Opportunities For Design Research Articles

On sparse regression, Lp‐regularization, and automated model discovery

AbstractSparse regression and feature extraction are the cornerstones of knowledge discovery from massive data. Their goal is to discover interpretable and predictive models that provide simple relationships among scientific variables. While the statistical tools for model discovery are well established in the context of linear regression, their generalization to nonlinear regression in material modeling is highly problem‐specific and insufficiently understood. Here we explore the potential of neural networks for automatic model discovery and induce sparsity by a hybrid approach that combines two strategies: regularization and physical constraints. We integrate the concept of Lp regularization for subset selection with constitutive neural networks that leverage our domain knowledge in kinematics and thermodynamics. We train our networks with both, synthetic and real data, and perform several thousand discovery runs to infer common guidelines and trends: L2 regularization or ridge regression is unsuitable for model discovery; L1 regularization or lasso promotes sparsity, but induces strong bias that may aggressively change the results; only L0 regularization allows us to transparently fine‐tune the trade‐off between interpretability and predictability, simplicity and accuracy, and bias and variance. With these insights, we demonstrate that Lp regularized constitutive neural networks can simultaneously discover both, interpretable models and physically meaningful parameters. We anticipate that our findings will generalize to alternative discovery techniques such as sparse and symbolic regression, and to other domains such as biology, chemistry, or medicine. Our ability to automatically discover material models from data could have tremendous applications in generative material design and open new opportunities to manipulate matter, alter properties of existing materials, and discover new materials with user‐defined properties.

Cucurbit[8]uril Mediated Supramolecular and Photocrosslinked Interpenetrating Network Hydrogel Matrices for 3D-Bioprinting.

Printing of biologically functional constructs is significant for applications in tissue engineering and regenerative medicine. Designing bioinks remains remarkably challenging due to the multifaceted requirements in terms of the physical, chemical, and biochemical properties of the three-dimensional matrix, such as cytocompatibility, printability, and shape fidelity. In order to promote matrix and materials stiffness, while not sacrificing stress relaxation mechanisms which support cell spreading, migration, and differentiation, this work reports an interpenetrating network (IPN) bioink design. The approach makes use of a chemically defined network, combining physical and chemical crosslinking units with a tunable composition and network density, as well as spatiotemporal control over post-assembly material stiffening. To this end, star-shaped poly(ethylene glycol)s functionalized with Phe-Gly-Gly tripeptide or photoactive stilbazolium are synthesized, and used to prepare three-dimensional networks with cucurbit[8]uril (CB[8]) through supramolecular host-guest complexation. The hydrogel obtained shows fast relaxation and thus supports the proliferation and differentiation of cells. Upon irradiation, the mechanical properties of the hydrogel can be rapidly adapted via selective photochemical dimerization of stilbazolium within CB[8], leading to IPNs with increased form stability while retaining the dynamic nature of the hydrogels. This modular approach opens new design opportunities for extrudable and cell-friendly dynamic biomaterials for applications in 3D-bioprinting.

Analytical approach to piezoelectric model synthesis with the use of Cauer's method for system design.

Piezoceramic materials have unique property which enables direct and bilateral conversion between mechanical and electrical energy. This ability facilitates significant miniaturisation of technology and opens many opportunities in design of new actuators and energy harvesters. Mathematical modelling of piezoelectric modules is notoriously hard due to complex constitutive equations defining mechanical and electrical energy conversion. The article presents research on a new synthesis method based on the Cauer's method. Mechanical damping is introduced with the use of Rayleigh's approximation. A discrete electromechanical model is formed based on the Mason's piezoelectric model. The proposed approach allows modelling of piezoelectric systems based on a set of characteristic frequencies. The method allows a more general approach to the problem of modelling new systems, as opposed to application-oriented methods seen in literature. A non-standard model analysis method using edge graphs and structural numbers is also verified as a potential alternative for matrix-based method. The authors compare their precision and computation requirements. The structural method of mechanical model analysis gave identical results as the reference matrix method. However, the non-classical algorithm took much longer to calculate and was using more memory. The electromechanical model analysis has shown an error of 5% in comparison to resonance frequencies taken from a reference plate specification. The calculated magnitude of displacement was well above the capability of a 3.5mm thick piezoelectric plate. The synthesis method presented in this paper allows synthesizing piezoelectric cascade models based on limited information in form of characteristic frequencies. Currently this method allows a coarse approximation of the real piezoelectric parameters with limited number of inputs. The additional method of analysis based on structural numbers offers a promising alternative to matrix calculations but requires a more thorough investigation of the computational power required to determine whether it can compete with existing algorithms.

