neurons (AVP‐IRs) in the paraventricular nucleus (PVN) and supraoptic nucleus (SON) were reduced in MT males compared to MC males, and there were no significant differences in the number of AVP‐IRs between MT males and FC females. The results indicate a significant role of hormone signalling in the development of male mate preference in the novel monogamous mammal model. [ABSTRACT FROM AUTHOR]

Marine biofouling is one of the most significant challenges hindering practical uranium extraction from seawater. Single atoms have been widely used in catalytic applications because of their remarkable redox property, implying that the single atom is highly capable of catalyzing the generation of reactive oxygen species (ROS) and acts as an anti‐biofouling substance for controlling biofouling. In this study, the Co single atom loaded polyacrylamidoxime (PAO) material, PAO‐Co, is fabricated based on the binding ability of the amidoxime group to uranyl and cobalt ions. Nitrogen and oxygen atoms from the amidoxime group stabilize the Co single atom. The fabricated PAO‐Co exhibits a broad range of antimicrobial activity against diverse marine microorganisms by producing ROS, with an inhibition rate up to 93.4%. The present study is the first to apply the single atom for controlling biofouling. The adsorbent achieves an ultrahigh uranium adsorption capacity of 9.7 mg g−1 in biofouling‐containing natural seawater, which decreased only by 11% compared with that in biofouling‐removed natural seawater. These findings indicate that applying single atoms would be a promising strategy for designing biofouling‐resistant adsorbents for uranium extraction from seawater. [ABSTRACT FROM AUTHOR]

An adaptive coordination structure is vital for selective uranium extraction from seawater. By strategy of molecular imprinting, uranyl is introduced into a multivariate metal–organic framework (MOF) during the synthesis process to guide the in situ construction of proper nanocage structure for targeting uranyl binding. Except for the coordination between uranium with four oxygen from the materials, the axial oxygen of uranyl also forms hydrogen bonds with hydrogen from the phenolic hydroxyl group, which enhances the binding affinity of the material to uranyl. Attributing to the high binding affinity, the adsorbent shows high uranium binding selectivity to uranyl against not only the interfering metal ions, but also the carbonate group that coordinates with uranyl to form [UO2(CO)3]4− in seawater. In natural seawater, the adsorbent realizes a high uranium adsorption capacity of 7.35 mg g−1, together with an 18.38 times higher selectivity to vanadium. [ABSTRACT FROM AUTHOR]

The detection of environmental uranyl is attracting increasing attention. However, the available detection strategies mainly depend on the selective recognition of uranyl, which is subject to severe interference by coexisting metal ions. Herein, based on the unique uranyl‐triggered photocleavage property, the protein BSA is labelled with fluorescent molecules that exhibit an aggregation‐induced emission effect for uranyl detection. Uranyl‐triggered photocleavage causes the separation of the fluorescent‐molecule‐labelled protein fragments, leading to attenuation of the emission fluorescence, which is used as a signal for uranyl detection. This detection strategy shows high selectivity for uranyl and an ultralow detection limit of 24 pM with a broad detection range covering five orders of magnitude. The detection method also shows high reliability and stability, making it a promising technique for practical applications in diverse environments. [ABSTRACT FROM AUTHOR]

The extraction of uranium from seawater, which is an abundant resource, has attracted considerable attention as a viable form of energy‐resource acquisition. The two critical factors for boosting the chemical thermodynamics of uranium extraction from seawater are the availability of sufficient amounts of uranyl ions for supply to adsorbents and increased interaction temperatures. However, current approaches only rely on the free diffusion of uranyl ions from seawater to the functional groups within adsorbents, which largely limits the uranium extraction capacity. Herein, inspired by the mechanism of plant transpiration, a plant‐mimetic directional‐channel poly(amidoxime) (DC‐PAO) hydrogel is designed to enhance the uranium extraction efficiency via the active pumping of uranyl ions into the adsorbent. Compared with the original PAO hydrogel without plant‐mimetic transpiration, the uranium extraction capacity of the DC‐PAO hydrogel increases by 79.33% in natural seawater and affords the fastest reported uranium extraction average rate of 0.917 mg g−1 d−1 among the most state‐of‐the‐art amidoxime group‐based adsorbents, along with a high adsorption capacity of 6.42 mg g−1 within 7 d. The results indicate that the proposed method can enhance the efficiency of solar‐transpiration‐based uranium extraction from seawater, particularly in terms of reducing costs and saving processing time. [ABSTRACT FROM AUTHOR]

