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An effective method for the production of fuels and chemicals from microalgae is to ferment the carbohydrate fraction, extract the lipids and convert the resulting solids through hydrothermal liquefaction (HTL).
The growing demand but uncertain supply makes the sustainability of the coffee industry a shared concern for all participants along the coffee supply chain. This study proposed a decision-making model that comprises the cultivation management, including shade management and annual agriculture management, and the supply chain logistics. A two-stage stochastic program is presented and used within a rolling horizon scheme that periodically updates input data information to deal with uncertainty associated with future climate scenarios. The program minimizes the total expected cost of the entire supply chain of arabica coffee. The study applied the model to the real case study of arabica coffee bean supply to the U.S. market, trying to answer whether arabica coffee supply can meet the U.S. demand from 2022 to 2050 and how to best mitigate any shortage through corporate-farmer partnerships. The results show that the coffee supply will have a 3% shortage in the future; medium-level shade management and more irrigation and fertilization are possible mitigation strategies. These results demonstrate the need for all participants to adopt suitable technologies for the sustainability of global coffee supply chains together.
Single-serve machines have proven to be a rapid and convenient mechanism for preparing coffee for consumption. However, disposing the single-use coffee pods accompanying each use creates insurmountable waste in landfills. With the introduction of biobased products being certified as industrially compostable, there is scope for an effective waste stream for nearly all biobased products that avoids adding to landfills. The case presented in this paper demonstrates the success of composting compostable coffee pods within a local industrial-scale composting facility. Utilizing the existing local composting facility at the University of Tennessee–Knoxville, a life cycle assessment was performed to calculate the overall embodied energy and related environmental impact(s) to determine the feasibility of using compostable coffee pods over conventional plastic ones. Testing showed complete degradation within 46 days, proving composting to be a feasible waste stream option and a sustainable marketing edge while treading the path toward a circular economy. Cost savings of 21% were realized in terms of waste disposal, in addition to creating a value-added product at the end of the coffee pods life cycle, with nutrient-rich compost being recirculated to campus gardens and farms.
Coffee is a widely consumed beverage, but approximately 80% mass of coffee cherry is discarded as waste. Here, in this study, spent coffee grounds (SCG) were recycled and used as a natural filler in a polyester-based epoxy matrix made from reactions of diglycidyl ethers of Bisphenol A and polyethylene glycol with methyl nadic anhydride. The properties of SCG/epoxy composites with various SCG contents up to 40% w/w were evaluated using various techniques such as FT-IR spectroscopy, tensile testing, optical microscopy, differential scanning calorimetry, and rheology. The SCG filled polyester-based epoxy matrix demonstrated excellent dimensional stability and retained a permanently crosslinked network, but re-shaping was possible at ~150 °C through transesterification, which occurred due to a dynamic chain exchange reaction between the ester group of the epoxy matrix and the hydroxyl group of the SCG fillers. The transesterification also improved the interfacial adhesion between the matrix and SCG fillers, resulting in tensile strength of over 20 MPa and a modulus of around 2.5 GPa, even with high SCG contents up to 40% w/w. While neat, cured epoxy shows thermoset behavior, filling the matrix with SCG enables reforming and origami-inspired spontaneous change in shape.
Coffee is among the world's most widely consumed beverages, generating 18 million wet tonnes of waste spent coffee grounds annually. Disposal of SCGs is complicated by their high moisture content, their recalcitrant chemical composition, and the presence of caffeine and other bioactive compounds. Dark fermentation is a promising technology for conversion of waste cellulosic biomass into renewable hydrogen, carbon dioxide, and volatile fatty acids. Typical SCGs have high moisture content, are pre-milled to fine particle sizes, and contain 8%-19% cellulose and up to 40% hemicellulose, making them an attractive feedstock for dark fermentation. In this study, we investigate biohydrogen production from unpretreated SCGs using Clostridium thermocellum strain KJC19-9, a cellulolytic bacterium engineered to co-utilize xylose, specifically examining the inhibitory effects of residual caffeine and high solids loadings on microbial growth and hydrogen production efficiency. While SCGs were initially resistant to both cell growth and biohydrogen production, a strategy to co-ferment with low concentrations of cellobiose dramatically enhanced process performance, reducing lag phase duration and enabling cellulosome production for efficient cellulose hydrolysis. This co-substrate approach generated up to 944 mL hydrogen per L reaction from 50 g/L of spent coffee grounds over 120 h (1.34 mol H2/mol carbohydrate), validating SCGs as a promising dark fermentation feedstock and underscoring the value of mixed substrate approaches in consolidated bioprocessing.
