Design Of Generation Research Articles

Interface engineering of Co3O4 nanoparticle for high-efficiency interfacial solar steam generation

Solar steam generation has been proven to be an innovative and sustainable technology to mitigate the issue of clean water shortage. But, to-date, optimizing a suitable candidate material system to improve the evaporation efficiency is an urgent need, especially the interface engineering tailored heat transfer of materials has not yet made a breakthrough. Herein, we successfully designed a Co3O4 nanoparticle that can promote the optimization of the heat transfer performance using interface engineering. The optimized Co3O4 nanoparticles creates a close contact with the cotton sheet, creating an efficient channel for heat transfer and achieving high photothermal steam generation efficiency. The interfacial solar steam generation has been found to achieve a high evaporation rate of 2.72 kg/m2·h, which is higher than that of traditional bulk phase heating. Our findings not only showcase the efficacy of interface engineering in the design of solar steam generation, but also present a cost-effective and straightforward methodology that can be applied for water purification purposes.

SERS as a Probe of Surface Chemistry Enabled by Surface-Accessible Plasmonic Nanomaterials.

ConspectusWhen the size of materials is reduced, their volume decreases much faster than their surface area, which in the most extreme case leads to 2D nanomaterials which are "all surface". Since atoms at the surface have free energies, electronic states, and mobility which are very different from bulk atoms, nanomaterials that have large surface-to-volume ratios can display remarkable new properties compared to their bulk counterparts. More generally, the surface is where nanomaterials interact with their environment, which in turn places surface chemistry at the heart of catalysis, nanotechnology, and sensing applications. Understanding and utilizing nanosurfaces are not possible without appropriate spectroscopic and microscopic characterization techniques. An emerging technique in this area is surface-enhanced Raman spectroscopy (SERS), which utilizes the interaction between plasmonic nanoparticles and light to enhance the Raman signals of molecules near the nanoparticles' surfaces. SERS has the great advantage that it can provide detailed in situ information on surface orientation and binding between molecules and the nanosurface. A long-standing dilemma that has limited the applications of SERS in surface chemistry studies is the choice between surface-accessibility and plasmonic activity. More specifically, the synthesis of metal nanomaterials with strong plasmonic and SERS-enhancing properties typically involves the use of strongly adsorbing modifier molecules, but these modifiers also passivate the surface of the product material, which prevents the general application of SERS in the analysis of weaker molecule-metal interactions.In this Account, we discuss our efforts in the development of modifier-free synthetic approaches to synthesize surface-accessible, plasmonic nanomaterials for SERS. We start by discussing the definition of "modifiers" and "surface-accessibility", especially in the context of surface chemistry studies in SERS. As a general rule of thumb, the chemical ligands on surface-accessible nanomaterials should be easily displaceable by a wide range of target molecules relevant to potential applications. We then introduce modifier-free approaches for the bottom-up synthesis of colloidal nanoparticles, which are the basic building blocks for nanotechnology. Following this, we introduce modifier-free interfacial self-assembly approaches developed by our group that allow the creation of multidimensional plasmonic nanoparticle arrays from different types of nanoparticle-building blocks. These multidimensional arrays can be further combined with different types of functional materials to form surface-accessible multifunctional hybrid plasmonic materials. Finally, we demonstrate applications for surface-accessible nanomaterials as plasmonic substrates for SERS studies of surface chemistry. Importantly, our studies revealed that the removal of modifiers led to not only significantly enhanced properties but also the observation of new surface chemistry phenomena that had been previously overlooked or misunderstood in the literature. Realizing the current limitations of modifier-based approaches provides new perspectives in manipulating molecule-metal interactions in nanotechnology and can have significant implications in the design and synthesis of the next generation of nanomaterials.

Retracted: Convolution-Based Design for Real-Time Pose Recognition and Character Animation Generation

Retracted: Convolution-Based Design for Real-Time Pose Recognition and Character Animation Generation

Silk nanofibrils-MOF composite membranes for pollutant removal from water

Membrane separation technology is considered an effective strategy to remove pollutants in sewage. However, it remains a significant challenge to fabricate inexpensive membranes with high purification efficiency. Therefore, the present study proposes the integration of silk nanofibrils (SNFs) and polydopamine⊂metal-organic framework (PDA⊂MOF) nanoparticles to prepare self-supporting membranes, which can effectively intercept nanoparticle pollutants through the size exclusion effect and can strongly adsorb organic dyes and metal ions by SNF. In addition, PDA⊂MOF enables these membranes to adsorb small molecules and heavy metal ions during the filtration process, thereby effectively removing various pollutants from sewage. The integration of size-exclusion and adsorption capabilities enables the SNF/PDA⊂MOF membrane to remove nanoparticles, small-molecule dyes, heavy metal ions, and radioactive elements. This work provides a rational approach for the design and development of the next generation of water treatment membranes and is expected to be used in environmental, food-related, and biomedical fields.