A bio-inspired cellulose paper–poly(amidoxime) composite hydrogel is explored via UV-polymerization. This hydrogel has a highly efficient uranium capture capacity of up to 6.21 mg g−1 for WU/Wdry gel and 12.9 mg g−1 for WU/Wpoly(amidoxime) in seawater for 6 weeks, due to its enhanced hydrophilicity, good hydraulic/ionic conductivity and broad-spectrum antibacterial performance. [ABSTRACT FROM AUTHOR]

Plug-in hybrid electric buses (PHEBs), compared with traditional fuel-driven vehicles, can achieve higher fuel economy and lower pollution emissions. For a PHEB with a single-shaft parallel powertrain, a major challenge for researchers is to find approximate optimal energy management strategies that can run in real time. Motivated by this idea, this paper aims at minimizing PHEB fuel consumption with a temporal-difference (TD) learning method. First, historical driving cycle data from real-world bus routes are collected and processed and parameter variables of TD are introduced. Specially, this process is completed offline. Then, the configuration and main parameters of PHEB are presented, and a control-oriented dynamic system of the PHEB is constructed. Thereafter, the TD learning method based on historical data is introduced. Furthermore, the approximate optimal control strategy for energy management is proposed. Compared with the traditional optimal control strategy, the proposed method can realize real-time running without sacrificing the accuracy of optimization, because the learning method updates the estimates based on other learned estimates without calculating a final outcome. This method can learn directly from the data of running PHEBs without a simplified model of the PHEB, which can avoid the influence of model error. Finally, to verify this method, several different strategies are used for comparison. In addition, experimental results in real-world driving cycles demonstrate that the proposed method can improve the fuel economy obviously by up to 21% compared with a traditional charge-deleting, charge-sustaining scenario. Therefore, this novel method has great potential in realistic applications. [ABSTRACT FROM AUTHOR]

Driving cycles and road slope are two important factors affecting fuel saving performance of plug-in hybrid electric buses (PHEBs) in Chinese cities. Moreover, onboard auxiliary equipment (e.g., Global Position System receiver and General Packet Radio Service (GPRS) wireless module) of PHEB may provide potential means to communicate with the control center of the bus company, allowing for driving cycle prediction through data communication between foregoing buses and the control center. With this general approach in mind, and by utilizing driving data clustering and driving cycle classifier, this paper presents a multimode switched logic control strategy, targeting fuel economy improvement of the PHEB team for a particular city bus route. First, the normal feature parameters are extracted from the sampled driving history cycles, and the composed feature parameters are given by a mapping of normal feature parameters in this approach. A novel improved hierarchical clustering algorithm is applied for driving cycles' data clustering into four groups. Then, on the basis of the clustering results, support vector machine method is used to predict the current driving cycle. Finally, a switched driving controller is presented according to current type of driving cycle and slope information. Simulation results are compared with those of traditional methods in the given real-world driving cycles of city bus, showing significant improvement, which may offer a theoretical solution with engineering application. Experimental results also demonstrate that the proposed control approach is feasible in the tested bus routes. [ABSTRACT FROM PUBLISHER]