The α subunit of AMP-activated protein kinase is critical for the metabolic success and tachyzoite proliferation of Toxoplasma gondii.

Toxoplasma gondii is a zoonotic parasite infecting humans and nearly all warm-blooded animals. Successful parasitism in diverse hosts at various developmental stages requires the parasites to fine tune their metabolism according to environmental cues and the parasite's needs. By manipulating the β and γ subunits, we have previously shown that AMP-activated protein kinase (AMPK) has critical roles in regulating the metabolic and developmental programmes. However, the biological functions of the α catalytic subunit have not been established. T. gondii encodes a canonical AMPKα, as well as a KIN kinase whose kinase domain has high sequence similarities to those of classic AMPKα proteins. Here, we found that TgKIN is dispensable for tachyzoite growth, whereas TgAMPKα is essential. Depletion of TgAMPKα expression resulted in decreased ATP levels and reduced metabolic flux in glycolysis and the tricarboxylic acid cycle, confirming that TgAMPK is involved in metabolic regulation and energy homeostasis in the parasite. Sequential truncations at the C-terminus found an α-helix that is key for the function of TgAMPKα. The amino acid sequences of this α-helix are not conserved among various AMPKα proteins, likely because it is involved in interactions with TgAMPKβ, which only have limited sequence similarities to AMPKβ in other eukaryotes. The essential role of the less conserved C-terminus of TgAMPKα provides opportunities for parasite specific drug designs targeting TgAMPKα.

Health Data Visualization Literacy Skills of Young Adults with Down Syndrome and the Barriers to Inference-making

As health management becomes more intertwined with data, an individual’s ability to read, interpret, and engage with personal health information in data visualizations is increasingly critical to one’s quality of care. People with Down Syndrome already experience greater health disparities than their typically developing peers. Inaccessible health information and technologies have the potential to magnify inequities further. Inaccessible health data can be an additional barrier to people with Down Syndrome’s ability to adopt and use health systems or devices, make informed decisions about their bodies, and advocate for themselves in health contexts. By examining their underlying data visualization literacy skills, our exploratory study involving ten young adults with Down Syndrome identifies several design opportunities to improve the accessibility of health data visualizations (HDVs) by addressing the cascade of negative effects caused by inference-making barriers in HDVs.

FLEXR GUI: a graphical user interface for multi-conformer modeling of proteins.

Proteins are well known 'shapeshifters' which change conformation to function. In crystallography, multiple conformational states are often present within the crystal and the resulting electron-density map. Yet, explicitly incorporating alternative states into models to disentangle multi-conformer ensembles is challenging. We previously reported the tool FLEXR, which, within a few minutes, automatically separates conformational signal from noise and builds the corresponding, often missing, structural features into a multi-conformer model. To make the method widely accessible for routine multi-conformer building as part of the computational toolkit for macromolecular crystallography, we present a graphical user interface (GUI) for FLEXR, designed as a plugin for Coot 1. The GUI implementation seamlessly connects FLEXR models with the existing suite of validation and modeling tools available in Coot. We envision that FLEXR will aid crystallographers by increasing access to a multi-conformer modeling method that will ultimately lead to a better representation of protein conformational heterogeneity in the Protein Data Bank. In turn, deeper insights into the protein conformational landscape may inform biology or provide new opportunities for ligand design. The code is open source and freely available on GitHub at https://github.com/TheFischerLab/FLEXR-GUI.