E-TEST: a compact low-frequency isolator for a large cryogenic mirror

To achieve the expected level of sensitivity of third-generation gravitational-wave (GW) observatories, more accurate and sensitive instruments than those of the second generation must be used to reduce all sources of noises. Amongst them, one of the most relevant is seismic noise, which will require the development of a better isolation system, especially at low frequencies (below 10 Hz), the operation of large cryogenic silicon mirrors, and the improvement of optical wavelength readouts. In this framework, this article presents the activities of the E-TEST (Einstein Telescope Euregio Meuse-Rhine Site & Technology) to develop and test new key technologies for the next generation of GW observatories. A compact isolator system for a large silicon mirror (100 kg) at low frequency (10 Hz) is proposed. The design of the isolator allows the overall height of the isolation system to be significantly compact and also suppresses seismic noise at low frequencies. To minimize the effect of thermal noise, the isolation system is provided with a 100 kg silicon mirror which is suspended in a vacuum chamber at cryogenic temperature (25–40 K). To achieve this temperature without inducing vibrations to the mirror, a radiation-based cooling strategy is employed. In addition, cryogenic sensors and electronics are being developed as part of the E-TEST to detect vibrational motion in the penultimate cryogenic stage. Since the commonly used silicon material is not transparent below the wavelengths typically used in the 1 µm range for GW detectors, new optical components and lasers must be developed in the range above 1500 nm to reduce absorption and scattering losses. Therefore, solid-state and fiber lasers with a wavelength of 2090 nm, matching high-efficiency photodiodes, and low-noise crystalline coatings are being developed. Accordingly, the key technologies provided by E-TEST serve crucially to reduce the limitations of the current generation of GW observatories and to determine the technical design for the next generation.

Development of multi-material hybrid yarns consisting of steel, glass and polypropylene filaments for fiber hybrid composites

With the increased use of fiber-reinforced composites (FRPs), the design of a new generation of composite structures with high stiffness and a ductile material behavior is required to cope with complex load scenarios and high damage tolerances. This can be achieved, in particular, by a combination of conventional FRPs, which possess high stiffness and strength, with metallic materials characterized by their high ductility and associated higher energy absorption capacity. Currently, there are no solutions for the hybridization of high-performance filament yarns, metal filament yarns or thermoplastic filament yarns at the micro level. Therefore, the aim of this paper is the hybridization of multi-fiber components intermixed at the micro scale. For this purpose, three hybridization processes were investigated. They are conventional air-jet technology, air-jet technology based on a modified air nozzle and an additional pre-spreading device and a process based on a newly developed multi-level-intermixing device. The effect of different processing parameters, such as air pressure, air nozzle and type of manufacturing, on the tensile properties, appearance and damage of multi-material hybrid yarn was analyzed. The results show that the developed multi-material hybrid yarns have high potential for use in composites with high requirements for crash and impact properties.

Neuron-Inspired Sticky Artificial Spider Silk for Signal Transmission.

Neurons exhibit excellent signal transmission capacity, which inspire artificial neuron materials for applications in the field of wearable electronics and soft robotics. In addition, the neuron fibers exhibit good mechanical robustness by sticking to the organs, which currently has rarely been studied. Here, a sticky artificial spider silk was developed by employing a proton donor-acceptor (PrDA) hydrogel fiber for application as artificial neuron fibers. Tuning the molecular electrostatic interactions by modulating the sequences of proton donors and acceptors, enables combination of excellent mechanical properties, stickiness, and ion conductivity. In addition, the PrDA hydrogel exhibits high spinning capacity for a wide range of donor-acceptor combinations. The PrDA artificial spider silk would shed light on the design of new generation of artificial neuron materials, bio-electrodes, and artificial synapses. This article is protected by copyright. All rights reserved.