Taking the complex but regular characteristics of bus routines into account, the stochastic dynamic programming (SDP) might be a method with more potential to optimize the energy management of a plug-in hybrid electric bus (PHEB). However, the discrete transmission system and the continuous power system make it a complicated multidimensional optimal problem, particularly for PHEB with automated mechanical transmission (AMT), and the optimal decisions, which are obtained based on historical data, might not always well satisfy the driver's expectation to vehicle maneuverability under various driving conditions. To solve these problems, an adaptive approach based on the SDP is proposed in this paper. Exhaustively, the SDP is propelled into the input of the transmission to only optimize the torque split under the special gearshift logic, which reduces the dimensions of optimization and obtains more applicable optimal sequences. Then, an adaptive factor, which trades off the vehicle fuel economy and drivability in real time by dynamically adjusting the gearshift points and the torque split, is developed for the variation of the complicated bus driving cycles. The simulation results demonstrate that the proposed method could well respond to the variations of the driving conditions (e.g., road grade and vehicle load). Furthermore, the performance of the proposed method is discussed in detail by comparisons with different control strategies. More importantly, the proposed approach has great potential to be applied in practice. [ABSTRACT FROM PUBLISHER]

Cellular automata (CA) models have been extensively employed to predict and understand the spatiotemporal dynamics of land use. Driving factors play a significant role in shaping and driving land-use changes. Mining land-use transition rules from driving factors and quantifying the contribution of driving factors to land-use dynamics are fundamental aspects of CA simulation. However, existing CA models have limitations in obtaining accurate transition rules and reliable interpretations simultaneously for multiple land-use simulations. In this study, we constructed a CA model based on a tree-based deep learning algorithm, deep cascade forest (DCF), to improve multiple land-use simulations and driving factors analysis. The DCF algorithm was utilized to mine accurate multiple land-use transition rules without overfitting to improve CA simulation accuracy. Additionally, a novel ensemble mean decrease of impurity (MDI) factor importance analysis method (DCF-MDI), which leverages the cascade structure of the DCF model, was proposed to qualify the contribution of each driving factor to land-use dynamics stably and efficiently. To evaluate the effectiveness of the proposed DCF-CA, we applied the model to simulate land-use distributions and explore the driving mechanisms of land-use dynamics in the Pearl River Delta (PRD), China, from 2000 to 2010. Compared to existing models, the proposed DCF-CA model exhibits the highest accuracy (FoM = 23.79%, PA = 39.77%, UA = 36.35%, OA = 91.50%), which demonstrates its superiority in mining accurate transition rules for capturing multiple land-use dynamics. Factor importance analysis reveals that the proposed DCF-MDI method yields more stable ranking orders and lower standard deviation of contribution weights (<0.10%) compared to the traditional method, indicating its robustness to random disturbances and effectiveness in elucidating the driving mechanisms of land-use dynamics. The DCF-CA model proposed in this study, demonstrating high simulation accuracy and reliable interpretability simultaneously, can provide substantial support for sustainable land use management. [ABSTRACT FROM AUTHOR]

The uranium level in seawater is ≈1000 times as high as terrestrial ores and can provide potential near‐infinite fuel for the nuclear energy industry. However, it is still a significant challenge to develop high‐efficiency and low‐cost adsorbents for massively extracting uranium from seawater. Herein, a simple and fast method through low‐energy consumption sunlight polymerization to direct fabrication of a poly(amidoxime) (PAO) hydrogel membrane, which exhibits high uranium adsorption capacity, is reported. This PAO hydrogel owns semi‐interpenetrating structure and a hydrophilic poly(acrylamide) 3D network of hydrogel which can disperse and fix PAOs well. As a result, the amidoxime groups of PAOs exhibit an outstanding uranium adsorption efficiency (718 ± 16.6 and 1279 ± 14.5 mg g−1 of muranium/mPAO in 8 and 32 ppm uranium‐spiked seawater, respectively) among reported hydrogel‐based adsorbents. Most importantly, U‐uptake capacity of this hydrogel can achieve 4.87 ± 0.38 mg g−1 of muranium/mdry gel just after four weeks within natural seawater. Furthermore, this hydrogel can be massively produced through low‐energy consumption and environmentally‐friendly sunlight polymerization. This work will provide a high‐efficiency and low‐cost adsorbent for massive uranium extraction from seawater. [ABSTRACT FROM AUTHOR]

