A game theoretic approach to contract-based enviro-economic coordination of wood pellet supply chains for bioenergy production.

Wood pellets have gained global attention due to their economic availability and increasing demand for bioenergy as part of sustainable energy solutions. Management of the wood pellet supply chains, from feedstock harvesting to bioenergy conversion, is critical to ensure competitiveness in the energy markets. In this regard, wood pellets supply chain coordination can play a strategic role in enhancing the efficiency and reliability of bioenergy generation. This study proposes a contract-based coordination mechanism for wood pellet supply chains and compares its performance in alternative centralized and decentralized decision-making structures. A bi-level nonlinear game-theoretic approach with two economic and environmental objective functions is developed. It utilizes the concept of life cycle assessment in a Stackelberg leader-follower game to obtain the bioenergy equilibrium solutions. Further, this study examines the case of wood pellet supply chains in three remote Canadian communities. The aim is to showcase the practicality and significance of the proposed approach and interpret the findings. By focusing on these communities, the crucial role of supply chain coordination in fostering sustainable development, particularly, in the context of bioenergy generation is emphasized. The study colludes by advocating a number of avenues for future research.

Impact of forest harvest intensity and transportation distance on biomass delivered costs within sustainable forest management - A case study in southeastern Canada.

This study compares the delivered cost of forest biomass and its associated GHG emissions for three sizes of biorefinery including 50,000 m3 (small scale), 250,000 m3 (medium scale), and 700,000 m3 (large scale). The proposed methodology in this study includes harvest intensity which is often overlooked. The Pontiac region located in the Province of Quebec (Southeastern Canada) is used as a case study due to the availability of data in this forestry biomass rich region. Furthermore, there are significant similarities with other forestry regions to enable generalisation of the proposed case study. Harvest intensities of 423 harvest zones (cutblocks) are considered in cost and GHG emissions analysis of delivered biomass from each cutblock to the biorefinery. The results show that harvest intensities of cutblocks must be prioritized over conventional parameters such as transportation distance. The selection and prioritisation of cutblocks according to transportation distance without considering harvest intensities would result in an increase of about 12.5% in delivered costs of biomass for small and medium scale biorefineries. Results also reveal that the transportation distance would be a more significant parameter when using the same harvest intensity for all the selected cutblocks. Required logistics and harvesting equipment for three biorefinery sizes were also quantified. Sensitivity analysis shows that reduced productivity of harvest equipment by 20% could increase the delivered costs of biomass and GHG emissions by 10% for medium and large scale biorefineries and by 13% for a small scale biorefinery.

Modeling and prediction of regional municipal solid waste generation and diversion in Canada using machine learning approaches.

The main objective of this study was to develop models for accurate prediction of municipal solid waste (MSW) generation and diversion based on demographic and socio-economic variables, with planned application of generating Canada-wide MSW inventories. Models were generated by mapping residential MSW quantities with socio-economic and demographic parameters of 220 municipalities in the province of Ontario, Canada. Two machine learning algorithms, namely decision trees and neural networks, were applied to build the models. Socio-economic variables were derived from Canadian Census data at regional and municipal levels. A data pre-processing and integration framework was developed in Matlab® computing software to generate datasets with sufficient data quantity and quality for modeling. Results showed that machine learning algorithms can be successfully used to generate waste models with good prediction performance. Neural network models had the best performance, describing 72% of variation in the data. The approach proposed in this study demonstrates the feasibility of creating tools that helps in regional waste planning by means of sourcing, pre-processing, integrating and modeling of publically available data from various sources.

Electro-optic investigation of the surface trapping efficiency in n-alkanethiol SAM passivated GaAs(001).

The electro-optic characteristics of the semi-insulating and n(+)-type GaAs(001) surfaces passivated with n-alkanethiol self-assembled monolayers were investigated using Kelvin probe surface photovoltage (SPV) and photoluminescence (PL) techniques. Referencing the equilibrium surface barrier height established in an earlier report, SPV measurements demonstrated a significant (>100 mV) increase in the non-equilibrium band-bending potential observed under low-level photo-injection. Modeling of the SPV accounts for these observations in terms of a large (>10(4)) decrease in the hole/electron ratio of surface carrier capture cross-sections, which is suggested to result from the electrostatic potential of the interfacial dipole layer formed upon thiol chemisorption. The cross-section effects are verified in the high-injection regime based on carrier transport modeling of the PL enhancement manifested as a reduction of the surface recombination velocity.

Surface dipole layer potential induced ir absorption enhancement in n-alkanethiol SAMs on GaAs(001).

The work function of n-alkanethiol self-assembled monolayers (SAMs) prepared on the GaAs(001) surface was measured using the Kelvin probe technique yielding the SAM 2D dipole layer potential (DLP). Direct n-dependent proportionality between the DLP values and the C-H stretching mode infrared (IR) absorption intensities was observed, which supports a correspondence of reported IR enhancements with the electrostatic properties of the interface. X-ray photoelectron spectroscopy is also used to verify the work function measurements. In addition, the principal components of the refractive index tensor are shown to be n-invariant in the ordered SAM phase. Our results suggest that a local field correction to the transition dipole moment accounts for the observed increase in IR activity through an increase to the electronic polarizability.

Nanotemplating for two-dimensional molecular imprinting.

A new 2D molecular imprinting technique based on nanotemplating and soft-lithography techniques is reported. This technique allows the creation of target-specific synthetic recognition sites on different substrates using a uniquely oriented and immobilized template and the attachment of a molecularly imprinted polymer on a substrate. The molecularly imprinted polymer was characterized by AFM, fluorescence microscopy, and ATR-FTIR. We evaluated the rebinding ability of the sites with theophylline (the target molecule). The selectivity of the molecularly imprinted polymer was determined for the theophylline-caffeine couple. The molecularly imprinted polymer exhibited selectivity for theophylline, as revealed by competitive rebinding experiments. Fluorescence microscopy experiments provided complementary proof of the selectivity of the molecularly imprinted polymer surfaces toward theophylline. These selective molecularly imprinted polymers have the potential for chemical sensor applications. Because of its 2D nature, this novel chemical sensor technology can be integrated with many existing high-sensitivity multichannel detection technologies.

Microwave synthesis of polymer-embedded Pt-Ru catalyst for direct methanol fuel cell.

Platinum-ruthenium nanoparticles stabilized within a conductive polymer matrix are prepared using microwave heating. Polypyrrole di(2-ethylhexyl) sulfosuccinate, or PPyDEHS, has been chosen for its known electrical conductivity, thermal stability, and solubility in polar organic solvents. A scalable and quick two-step process is proposed to fabricate alloyed nanoparticles dispersed in PPyDEHS. First a mixture of PPyDEHS and metallic precursors is heated in a microwave under reflux conditions. Then the nanoparticles are extracted by centrifugation. Physical characterization by TEM shows that crystalline and monodisperse alloyed nanoparticles with an average size of 2.8 nm are obtained. Diffraction data show that crystallite size is around 2.0 nm. Methanol electro-oxidation data allow us to propose these novel materials as potential candidates for direct methanol fuel cells (DMFC) application. The observed decrease in sulfur content in the polymer upon incorporation of PtRu nanoparticles may have adversely affected the measured catalytic activity by decreasing the conductivity of PPyDEHS. Higher concentration of polymer leads to lower catalyst activity. Design and synthesis of novel conductive polymers is needed at this point to enhance the catalytic properties of these hybrid materials.