A Multi-Scale method for combined design and dispatch optimization of nuclear hybrid energy systems including storage

Reliable, carbon-free power generation is increasing in importance. Nuclear-renewable hybrid energy systems (NHES) are a potential solution for current generation challenges, but design and dispatch optimization for these systems remains challenging particularly when stochastic effects, long time horizons and nonlinear modeling are needed. This work presents a multi-scale method for combining the design and dispatch optimization problems for nonlinear NHES over long time horizons. Rather than treating the entire horizon as a single optimization problem, a large number of combined optimization problems are solved for shorter samples of the horizon resulting in distributions of optimal capacities for each of the capacities being optimized. The distributions are then aggregated, and the techno-economics of each aggregate is analyzed. The result is a contextual knowledge about the techno-economic tradeoffs for the system rather than a single set of optimal capacities. This method is applied to two NHES of varying complexity. Results indicate that unit capacities for a grid-connected district energy system can be reduced by at least 18.9% by allowing for 3 periods of power import over the course of a 2000 day dispatch. The algorithm is used to solve combined optimization problems with dispatch horizons 112.5 times longer than are solvable directly.

Redox-Responsive Dendrimer Nanogels Enable Ultrasound-Enhanced Chemoimmunotherapy of Pancreatic Cancer via Endoplasmic Reticulum Stress Amplification and Macrophage Polarization.

Developing a multifunctional nanoplatform to achieve efficient theranostics of tumors through multi-pronged strategies remains to be challenging. Here, the design of the intelligent redox-responsive generation 3 (G3) poly(amidoamine) dendrimer nanogels (NGs) loaded with gold nanoparticles (Au NPs) and chemotherapeutic drug toyocamycin (Au/Toy@G3 NGs) for ultrasound-enhanced cancer theranostics is showcased. The constructed hybrid NGs with a size of 193nm possess good colloidal stability under physiological conditions, and can be dissociated to release Au NPs and Toy in the reductive glutathione-rich tumor microenvironment (TME). The released Toy can promote the apoptosis of cancer cells through endoplasmic reticulum stress amplification and cause immunogenic cell death to maturate dendritic cells. The loaded Au NPs can induce the conversion of tumor-associated macrophages from M2-type to antitumor M1-type to remodulate the immunosuppressive TME. Combined with antibody-mediated immune checkpoint blockade, effective chemoimmunotherapy of a pancreatic tumor mouse model can be realized, and the chemoimmunotherapy effect can be further ultrasound enhanced due to the sonoporation-improved tumor permeability of NGs. The developed Au/Toy@G3 NGs also enable Au-mediated computed tomography imaging of tumors. The constructed responsive dendrimeric NGs tackle tumors through a multi-pronged chemoimmunotherapy strategy targeting both cancer cells and immune cells, which hold a promising potential for clinical translations.

LS-MolGen: Ligand-and-Structure Dual-Driven Deep Reinforcement Learning for Target-Specific Molecular Generation Improves Binding Affinity and Novelty.

Deep learning-based molecular generative models have garnered considerable interest in the field of de novo drug design. However, most extant models focus on either ligand-based or structure-based strategies, thereby failing to effectively harness the combined knowledge derived from both ligands and the structure of the binding target. In this article, we introduce LS-MolGen, a novel ligand and structure-integrated molecular generative model. This model synergistically combines representation learning, transfer learning, and reinforcement learning. The targeted knowledge assimilation from transfer learning, coupled with an advanced exploration strategy in reinforcement learning, empowers LS-MolGen to efficiently generate novel and high-affinity molecules efficiently. The comparable performance of our model is affirmed through multiple evaluations, including EGFR, DRD3, CDK2, AA2AR, ADRB2, and a dedicated case study of inhibitor design for SARS-CoV-2 Mpro. The results indicate that LS-MolGen performs better than other ligand-based or structure-based generative models in de novo designing promising compounds with novel scaffolds and high binding affinity. This proof-of-concept study signifies the potential of our ligand- and structure-based generative model, LS-MolGen, as a promising new tool for target-specific molecular generation and drug design.

Auxeticity as a Mechanobiological Tool to Create Meta-Biomaterials.