Machine learning methods offer opportunities improve pandemic response and risk management by supplementing mechanistic modeling approaches to pandemic planning and response based on diverse sources of data at every level from the local to global scale. However, such solutions rely on the availability of appropriate data as well as communication and dissemination of that data to develop tools and guidance for decision making. A lack of consistency in the reporting and availability of disaggregated, detailed data on COVID-19 in the US has limited the application of artificial intelligence methods and the effectiveness of those methods for projecting the spread and subsequent impacts of this disease in communities. These limitations are missed opportunities for AI methods to make a positive contribution, and they introduce the possibility of inappropriate use of AI methods when not acknowledged. Going forward, governing bodies should develop data collection and sharing standards in collaboration with AI researchers and industry experts to facilitate preparedness for pandemics and other disasters in the future. [ABSTRACT FROM AUTHOR]

This paper proposes a novel hybrid algorithm for simultaneously estimating the vehicle mass and road grade for hybrid electric bus (HEB). First, the road grade in current step is estimated using extended Kalman filter (EKF) with the initial state including velocity and engine torque. Second, the vehicle mass is estimated twice, one with EKF and the other with recursive least square (RLS) using the estimated road grade. A more accurate value of the estimated mass is acquired by weighting the trade-off between EKF and RLS. Finally, the road grade and vehicle mass thus obtained are used as the initial states for the next step, and two variables could be decoupled from the nonlinear vehicle dynamics by performing the above procedure repeatedly. Simulation results show that in different starting conditions, the proposed algorithm provides higher accuracy and faster convergence speed, compared with the results using EKF or RLS alone. [ABSTRACT FROM AUTHOR]

Cultural differences have been reported between the taste sensitivity of persons of Asian and European ancestry, although findings have been mixed. This study sought to determine whether American and Chinese adults perform differently on a novel taste test that requires no water, can be self‐administered, and employs a representative of umami as one of its tastants. This 53‐trial test was administered to 113 Chinese and 214 Americans. The subjects orally sampled monomer cellulose pads containing one of four dried concentrations of sucrose, citric acid, NaCl, caffeine, and monosodium glutamate and indicated whether a sweet, sour, bitter, salty, brothy, or no taste sensation was perceived. Separate gender by culture analyses of covariance with age as the covariate were performed on the total score and the scores of each taste stimulus. For all taste qualities, women outperformed men and test scores declined with age. No difference between American and Chinese subjects was found for the total taste score (p =.129) or for the sucrose (p =.129) or NaCl (p =.368) scores. However, for monosodium glutamate, the scores were 28.40% higher for the Chinese than for the American subjects (p =.024), and for citric acid and caffeine, the scores were 24.12 and 21.79% higher for the American subjects (p's =.001 and.029). The basis for these differences is unclear, although both anatomical (e.g., differences in density or distribution of taste buds) and cultural factors may be involved. Future work is needed to determine the cause of these largely novel findings and whether they generalize to other Chinese and American samples. Practical applicationsIn this study, a practical self‐administered quantitative taste test that requires no water was found to be sensitive to quality‐specific differences in test scores between Chinese and American subjects, as well as to age and gender. The Chinese subjects outperformed the American subjects in correctly identifying the quality of monosodium glutamate (umami), whereas the American subjects outperformed Chinese subjects in correctly identifying the bitter and sour qualities of caffeine and citric acid, respectively. Experiential factors related to culture‐specific cuisines may explain some of these differences. This research indicates that a relatively rapid taste test, which can be sent through the mail and which requires no test administrator or source of water, can be used in cross‐cultural studies to elucidate individual differences in taste perception. [ABSTRACT FROM AUTHOR]