Revolutionizing Monolithic Catalysts: The Breakthroughs of Design Control through Computer‐Aided‐Manufacturing

AbstractAdditive manufacturing (AM) presents a promising opportunity for the innovative design and production of structured catalytic materials. Given the critical role of catalysts in industrial catalytic processes, AM has the potential to contribute to the development of improved catalysts by reducing activation energy and enhancing selectivity. Conventional synthesis methods limit the choice of structural materials and composition for producing monoliths. Additionally, the deposition of catalytic compounds is also restricted by commonly applied techniques that may require prior coverage or treatments to improve adherence or do not achieve a homogenous coat. Moreover, production is limited to monoliths with straight and parallel channels. However, this format drives to laminar regime flow thus restricting the radial mass and heat transfer. Conversely, AM allows the production of a wider variety of compositions and more complex structures that have proven to rise their effectiveness by increasing reagents‐catalyst interaction, making catalytic processes more cost‐effective. Therefore, in this review an outline of the recent progress of AM methods in the development of monolithic catalysts is presented focusing on the requirements, advantages, and disadvantages of each technique, hence providing a practical overview of their novel opportunities to overcome current limitations in catalyst synthesis.

Conversational agents in mHealth: use patterns, challenges, and design opportunities for individuals with visual impairments

Conversational agents in mHealth: use patterns, challenges, and design opportunities for individuals with visual impairments

SUSTAINABLE FOOTWEAR DESIGN: BALANCING COMFORT, FUNCTIONALITY AND ENVIRONMENTAL IMPACT

Sustainability is becoming increasingly important in the footwear industry, with consumers demanding products that are not only comfortable and functional but also environmentally friendly. This paper explores the challenges and opportunities of sustainable footwear design, focusing on the balance between comfort, functionality, and environmental impact. The first part of the paper examines the current state of the footwear industry, highlighting the environmental issues associated with traditional footwear manufacturing processes and materials. It then discusses the growing interest in sustainable footwear design and the need for innovative solutions to reduce the industry's environmental footprint. Next, the paper explores various aspects of sustainable footwear design, including material selection, manufacturing processes, and product life cycle. It discusses the importance of using eco-friendly materials such as recycled plastics, organic cotton, and sustainable leather alternatives, as well as the benefits of incorporating sustainable practices into the design process. The paper also considers the challenges of balancing sustainability with comfort and functionality. It discusses the importance of designing footwear that meets the needs of consumers while also minimizing environmental impact, highlighting the role of innovative design and technology in achieving this balance. Keyword: sustainable footwear design, comfort, functionality, environmental impact, eco-friendly materials, innovation

Abstract 6344: Engineering of ICAM-1 refractory antibodies for the development of therapeutic antibodies and antibody drug conjugates (ADCs) in ICAM-1 over-expressing cancers

Abstract Human cancers employ a number of mechanisms to evade host immune responses against novel antigens generated from aberrant over-expression, mutations and/or epigenetic alterations. Humoral immunity utilizes antibodies and immune-effector cells as well as molecular immune complexes involving the complement system to mediate the killing of dysregulated cancer cells. We refer to these anti-cancer mechanisms as Humoral Immuno-Oncology (HIO). HIO suppression is mediated by tumor-produced proteins called HIO factors. One such factor is CA125, which was previously shown to bind IgG-type antibodies and inhibit their immune-effector activities, including antibody dependent (ADCC) and complement dependent (CDC) cellular cytotoxicity. Using a combination of experimental screening and literature searches, a second protein produced by tumors and associated with a variety of cancer indications was discovered and found to be soluble ICAM-1 (sICAM-1). Through various functional studies we reported that sICAM-1 and membrane ICAM-1 (mICAM-1) are capable of binding IgG1-type antibodies that in turn inhibits their immune-effector activity. Through a combination of truncation and substitution mutagenesis, we identified a four amino acid motif within the CH3 domain of IgG1 essential for ICAM-1 binding, resulting in the inhibition of ADCC activity. While traditional glycine and alanine substitutions in this region abrogated ICAM-1 binding, these modifications caused tertiary structural changes in the Fc domain that resulted in loss of ADCC activity. Through a combinatorial amino acid substitution approach, we identified a four amino acid combination within the 407YSKL410 to 407FARV410 motif that resulted in antibodies refractory to sICAM-1 binding with robust ADCC activity. Additionally, isogenic wildtype and ICAM-1 knockdown target cell lines showed mICAM-1 inhibited target cell killing of a saporin antibody drug conjugate (ADC) and this was the direct result of mICAM-1 reducing antibody internalization, a requisite for maximal ADC target cell killing. The inhibitory activity of mICAM-1 could be overcome employing antibodies containing the 407FARV410 substitution. These findings highlight yet another mechanism by which tumors can suppress the host’s immune system for survival and offer new concepts for developing antibody-based therapies that can aid in the treatment of various cancer indications, especially those over-expressing ICAM-1. Moreover, the findings here offer diagnostic and therapeutic clinical design opportunities to improve upon existing approved immune-mediated therapies for which this factor is present. Citation Format: Brad Kline, Luigi Grasso, Nicolas Nicolaides. Engineering of ICAM-1 refractory antibodies for the development of therapeutic antibodies and antibody drug conjugates (ADCs) in ICAM-1 over-expressing cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6344.

