Mohammad Iskandar Petra

Potential natural sensitizers extracted from the skin of Canarium odontophyllum fruits for dye-sensitized solar cells.

Possibility of use of dye extract from skin samples of a seasonal, indigenous fruit from Borneo, namely Canarium odontophyllum, in dye sensitized solar cells (DSSCs) are explored. Three main groups of flavonoid pigments are detected and these pigments exhibit different UV-vis absorption properties, and hence showing different light harvesting capabilities. When applied in DSSCs. The detected pigment constituents of the extract consist of aurone (maritimein), anthocyanidin (pelargonidin) and anthocyanidin (cyanidin derivatives). When tested in DSSC, the highest conversion efficiency of 1.43% is exhibited by cyanidin derivatives, and this is followed by conversion efficiencies of 0.51% and 0.79% for aurone and pelargonidin, respectively. It is shown that individual pigments, like cyanidin derivatives and pelargonidin, exhibit higher power conversion efficiency when compared to that of C.odontophyllum skin pigment mixture (with a conversion efficiency of only 0.68%). The results indicate a possibility of masking effects of the pigments when used as a mixture. The acidification of C.odontophyllum skin pigments with concentrated hydrochloric acid improves the conversion efficiency of the mixture from 0.68% to 0.99%. The discussion in this paper will draw data and observations from the variation in absorption and adsorption properties, the HOMO-LUMO levels, the energy band gaps and the functional group compositions of the detected flavonoids.

Equilibrium Isotherm Studies of Adsorption of Pigments Extracted from Kuduk-kuduk (Melastoma malabathricum L.) Pulp onto TiO2 Nanoparticles

The adsorption of natural pigments onto TiO2 nanoparticles was investigated. The pigments were extracted from the dark purple colored pulp of the berry-like capsule of Kuduk-kuduk (Melastoma malabathricum L.). The Langmuir, Freundlich, Redlich-Peterson, and Sips isotherm models were used to analyze the equilibrium experimental data. Data correlated well with the Sips isotherm model, where the heterogeneity factor ( ) indicated heterogeneous adsorption characteristics, with a maximum adsorption capacity of 0.0130 mg/g. The heterogeneous adsorption character was further supported by results obtained from zeta-potential measurements. When a dye-sensitized solar cell (DSSC) was sensitized with the extracted pigment, the photo-energy conversion efficiency was measured to be 0.83%, thus proving the suitability of Kuduk-kuduk fruit pulp as a sensitizer in DSSCs.

DFT/TDDFT and Experimental Studies of Natural Pigments Extracted from Black Tea Waste for DSSC Application

We report results of combined experimental and theoretical studies of black tea waste extract (BTE) as a potential sensitizer for TiO2-dye-sensitized solar cells (DSSCs). UV-vis absorption data revealed that BTE contains theaflavin. DSSC sensitized with pigment complexes of BTE showed a photon-energy conversion efficiency of %, while a significant increase (%) is observed when pH of the pigment solution was lowered. The HOMO and LUMO energy levels were calculated using experimental data of UV-vis absorption and cyclic voltammetry. These calculations revealed a reduction of the band gap by 0.17 eV and more negativity of HOMO level of acidified pigment, compared to that of original pigment. Combined effect of these developments caused the enhanced efficiency of DSSC. Density functional theory (DFT) and time-dependent density functional theory (TDDFT) computational calculations were carried out to study the four theaflavin analogues which are responsible for the dark colour of BTE. According to the calculations, two theaflavin analogues, theaflavin and theaflavin digallate, are the most probable sensitizers in this dye-sensitized solar cell system.