Background. Chronic rhinosinusitis with nasal polyps (CRSwNP) is a common inflammatory disease with high heterogeneity and postoperative recidivation. The IL-33/ST2 axis is known to be involved in Th2 immune responses. This study is aimed at exploring levels of serum IL-33 and soluble ST2 (sST2) in CRSwNP patients and their potential for predicting CRSwNP endotypes and postoperative recurrence. Methods. The present study recruited 149 CRSwNP patients, 80 of whom were noneosinophilic (neCRSwNP) and 69 eosinophilic (eCRSwNP), as well as 60 healthy controls (HCs). Serum samples were collected from all participants, and sST2 and IL-33 concentrations were measured using ELISA. Multivariate analysis, receiver operating characteristic (ROC) curves, and Kaplan-Meier curves were used to evaluate the value of serum sST2 and IL-33 levels in distinguishing CRSwNP endotypes and predicting postoperative recurrence. Results. The levels of serum sST2 and IL-33 in CRSwNP patients were significantly higher than those in HCs, especially in the eCRSwNP group. Increased sST2 and IL-33 levels were associated with eosinophil counts and percentages in both tissue and blood. Multivariate regression and ROC curve analysis showed that serum sST2 and IL-33 exhibited potential for distinguishing CRSwNP endotypes, and the combination of serum IL-33 and sST2 showed even more predictive power. Finally, 124 CRSwNP patients completed the entire 3-year follow-up. Multivariate analysis and Kaplan-Meier curves showed that serum sST2 and IL-33 levels were associated with recurrence; serum sST2 and IL-33 each exhibited potential for predicting postoperative recurrence, and combining serum sST2 and IL-33 exhibited better accuracy and practicability. Conclusion. Our results suggested that serum sST2 and IL-33 levels were upregulated in CRSwNP patients and related to the degree of mucosal eosinophil infiltration and postoperative recurrence. Serum sST2 and IL-33 might serve as objective biomarkers for distinguishing phenotypes and predicting recurrence in CRSwNP, and their combined use outperformed either marker alone. [ABSTRACT FROM AUTHOR]

Motivation Survival analysis using gene expression profiles plays a crucial role in the interpretation of clinical research and assessment of disease therapy programs. Several prediction models have been developed to explore the relationship between patients' covariates and survival. However, the high-dimensional genomic features limit the prediction performance of the survival model. Thus, an accurate and reliable prediction model is necessary for survival analysis using high-dimensional genomic data. Results In this study, we proposed an improved survival prediction model based on XGBoost framework called XGBLC, which used Lasso-Cox to enhance the ability to analyze high-dimensional genomic data. The novel first- and second-order gradient statistics of Lasso-Cox were defined to construct the loss function of XGBLC. We extensively tested our XGBLC algorithm on both simulated and real-world datasets, and estimated the performance of models with 5-fold cross-validation. Based on 20 cancer datasets from The Cancer Genome Atlas (TCGA), XGBLC outperforms five state-of-the-art survival methods in terms of C-index, Brier score and AUC. The results show that XGBLC still keeps good accuracy and robustness by comparing the performance on the simulated datasets with different scales. The developed prediction model would be beneficial for physicians to understand the effects of patient's genomic characteristics on survival and make personalized treatment decisions. Availability and implementation The implementation of XGBLC algorithm based on R language is available at: https://github.com/lab319/XGBLC Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Background: Myelomatous pleural effusion (MPE), as a presentation of extramedullary infiltration of multiple myeloma (MM), is rare and currently associated with poor outcomes without effective therapy. The potential value of cytokine detection in pleural effusion to MPE has not been reported to date. Case Presentation: We herein report a case of refractory and relapsed multiple myeloma that developed bilateral MPE due to disease progression caused by intolerance to various chemotherapy regimens. Cytomorphology and flow cytometry were adopted for diagnosis confirmation. Chemotherapy containing immunomodulators combined with thoracic catheterization drainage was applied to the patient, showing a certain therapeutic effect. During the course of disease, the change of cytokine profile in pleural effusion was monitored by cytometric bead array (CBA) technology, revealing that cytokines related to tumor load such as interleukin 6 (IL-6) and interleukin 10 (IL-10) in pleural effusion decreased with the improvement of disease, while other cytokines such as interleukin 2 (IL-2), interleukin 4 (IL-4), interleukin 17A (IL-17A), tumor necrosis factor α (TNF-α), interferon γ (IFN-γ), granzyme A, granzyme B, perforin and granulysin increased with the improvement of disease. Conclusion: There is a prospect that cytokine level in pleural effusion may indicate treatment response of MPE, and in light of this case, immunomodulators may be utilized in treating patients suffering MPE. Due to limitations of our single case, we urge more groups to evaluate the potential role of cytokine profile in MPE. [ABSTRACT FROM AUTHOR]