Mechanical and morphological design parameters, such as stiffness or porosity, play important roles in creating orthopedic implants and bone substitutes. However, we have only a limited understanding of how the microarchitecture of porous scaffolds contributes to bone regeneration. Meta-biomaterials are increasingly used to precisely engineer the internal geometry of porous scaffolds and independently tailor their mechanical properties (e.g., stiffness and Poisson's ratio). This is motivated by the rare or unprecedented properties of meta-biomaterials, such as negative Poisson's ratios (i.e., auxeticity). It is, however, not clear how these unusual properties can modulate the interactions of meta-biomaterials with living cells and whether they can facilitate bone tissue engineering under static and dynamic cell culture and mechanical loading conditions. Here, we review the recent studies investigating the effects of the Poisson's ratio on the performance of meta-biomaterials with an emphasis on the relevant mechanobiological aspects. We also highlight the state-of-the-art additive manufacturing techniques employed to create meta-biomaterials, particularly at the micrometer scale. Finally, we provide future perspectives, particularly for the design of the next generation of meta-biomaterials featuring dynamic properties (e.g., those made through 4D printing).

A stretch hardening self-assembly strategy inspired by natural rubber for degradable, recyclable and self-repairing polyurethane elastomers

How to develop polyurethane elastomer with excellent mechanical properties and quick repair ability under mild conditions still needs to be overcome and improved. The transformation from static amorphous structure to dynamic crystalline state is an important factor for natural rubber to obtain high elasticity and mechanical properties. Therefore, with the aim of strengthening the dynamic crystalline phase of polycarbonate, a kind of high-strength and fast self-healing polyurethane elastomer with dual-phase transformation function was prepared by molecular conformation design. After testing, the mechanical strength of the elastomer is about 47 MPa, and the fracture energy is about 216 J/m2. Under mild conditions, rapid self-repair can be achieved within 1000 minutes, and the repair efficiency is as high as 95%, which is far higher than that of previously reported high toughness materials. Through the microstructure test, it is found that ultimate tensile hardening strategy of elastomers is the key to obtain high mechanical properties. Loose amorphous structure endows the elastomer with excellent dynamic properties, which is conducive to the rearrangement of disulfide bonds in the structure, so that the elastomer can quickly self-repair in a short time. In addition, the elastomer is easy to recycle and biodegrade (the degradation rate is 30.4% at 35 days), which provides an excellent strategy for the design of a new generation of electronic skin carriers.

Optical Nonlinearity in Silicon Nanowires Enabled by Bound States in the Continuum.

Intense electromagnetic fields localized within resonant photonic nanostructures provide versatile opportunities for engineering nonlinear optical effects on a subwavelength scale. For dielectric structures, optical bound states in the continuum (BICs, resonant nonradiative modes that exist within the radiation continuum) are an emerging strategy to localize and intensify fields. Here, we report efficient second and third harmonic generation from Si nanowires (NWs) encoded with BIC and quasi-BIC resonances. In situ dopant modulation during vapor-liquid-solid NW growth was followed by wet-chemical etching to periodically modulate the diameter of the Si NWs and create cylindrically symmetric geometric superlattices (GSLs) with precisely defined axial and radial dimensions. By variation of the GSL structure, BIC and quasi-BIC resonant conditions were created to span visible and near-infrared optical frequencies. To probe the optical nonlinearity of these structures, we collected linear extinction spectra and nonlinear spectra from single-NW GSLs, demonstrating that quasi-BIC spectral positions at the fundamental frequency are directly correlated with enhanced harmonic generation at second and third harmonic frequencies. Interestingly, we find that deliberate geometric detuning from the BIC condition leads to a quasi-BIC resonance with maximal harmonic generation efficiency by providing a balance between the capacity to trap light and the capacity to couple to the external radiation continuum. Moreover, under focused illumination, as few as 30 geometric unit cells are required to achieve more than 90% of the approximate maximum theoretical efficiency of an infinite structure, indicating that nanostructures with projected areas smaller than ∼10 μm2 can support quasi-BICs for efficient harmonic generation. The results represent an important step toward the design of efficient harmonic generation at the nanoscale and further highlight the photonic utility of BICs at optical frequencies in ultracompact one-dimensional nanostructures.