Abstract Several pests affect coffee (Coffea spp., Rubiaceae) and macadamia, Macadamia integrifolia Maiden & Betche (Proteaceae) in Hawaii. The coffee berry borer, Hypothenemus hampei (Ferrari) (Coleoptera: Curculionidae: Scolytinae), is the most damaging to coffee, while the tropical nut borer, Hypothenemus obscurus (Fabricius) (Coleoptera: Curculionidae: Scolytinae), is one of the worst pests of macadamia. This paper investigates the potential efficacy of a long-lasting insecticide-incorporated net (LLIN) under laboratory conditions to manage these pests. The LLIN (40 denier with mesh size 625 knots/in²), incorporated with α-cypermethrin (0.34%), was excised into 100 mm circles and inserted in 100 mm Petri dishes. Nets with the same quality but without insecticides were used as control treatments. Twenty beetles (H. obscurus or H. hampei) each were placed on the treated and non-treated netting at 4 treatment or exposure hours—1, 6, 12, and 24—with 5 replicates. Subsequently, the beetles were ranked alive, affected, or dead. The results showed that the LLIN with α-cypermethrin had significant lethal and sub-lethal effects on both Hypothenemus species, causing over 90% mortality after 24 h of exposure and paralysis after 1, 6, and 12 h of exposure. The highest lethality value was recorded after 24 h of exposure for both H. obscurus and H. hampei. The LT50 of H. obscurus and H. hampei was 18.78 min and 2.15 h, respectively, while the LT90 values were 32.11 and 20.67 h. These results imply the potential effectiveness of LLINs with α-cypermethrin for management of H. obscurus and H. hampei, but field studies are warranted for optimization.
Coffee (Coffea species) and Cocoa (Theobroma cacao) are important cash crops grown in the tropics but traded globally. This study was conducted to apply the ALMANAC model to these crops for the first time, and to test its ability to simulate them under agroforestry management schemes and varying precipitation amounts. To create this simulation, coffee was grown on a site in Kaua’i, Hawai’i, USA, and cocoa was grown on a site in Sefwi Bekwai, Ghana. A stand-in for a tropical overstory tree was created for agroforestry simulations using altered parameters for carob, a common taller tropical tree for these regions. For both crops, ALMANAC was able to realistically simulate yields when compared to the collected total yield data. On Kaua’i, the mean simulated yield was 2% different from the mean measured yield, and in all three years, the simulated values were within 10% of the measured values. For cocoa, the mean simulated yield was 3% different from the mean measured yield and the simulated yield was within 10% of measured yields for all four available years. When precipitation patterns were altered, in Ghana, the wetter site showed lower percent changes in yield than the drier site in Hawai’i. When agroforestry-style management was simulated, a low Leaf Area Index (LAI) of the overstory showed positive or no effect on yields, but when LAI climbed too high, the simulation was able to show the detrimental effect this competition had on crop yields. These simulation results are supported by other literature documenting the effects of agroforestry on tropical crops. This research has applied ALMANAC to new crops and demonstrated its simulation of different management and environmental conditions. The results show promise for ALMANAC’s applicability to these scenarios as well as its potential to be further tested and utilized in new circumstances.
