Making the Brightest Ones Dim: Maximizing the Photothermal Conversion Efficiency of BODIPY-Based Photothermal Agents.

The successful implementation of photothermal therapy (PTT) in cancer treatment hinges on the development of highly effective photothermal agents (PTAs). Boron dipyrromethene (BODIPY) dyes, being well known for their high brightness and quantum efficiencies, are the antithesis of PTAs. Nonetheless, a systematic exploration of the photophysics and photothermal characteristics of a series of π-extended BODIPY dyes with high absorptivity in the near-infrared (NIR) region has achieved superior photothermal conversion efficiencies (>90%), in both monomeric state and nanoparticles after encapsulation in a biocompatible polyethyleneglycol 1,2-distearoyl-sn-glycero-3-phosphoethanolamine-N-[methoxy-(polyethylene glycol)-2000]. Optimal PTA candidates combine strong NIR absorption provided by extended donor-acceptor conjugation and an optimization of the electronic and steric effects of meso-substituents to maximize photothermal conversion performance. The PTT-optimized meso-CF3-BODIPY, TCF3PEn exhibits exceptional efficacy in inducing cancer cell apoptosis and in vivo tumor ablation using low-power NIR laser irradiation (0.3 W cm-2, 808 nm) as well as excellent biological safety, underscoring its potential for advancing light-induced cancer therapies.

Development of a cold-start emission model for diesel vehicles using an artificial neural network trained with real-world driving data.

During the cold start and warm-up phase, modern vehicles emit considerable amounts of pollutants due to the incomplete combustion and deteriorated performance of aftertreatment devices. In terms of emission modeling, there have been many attempts to estimate cold start emission such as cold-hot conversion factor, regression model, and physis-based model. However, as the emission characteristic become complicated due to the adoption of aftertreatment devices and various emission control strategies for the strengthened emission regulations, the conventional cold start emission models do not always show satisfactory performances. In this study, artificial neural networks were used to predict the cold start emissions of carbon dioxide, nitrogen oxides, carbon monoxide, and total hydrocarbon of diesel passenger vehicles. We used real-world driving data to train neural networks as an emission prediction tool. Through machine leaning, numerous trainable variables of neural networks were properly adjusted to predict cold start emissions. For input variables of the ANN model, the velocity, vehicle specific power, engine speed, engine torque, and engine coolant temperature were used. The proposed ANN models accurately predicted sharp increases in carbon monoxide, hydrocarbon, and nitrogen oxides during the cold start phase. In addition to the quantitative estimations, the correlations between the operating variables and exhaust gas emissions were visually described in the form of emission maps. The emission map graphically showed the emission levels according to the vehicle and engine operating parameters.

Prediction of instantaneous real-world emissions from diesel light-duty vehicles based on an integrated artificial neural network and vehicle dynamics model.

This paper presents a road vehicle emission model that integrates an artificial neural network (ANN) model with a vehicle dynamics model to predict the instantaneous carbon dioxide (CO2), nitrogen oxides (NOx) and total hydrocarbon (THC) emissions of diesel light-duty vehicles. Real-world measurement data were used to train a multi-layer feed-forward ANN model. The optimal combination of the various experimental variables was selected as the ANN input through a parametric study considering both practicality and accuracy. For CO2 prediction, two variables (engine speed and engine torque) are enough to develop an accurate ANN model. In order to achieve satisfactory accuracy for CO and NOx prediction, more variables were used for ANN training. The trained ANN model was used to predict road vehicle emissions by integrating the vehicle dynamics model, which was used as a supplementary tool to produce ANN input data. The integrated model is practical because it requires relatively simple data for input such as vehicle specifications, velocity, and road gradient. In the accuracy validation, the proposed model showed satisfactory prediction accuracy for road vehicle emissions.

Estimating the effectiveness of vehicle emission regulations for reducing NOx from light-duty vehicles in Korea using on-road measurements.

The South Korean government has reinforced emission regulations for newly manufactured vehicles to reduce air pollution from automobiles. The government has applied different emission regulations depending on the fuel, following the regulations set for gasoline vehicles in California, USA, and those set for diesel vehicles in the European Union (EU). In this study, the on-road NOx emissions of 109 light-duty vehicles in South Korea were measured on roads in Seoul and the surrounding metropolitan area using a portable emissions measurement system (PEMS). The results were then analyzed to evaluate the effectiveness of the emission regulations introduced in Korea for NOx reduction. The average on-road NOx emissions for the Euro 5 and Euro 6b diesel vehicles were approximately five times higher than the laboratory emission limits set by the EU regulation. The NOx emissions also showed significant variation depending on the driving parameters, such as the driving dynamics and the ambient temperature. From the Euro 6d-TEMP regulation in which the real driving emissions-light duty vehicles (RDE-LDV) regulatory package was implemented, the average on-road NOx emissions from the diesel vehicles were controlled within the laboratory emission limits, but were still higher than those of the gasoline vehicles. Despite the absence of the RDE-LDV regulations, the average on-road NOx emissions of the gasoline vehicles that had ultra-low emission vehicle (ULEV) and super ultra-low emission vehicle (SULEV) standard certifications were controlled within the laboratory emission limits set by the FTP-75, regardless of the various driving parameters. The results of this study show that it is necessary to include a wide range of driving conditions in emission certification test procedures, such as RDE-LDV, and enhance the regulatory measures that enable manufacturers to maintain the effectiveness of emission control systems.