Explainable SHAP-XGBoost models for in-hospital mortality after myocardial infarction

A lack of explainability in published machine learning (ML) models limits clinicians' understanding of how predictions are made, in turn undermining uptake of the models into clinical practice. The purpose of this study was to develop explainable ML models to predict in-hospital mortality in patients hospitalized for myocardial infarction (MI). Adult patients hospitalized for an MI were identified in the National Inpatient Sample between January 1, 2012, and September 30, 2015. The resulting cohort comprised 457,096 patients described by 64 predictor variables relating to demographic/comorbidity characteristics and in-hospital complications. The gradient boosting algorithm eXtreme Gradient Boosting (XGBoost) was used to develop explainable models for in-hospital mortality prediction in the overall cohort and patient subgroups based on MI type and/or sex. The resulting models exhibited an area under the receiver operating characteristic curve (AUC) ranging from 0.876 to 0.942, specificity 82% to 87%, and sensitivity 75% to 87%. All models exhibited high negative predictive value ≥0.974. The SHapley Additive exPlanation (SHAP) framework was applied to explain the models. The top predictor variables of increasing and decreasing mortality were age and undergoing percutaneous coronary intervention, respectively. Other notable findings included a decreased mortality risk associated with certain patient subpopulations with hyperlipidemia and a comparatively greater risk of death among women below age 55 years. The literature lacks explainable ML models predicting in-hospital mortality after an MI. In a national registry, explainable ML models performed best in ruling out in-hospital death post-MI, and their explanation illustrated their potential for guiding hypothesis generation and future study design.

Additive Manufacturing of Porous Biominerals

AbstractNature fabricates hard functional materials from soft organic scaffolds that are mineralized. To enable an energy‐efficient locomotion of these creatures while maintaining their structural stability, nature often renders parts of these minerals porous. Unfortunately, methods to produce synthetic minerals with a similar degree of control over their multi length scale porous structure remain elusive. This level of control, however, would be required to design lightweight yet robust biominerals. Here, a room temperature process is presented that combines a localized mineralization with emulsion‐based 3D printing to form cm sized biominerals possessing pores whose diameters range from the 100 s of nm up to the mm length scale. The samples encompass up to 80 wt% of CaCO3 and display a specific compressive strength that is significantly higher than that of previously reported 3D printed porous biominerals and close to those of trabecular bones. The universality of this approach by forming different types of bioactive minerals, including calcite, aragonite, and brushite is demonstrated. The ability to 3D print these materials under benign conditions renders this energy‐efficient process well‐suited to construct cm‐sized lightweight yet load‐bearing structures that might find applications, for example, in the design of the next generation of flying or motile objects.

Decarbonizing the Spanish transportation sector by 2050: Design and techno-economic assessment of the hydrogen generation and supply chain

The transport sector is difficult to decarbonize due to its high reliance on fossil fuels, accounting for 37% of global end-use sectors emissions in 2021. Therefore, this work proposes an energy model to replace the Spanish vehicle fleet by hydrogen-fueled vehicles by 2050. Thus, six regions are defined according to their proximity to regasification plants, where hydrogen generation hubs are implemented. Likewise, renewables deployment is subject to their land availability. Hydrogen is transported through an overhauled primary natural gas transport network, while two distribution methods are compared for levelized cost of hydrogen minimization: gaseous pipeline vs liquid hydrogen supply in trucks. Hence, a capacity of 443.1 GW of renewables, 214 GW of electrolyzers and 3.45 TWh of hydrogen storage is required nationwide. Additionally, gaseous hydrogen distribution is on average 17% cheaper than liquid hydrogen delivery. Finally, all the regions present lower prices per km traveled than gasoline or diesel.

Research on Automatic Design of Substation Secondary Circuit Based on Improved SPCD Model and Knowledge Graph

In view of the lack of automatic design research of the physical circuit of the substation secondary system, and the digital design and handover level that the substation urgently needs to improve, this paper proposes the following solutions. First of all, an improvement plan is proposed for the existing SPCD model, adding all design related information such as external function grouping and logical interface of the panel cabinet. Secondly, extract the function group elements and secondary cabinet elements of the improved SPCD model as knowledge graph nodes, and abstract the relationship between function group’s interfaces as knowledge graph relationships. Based on the Neo4j graph database, a typical bay knowledge graph for secondary design is conFstructed. Furthermore, the electric power secondary field data set is mined, the Word2vec model is trained by the secondary field data set, and the text similarity algorithm model in the secondary design field is established. Finally, based on the above research, a method for automatic design of substation secondary physical circuits and automatic generation of SPCD model file based on word2vec text similarity is proposed.