This project will evaluate the functionality of Object Research Dragonfly software’s user produced deep learning (DL) models on Computed Tomography (CT) scanned coffee beans from green (unroasted) through a dark roast. DL models will be expected to identify voids within the coffee beans, and identify the level of roast of the bean from the CT reconstruction. The scope of this project is intended to meet the Capstone Project requirements of University of California San Diego (UCSD) Structural Engineering master’s degree and offer useful insight on Dragonfly’s DL capability for LANL’s Non-Destructive Evaluation (NDE) CT team. The results of this project will be presented to the E-6 NDE group within LANL. Data acquisition was completed with a North Star Imaging (NSI) X-25 CT Cabinet.
A resin-wafer electrodeionization (RW-EDI) apparatus for purifying water for coffee brewing comprises a cathode; an anode; and multiple porous solid resin wafer exchange units arranged in a stack between the cathode and the anode, and an air distributor adapted and arranged to aerate the water to be purified. Each unit comprises a monovalent cation exchange membrane (CEM), an anion exchange membrane (AEM), and an ion exchange resin wafer between the CEM and the AEM, which is in contact with, and in fluid flow connection with the CEM and AEM. Each resin wafer comprises a cation exchange resin and an anion exchange resin. The units are oriented with the CEM facing the cathode and the AEM facing the anode, with space between the units defining ion concentrate chambers. Bipolar ion exchange membranes separate the anode and cathode from their nearest resin wafer exchange units.
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Espresso is a beverage brewed using hot, high-pressure water forced through a bed of roasted coffee. Despite being one of the most widely consumed coffee formats, it is also the most susceptible to variation. Here, we report a novel model, complimented by experiment, that is able to isolate the contributions of several brewing variables, thereby disentangling some of the sources of variation in espresso extraction. Under the key assumption of homogeneous flow through the coffee bed, a monotonic decrease in extraction yield with increasingly coarse grind settings is predicted. However, experimental measurements show a peak in the extraction yield versus grind setting relationship, with lower extraction yields at both very coarse and fine settings. This result strongly suggests that inhomogeneous flow is operative at fine grind settings, resulting in poor reproducibility and wasted raw material. With instruction from our model, we outline a procedure to eliminate these shortcomings.
The current linear “take-make-waste-extractive” model leads to the depletion of natural resources and environmental degradation. Circular Economy (CE) aims to address these impacts by building supply chains that are restorative, regenerative, and environmentally benign. This can be achieved through the re-utilization of products and materials, the extensive usage of renewable energy sources, and ultimately by closing any open material loops. Such a transition towards environmental, economic and social advancements requires analytical tools for quantitative evaluation of the alternative pathways. Here, in this work, we present a novel CE system engineering framework and decision-making tool for the modeling and optimization of food supply chains. First, the alternative pathways for the production of the desired product and the valorization of wastes and by-products are identified. Then, a Resource-Task-Network representation that captures all these pathways is utilized, based on which a mixed-integer linear programming model is developed. This approach allows the holistic modeling and optimization of the entire food supply chain, taking into account any of its special characteristics, potential constraints as well as different objectives. Considering that typically CE introduces multiple, often conflicting objectives, we deploy here a multi-objective optimization strategy for trade-off analysis. A representative case study for the supply chain of coffee is discussed, illustrating the steps and the applicability of the framework. Single and multi-objective optimization formulations under five different coffee-product demand scenarios are presented. The production of instant coffee as the only final product is shown to be the least energy and environmental efficient scenario. On the contrary, the production solely of whole beans sets a hypothetical upper bound on the optimal energy and environmental utilization. In both problems presented, the amount of energy generated is significant due to the utilization of waste generated for the production of excess energy.