Deep Learning Models Compared to Experimental Variability for the Prediction of CYP3A4 Time-Dependent Inhibition.

Most drugs are mainly metabolized by cytochrome P450 (CYP450), which can lead to drug-drug interactions (DDI). Specifically, time-dependent inhibition (TDI) of CYP3A4 isoenzyme has been associated with clinically relevant DDI. To overcome potential DDI issues, high-throughput in vitro assays were established to assess the TDI of CYP3A4 during the discovery and lead optimization phases. However, in silico machine learning models would enable an earlier and larger-scale assessment of TDI potential liabilities. For CYP inhibition, most modeling efforts have focused on highly imbalanced and small data sets. Moreover, assay variability is rarely considered, which is key to understand the model's quality and suitability for decision-making. In this work, machine learning models were built for the prediction of TDI of CYP3A4, evaluated prospectively, and compared to the variability of the experimental assay. Different modeling strategies were investigated to assess their influence on the model's performance. Through multitask learning, additional data sets were leveraged for model building, coming from public databases, in-house CYP-related assays, or other pharmaceutical companies (federated learning). Apart from the numerical prediction of inactivation rates of CYP3A4 TDI, three-class predictions were carried out, giving a negative (inactivation rate kobs < 0.01 min-1), weak positive (0.01 ≤ kobs ≤ 0.025 min-1), or positive (kobs > 0.025 min-1) output. The final multitask graph neural network model achieved misclassification rates of 8 and 7% for positive and negative TDI, respectively. Importantly, the presented deep learning-based predictions had a similar precision to the reproducibility of in vitro experiments and thus offered great opportunities for drug design, early derisk of DDI potential, and selection of experiments. To facilitate CYP inhibition modeling efforts in the public domain, the developed model was used to annotate ∼16 000 publicly available structures, and a surrogate data set is shared as Supporting Information.

Blind Passengers’ Travel Experiences: An Interpretative Phenomenology Analysis of the Challenges and Opportunities of Traveling by Train in Indonesia

This research investigates the experiences of blind passengers using long-distance intercity trains. Although prior research has been done on transportation use by blind consumers, their customer experience in Indonesia has yet to be thoroughly examined. This study employs Smith’s Interpretative Phenomenology Analysis (IPA) method, conducting video conference interviews with six blind passengers who frequently travel on long-distance intercity trains. These in-depth interviews were analyzed using double hermeneutics to develop the Blind Passenger Journey Mapping (BPJM) and Blind Passenger Journey &amp; Experience (BPJE) models. The findings reveal that blind passengers often require assistance at nearly all touchpoints and need more accessible services throughout their journey. Two main themes emerged: Blind Passenger Experience (BPE) and Sighted Mobility Oriented Assistant (SMOA). The paper identifies significant gaps and opportunities in the current service design for blind passengers, suggesting that addressing these through entrepreneurial initiatives cannot only improve inclusivity and accessibility but also revolutionize the travel industry by opening a potentially underserved market, promoting a more equitable and sustainable travel ecosystem.

C3-Symmetric ligands in drug design: An overview of the challenges and opportunities ahead

C3-symmetry is a type of star-shaped molecule consisting of a central core and three symmetrically attached chains. These molecules are used in drug discovery due to their unique three-fold rotational symmetry, which allows for specific binding interactions and improved molecular recognition. In this text, we provide an overview of synthetic approaches with C3-symmetry as a pharmaceutical tool: progress, challenges, and opportunities. C3-symmetric ligands offer both challenges and opportunities in drug design. Their unique symmetry can enhance binding interactions, but careful consideration of rigidity, synthetic complexity, and target compatibility is crucial. Further research and advancements in synthetic methods and modeling tools will likely drive their exploration in drug discovery, leading to the discovery of potent C3-symmetric ligands.