Plants can change leaf forms, adjusting light conditions on their adaxial and abaxial surfaces, to adapt to light environments and enhance their light use efficiencies. The difference between photosynthesis on the two leaf sides (dorsoventral asymmetry) is an important factor that affects light use efficiency. However, photosynthetic dorsoventral asymmetry is rarely compared under direct and diffuse light conditions. To estimate the impacts of recently reported alterations in direct and diffuse light in the sky radiation on plant carbon assimilation, variations in morphology between the two leaf sides in tobacco (Nicotiana tabacum L.) were investigated, and the dorsoventral responses of photosynthesis to illuminating directions were compared in direct and diffuse light. Dorsoventral asymmetry was reflected in stomatal densities, anatomic structures, and photochemical traits, which caused markedly different photosynthetic rates as well as stomatal conductances both in direct and diffuse light. However, the degree of photosynthetic asymmetry was weakened in diffuse light. The diffuse light caused a greater stomatal conductance on the abaxial side than direct light, which resulted in reduced photosynthetic asymmetry. In addition, the photosynthetic dorsoventral asymmetry could be affected by the photosynthetic photon flux density. These results contribute to understanding the dorsoventral regulation of photosynthesis in bifacial leaves, and provide a reference for breeding to cope with the increase in the proportion of diffuse light in the future. [ABSTRACT FROM AUTHOR]

Driving cycles of a city bus is statistically characterized by some repetitive features, which makes the predictive energy management strategy very desirable to obtain approximate optimal fuel economy of a plug-in hybrid electric bus. But dealing with the complicated traffic conditions and finding an approximated global optimal strategy which is applicable to the plug-in hybrid electric bus still remains a challenging technique. To solve this problem, a novel driving-behavior-aware modified stochastic model predictive control method is proposed for the plug-in hybrid electric bus. Firstly, the K -means is employed to classify driving behaviors, and the driver models based on Markov chains is obtained under different kinds of driving behaviors. While the obtained driver behaviors are regarded as stochastic disturbance inputs, the local minimum fuel consumption might be obtained with a traditional stochastic model predictive control at each step, taking tracking the reference battery state of charge trajectory into consideration in the finite predictive horizons. However, this technique is still accompanied by some working points with reduced/worsened fuel economy. Thus, the stochastic model predictive control is modified with the equivalent consumption minimization strategy to eliminate these undesirable working points. The results in real-world city bus routines show that the proposed energy management strategy could greatly improve the fuel economy of a plug-in hybrid electric bus in whole driving cycles, compared with the popular charge depleting–charge sustaining strategy and it may offer some useful insights for realizing the approximate global optimal energy management for the plug-in hybrid electric vehicles. [ABSTRACT FROM AUTHOR]

A series of carbazole-based organic dyes such as CzFCA, CzTCA, CzSeCA, CzFRA, CzTRA and CzSeRA with introduction of various linkers (furan, thiophene and selenophen) and acceptor (cyanoacrylic acid and rhodanine-3-acetic acid) groups, were designed and theoretically investigated by density functional theory (DFT) and time dependent DFT (TD-DFT). The ground state structures and absorption spectra (both in vacuum and solvated form) were optimized by using DFT-B3LYP/6-31G(d,p), TD-B3LYP/6-311G(d,p) and PCM-TD-B3LYP/6-311G(d,p) levels of theory. Substitution of linker groups from furan to selenophen, resulted in red-shift of absorption band. CzTRA and CzSeRA exhibited superior electron injection capabilities. The finding of this study can be helpful to obtain dye-sensitized solar cell (DSSC) with superior conversion efficiency. The effect of different linkers and donor groups in carbazole-based organic dye sensitizers were investigated. The absorption was found toward red-shift by substituting of the linkers by selenophen and acceptors by rhodanine-3-acetic acid and the electron injection abilities of CzTRA and CzSeRA were enhanced. [ABSTRACT FROM AUTHOR]