Ultrafast Photocurrent Detection in Low‐Dimensional Materials

Photocurrent generation from photon absorption to electron collection involves various physical processes and reflects the whole life of excited charge carriers. The investigation of photocurrent enables the revelation of fundamental material properties and improvement of electronic device performance. Recently, with the development of ultrafast optics, the time‐resolved photocurrent (TRPC) technique is innovated as a photocurrent detection method with a time resolution down to subpicosecond, which allows exploring the ultrafast carrier dynamics and the photodetector intrinsic response highly efficiently. Herein, the advancement of TRPC studies in low‐dimensional materials is focused on. First, the TRPC technique is first introduced, including the setup composition and its measurement mechanism, and compared with the conventional transport‐based method to demonstrate its uniqueness. Then, the TRPC studies in various low‐dimensional materials including 2D graphene, transition metal dichalcogenides and black phosphorus, 1D nanowires, and 0D nanocrystals are discussed, where the originations of their ultrafast photocurrent are analyzed. Finally, an outlook is given on the research prospects of this powerful technique. The TRPC measurement is highlighted as a significant tool to analyze the ultrafast carrier dynamics in the generation of photocurrent and to instruct the structure design of a new generation of ultrafast photodetectors.

Flexible and free-supporting Prussian blue analogs /MXene film for high-performance sodium-ion batteries

Sodium-ion batteries are the most promising alternative to lithium-ion batteries because of their low cost and environmental friendliness. Prussian blue analogs (PBAs) have attracted the attention of researchers because of their open skeleton structure and abundant redox active sites. However, the interstitial water in its crystal structure and poor electron conductivity limit its performance in sodium-ion batteries. A flexible self-supporting electrode is prepared by anhydrous preparation in a ball mill and simple filtration method. The high surface area nanoparticles and the high flexibility MXene layer in the electrode adapt to the rapid Na+ dynamics, alleviating the electrode expansion. The formation of a NaF-rich cathode-electrolyte interface (CEI) layer on the electrode surface inhibits the erosion of active substances by electrolyte, reduces the capacity attenuation of the electrode, and enhances the cyclic stability of the electrode. Electrode without a conductive agent and binder shows excellent electrochemical performance. The discharge capacity of the electrode is 58.3 mAh g−1at 1000 mA g−1 current density, and the capacity retention rate is 59.7% after 1000 cycles. This work has implications for the design of the next generation of low-cost, flexible, and fast charge and discharge energy storage devices.

Business model innovation in the food sector: towards a dimensional framework for analyzing the value proposition of digital platforms for food information services

PurposeDigital platforms for the provision of food information-based services (FISs) represent a consolidated business with increasing revenue streams for entrepreneurs. Such platforms have transformed and clarified the nature of uncertainty and ambiguity inherent in the traditional food sector entrepreneurial processes. Anyway, a clear understanding of the value of digital platforms for FISs is not yet consolidated in the literature. With this paper, the authors try to fill this gap through a critical literature review of scientific research that combines knowledge on food consumer's behavior and user's knowledge behavior.Design/methodology/approachThe authors carried out a critical literature review of scientific research combining knowledge of food consumer's and food information user's behavior. This allowed the identification of the main value components of FISs.FindingsThe authors propose a multidimensional framework for modelling the value proposition of digital platforms for FISs. Three main value dimensions have been identified: relevance, credibility, and accessibility. These dimensions concur with the consumer's perceived value of consumers in terms of benefits increase and cost reduction.Research limitations/implicationsThe research was intended to shed light on aspects characterizing consumers' perception of food information value. The authors put in evidence that the informational perspective of food communication is under-investigated. This study attempts to provide a holistic overview of the dimensions impacting on consumers' perception of the value of information for food consumers, opening new research perspectives.Practical implicationsThe framework represents a tool for positioning food information offerings on the market, with the objective to analyze the value proposition of FISs according to a consumer perspective and to understand gaps of current offering of FISs. Moreover, it may support the design of a new generation of digital platform for food information provision, which would respond to consumers' expectations and information needs, highlighting emerging business opportunities for digital entrepreneurs.Originality/valueFew research works provide a characterization of value proposition of digital platforms providing food information to consumers. In particular, to date, literature lacks of a holistic overview of the dimensions influencing consumer's perception of the information value of food communications.