Influence of Southeast Asian Haze episodes on high PM10 concentrations across Brunei Darussalam

Particulate matter (PM10) is the key indicator of air quality index in Brunei Darussalam and the principal pollutant for haze related episodes in Southeast Asia. This study examined the temporal and spatial distribution of PM10 base on a long-term monitoring data (2009-2014) in order to identify the emission sources and favorable meteorological conditions for high PM10 concentrations across the country. PM10 concentrations measured at the various locations differ significantly but the general temporal characteristics show clear patterns of seasonal variations across the country with the highest concentrations recorded during the southwest monsoon. The high PM10 values defined in the study were not evenly distributed over the years but occurred mostly within the southwest monsoon months of June to September. Further investigations with bivariate polar concentrations plots and k-means clustering demonstrated the significant influence of Southeast Asian regional biomass fires on the high PM10 concentrations recorded across the country. The results of the polar plots and cluster analyses were further confirmed by the evaluations with Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) backward air masses trajectories analysis and the Moderate Resolution Imaging Spectroradiometer (MODIS) fire records. Among the meteorological variables considered, temperature, rainfall and relative humidity were the most important meteorological variables that influence the concentration throughout the year. High PM10 values are associated with high temperatures and low amounts of rainfall and relative humidity. In addition, wind speed and direction also play significant role in the recorded high PM10 concentrations and were mainly responsible for its seasonality during the study period.

Cyanidin-Based Novel Organic Sensitizer for Efficient Dye-Sensitized Solar Cells: DFT/TDDFT Study

Cyanidin is widely considered as a potential natural sensitizer in dye-sensitized solar cells due to its promising electron-donating and electron-accepting abilities and cheap availability. We consider modifications of cyanidin structure in order to obtain broader UV-Vis absorption and hence to achieve better performance in DSSC. The modified molecule consists of cyanidin and the benzothiadiazolylbenzoic acid group, where the benzothiadiazolylbenzoic acid group is attached to the cyanidin molecule by replacing one hydroxyl group. The resulting structure was then computationally simulated by using the Spartan’10 software package. The molecular geometries, electronic structures, absorption spectra, and electron injections of the newly designed organic sensitizer were investigated in this work through density functional theory (DFT) and time-dependent density functional theory (TDDFT) calculations using the Gaussian’09W software package. Furthermore, TDDFT computational calculations were performed on cyanadin and benzothiadiazolylbenzoic acid separately, as reference. The computational studies on the new sensitizer have shown a reduced HOMO-LUMO gap; bathochromic and hyperchromic shifts of absorption spectra range up to near-infrared region revealing its enhanced ability to sensitize DSSCs.

PV power predictors for condition monitoring

In countries such as India with low grid prices, energy firms are offering competitive PPA tariffs by setting up large solar farms. Given lower margins in operating these farms, there is great sensitivity to panels underperforming. To detect under-performance, existing condition monitoring methods compare generated power with an ideal yield calculated based on local weather conditions. Applying such methods to a 1.2MW farm with 6 different PV technologies over 3 years, we observed prediction errors large enough to mask under-performance. To reduce these errors, this work proposes two approaches. Firstly, a piecewise regression approach is proposed which improves estimation accuracy by applying a set of regression models, each corresponding to a partition of the predictor space. This helps capture the inherent non-linearities in PV power output. Secondly, we explicitly model the Maximum Power Point Tracker (MPPT) in a two-step prediction method. In doing so, we combine a regression method on irradiance data with physical modeling of I-V characteristics of panels, resulting in an average reduction in error by about 16%. While the piecewise regression approach requires only power measurements, the I-V approach requires both voltage and ampere measurements. The proposed predictors may be used to monitor the performance of solar farms, leading to timely identification of operational problems and aging.

Combining data with physics to monitor solar panels

In countries such as India with low grid prices, energy firms are offering competitive PPA tariffs for solar farms. Given lower margins in operating these farms, there is great sensitivity to panels under-performing. To detect under-performance, condition-monitoring methods compare generated power with an ideal yield calculated for localised weather. Applying such methods to a 1.2MW farm with 6 different PV technologies over 3 years, we observed prediction errors large enough to mask under-performance. To reduce this error, we explicitly modelled the Maximum Power Point Tracker (MPPT) in a two-step prediction method. In doing so, we combine a regression method on weather data with physical modelling of IV-characteristics of panels, resulting in an average reduction in error by 16%.