“Playing” with Color: How Similar Is the “Geometry” of Color Harmony in the CIELAB Color Space across Countries?

In physical environments and cultural landscapes, we most often deal not with separate colors, but with color combinations. When choosing a color, we usually try to “fit” it into a preexisting color context, making the new color combination harmonious. Yet are the “laws” of color harmony fundamental to our shared cognitive architecture, or are they cultural products that vary from country to country? To answer these questions we conducted an experiment with 599 participants aged 18 to 76 from eight different countries, including Algeria (MA = 26.2 years; SD = 8.8; 49 men, 26 women), Belarus (MA = 19.8 years; SD = 9.1; 19 men, 63 women), Italy (MA = 29.0 years; SD = 12.8; 23 men, 67 women), Mexico (MA = 20.0 years; SD = 7.0; 34 men, 23 women), Nigeria (MA = 34.7 years; SD = 10.5; 29 men, 32 women), Russia (MA = 24.6 years; SD = 6.3; 17 men, 72 women), Saudi Arabia (MA = 24.5 years; SD = 8.6; 28 men, 38 women), and Chile (MA = 34.3 years; SD = 15.1; 35 men, 43 women). To create experimental stimuli, we used 10 color combinations composed by the Russian avant-garde artist Mikhail Matyushin and his disciples for the Reference Book of Color (1932) based on shades that were typical in architectural design—yellow ochre, light umber, light ochre, and burnt umber. We removed the “intermediary” linking color from each of the selected color triads and asked participants to adjust the color of this band according to their liking. Mapping 2995 color choices into CIELAB and CIELCh color space to identify their chromatic characteristics (hue, lightness, and chroma), we demonstrate graphically that color triads in different cultures have a different “geometry” in CIELAB color space and on the color circle. We conclude that the revealed patterns of these relationships reflect cross-cultural “shifts” in human perception of color harmony. The analysis presented in this paper will facilitate opportunities for architects, designers, and other color professionals to create culturally specific harmonic color combinations in urban environments.

Site-specific reactivity of stepped Pt surfaces driven by stress release.

Heterogeneous catalysts are widely used to promote chemical reactions. Although it is known that chemical reactions usually happen on catalyst surfaces, only specific surface sites have high catalytic activity. Thus, identifying active sites and maximizing their presence lies at the heart of catalysis research1-4, in which the classic model is to categorize active sites in terms of distinct surface motifs, such as terraces and steps1,5-10. However, such a simple categorization often leads to orders of magnitude errors in catalyst activity predictions and qualitative uncertainties of active sites7,8,11,12, thus limiting opportunities for catalyst design. Here, using stepped Pt(111) surfaces and the electrochemical oxygen reduction reaction (ORR) as examples, we demonstrate that the root cause of larger errors and uncertainties is a simplified categorization thatoverlooks atomic site-specific reactivity driven by surface stress release. Specifically, surface stress release at steps introduces inhomogeneous strain fields, with up to 5.5% compression, leading to distinct electronic structures and reactivity for terrace atoms with identical local coordination, and resulting in atomic site-specific enhancement of ORR activity. For the terrace atoms flanking both sides of the step edge, the enhancement is up to 50 times higher than that of the atoms in the middle of the terrace, which permits control of ORR reactivity by either varying terrace widths or controlling external stress. Thus, the discovery of the above synergy provides a new perspective for both fundamental understanding of catalytically active atomic sites and design principles of heterogeneous catalysts.