Abstract Despite significant progress in solution‐processing of 2D materials, it remains challenging to reliably print high‐performance semiconducting channels that can be efficiently modulated in a field‐effect transistor (FET). Herein, electrochemically exfoliated MoS 2 nanosheets are inkjet‐printed into ultrathin semiconducting channels, resulting in high on/off current ratios up to 10 3 . The reported printing strategy is reliable and general for thin film channel fabrication even in the presence of the ubiquitous coffee‐ring effect. Statistical modeling analysis on the printed pattern profiles suggests that a spaced parallel printing approach can overcome the coffee‐ring effect during inkjet printing, resulting in uniform 2D flake percolation networks. The uniformity of the printed features allows the MoS 2 channel to be hundreds of micrometers long, which easily accommodates the typical inkjet printing resolution of tens of micrometers, thereby enabling fully printed FETs. As a proof of concept, FET water sensors are demonstrated using printed MoS 2 as the FET channel, and printed graphene as the electrodes and the sensing area. After functionalization of the sensing area, the printed water sensor shows a selective response to Pb 2+ in water down to 2 ppb. This work paves the way for additive nanomanufacturing of FET‐based sensors and related devices using 2D nanomaterials.
Ceramic On-Demand Extrusion (CODE) is a direct ink writing process which allows for the creation of near theoretically dense ceramic components with large cross-sections due to oil-assisted drying. Here, Yttria-stabilized zirconia (YSZ) colloidal pastes were used in CODE to produce dense (multi-road infill and ≳ 98% relative density), large continuous volume (> 1 cm 3 ), and high fidelity (nozzle diameters ≲ 1 mm) structural ceramic components with nanoparticle feedstocks (~d 50 ≲ 1 µm). However, many of these printed components underwent significant particle migration after forming. The reason for this particle migration defect was investigated using the coffee-ring effect for dilute solutions and rheological methods for dense suspensions. Modifications to the colloidal paste, such as changes in solids loading, pH, or surfactant concentration were explored as to their effectiveness to mitigate the defect. Ultimately, paste formulation and printing trade-offs are discussed with respect to the post-printing defect and as to general direct-write patterning.
Drying of porous media is governed by a combination of evaporation and movement of the liquid phase within the porous structure. Contact angle hysteresis induced by surface roughness is shown to influence multi-phase flows, such as contact line motion of droplet, phase distribution during drainage and coffee ring formed after droplet drying in constant contact radius mode. However, the influence of contact angle hysteresis on liquid drying in porous media is still an unanswered question. Lattice Boltzmann model (LBM) is an advanced numerical approach increasingly used to study phase change problems including drying. In this paper, based on a geometric formulation scheme to prescribe contact angle, we implement a contact angle hysteresis model within the framework of a two-phase pseudopotential LBM. The capability and accuracy of prescribing and automatically measuring contact angles over a large range are tested and validated by simulating droplets sitting on flat and curved surfaces. Afterward, the proposed contact angle hysteresis model is validated by modeling droplet drying on flat and curved surfaces. Then, drying of two connected capillary tubes is studied, considering the influence of different contact angle hysteresis ranges on drying dynamics. Finally, the model is applied to study drying of a dual-porosity porous medium, where phase distribution and drying rate are compared with and without contact angle hysteresis. The proposed model is shown to be capable of dealing with different contact angle hysteresis ranges accurately and of capturing the physical mechanisms during drying in different porous media including flat and curved geometries.
In this work, an automated image analysis tool is developed to establish quantitative correlations between the particle/cluster size distribution and the mechanical properties of particle reinforced polymer composites (PRPC).This automated image analysis tool is developed within python programming software to process and analyze the microstructural images of the polymerbased composite materials. The spent coffee bean powder (SCBP) reinforced poly-propylene carbonate (PPC) polymer composite with differing wt.% of the filler is selected for the analysis. Detailed statistical analysis of the microstructural images reveals that ‘clustering of clusters’ is also presented in addition to the most commonly reported ‘clustering of particles’, and the distribution of particle/clusters is bimodal. Based on these findings, an effective volume fraction for the filler material is proposed to mainly capture the agglomeration effect. With this effective volume fraction, the standard rule-of-mixture model correctly captures the experimentally measured tensile strength and modulus as a function of filler wt.%. Further, the applicability of this effective volume fraction for other theoretical models is also analyzed. The detailed statistical analysis of the microstructure and the proposed effective volume fraction helps to develop a deeper quantitative understanding of the PRPC than the conventional qualitative correlation of microstructural features with the properties and failure processes.