Data-Driven Design in the Design Process: A Systematic Literature Review on Challenges and Opportunities

This systematic review critically evaluates the nexus between data-driven design (DDD) and design processes (DP), guided by PRISMA standards. It synthesises findings from 84 selected articles from databases such as WoS and Scopus, published between 2010 and 2023. It identifies a notable gap in the literature in directly linking DDD to DP, while acknowledging a broader, more complex relationship. This research reveals the complexity of integrating DDD into traditional design paradigms, highlighting both challenges and opportunities for innovation. The findings serve as an important resource for academics and practitioners interested in using data to enhance DP. As well as highlighting existing challenges and opportunities, the review calls for further research to fill identified gaps, highlighting the significant potential for advancing design methodologies through a more coherent integration of data-driven insights. This work points to the need for a deeper understanding and use of DDD to improve traditional design practices.

High-Capacity, Cooperative CO2 Capture in a Diamine-Appended Metal-Organic Framework through a Combined Chemisorptive and Physisorptive Mechanism.

Diamine-appended Mg2(dobpdc) (dobpdc4- = 4,4'-dioxidobiphenyl-3,3'-dicarboxylate) metal-organic frameworks are promising candidates for carbon capture that exhibit exceptional selectivities and high capacities for CO2. To date, CO2 uptake in these materials has been shown to occur predominantly via a chemisorption mechanism involving CO2 insertion at the amine-appended metal sites, a mechanism that limits the capacity of the material to ∼1 equiv of CO2 per diamine. Herein, we report a new framework, pip2-Mg2(dobpdc) (pip2 = 1-(2-aminoethyl)piperidine), that exhibits two-step CO2 uptake and achieves an unusually high CO2 capacity approaching 1.5 CO2 per diamine at saturation. Analysis of variable-pressure CO2 uptake in the material using solid-state nuclear magnetic resonance (NMR) spectroscopy and in situ diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) reveals that pip2-Mg2(dobpdc) captures CO2 via an unprecedented mechanism involving the initial insertion of CO2 to form ammonium carbamate chains at half of the sites in the material, followed by tandem cooperative chemisorption and physisorption. Powder X-ray diffraction analysis, supported by van der Waals-corrected density functional theory, reveals that physisorbed CO2 occupies a pocket formed by adjacent ammonium carbamate chains and the linker. Based on breakthrough and extended cycling experiments, pip2-Mg2(dobpdc) exhibits exceptional performance for CO2 capture under conditions relevant to the separation of CO2 from landfill gas. More broadly, these results highlight new opportunities for the fundamental design of diamine-Mg2(dobpdc) materials with even higher capacities than those predicted based on CO2 chemisorption alone.

Perspectives and progress on wurtzite ferroelectrics: Synthesis, characterization, theory, and device applications

Wurtzite ferroelectrics are an emerging material class that expands the functionality and application space of wide bandgap semiconductors. Promising physical properties of binary wurtzite semiconductors include a large, reorientable spontaneous polarization, direct band gaps that span from the infrared to ultraviolet, large thermal conductivities and acoustic wave velocities, high mobility electron and hole channels, and low optical losses. The ability to reverse the polarization in ternary wurtzite semiconductors at room temperature enables memory and analog type functionality and quasi-phase matching in optical devices and boosts the ecosystem of wurtzite semiconductors, provided the appropriate combination of properties can be achieved for any given application. In this article, advances in the design, synthesis, and characterization of wurtzite ferroelectric materials and devices are discussed. Highlights include: the direct and quantitative observation of polarization reversal of ∼135 μC/cm2 charge in Al1−xBxN via electron microscopy, Al1−xBxN ferroelectric domain patterns poled down to 400 nm in width via scanning probe microscopy, and full polarization retention after over 1000 h of 200 °C baking and a 2× enhancement relative to ZnO in the nonlinear optical response of Zn1−xMgxO. The main tradeoffs, challenges, and opportunities in thin film deposition, heterostructure design and characterization, and device fabrication are overviewed.

Safety Concepts for Future Electromechanical Brake Actuators

&lt;div&gt;A growing interest in electromechanical brakes (EMB) is discernible in the automotive industry finding its climax in an announcement of EMB series production in late 2022 [&lt;span&gt;1&lt;/span&gt;]. The introduction of EMB allows for new design opportunities using distributed software on smart actuators. However, additional efforts are needed to ensure continuously high levels of safety even when established design principles in the brake system are changed.&lt;/div&gt; &lt;div&gt;This article discusses different safety concepts that could potentially be put in place in EMB actuators. Therefore, safety goals that need to be satisfied by an actuator are derived. Furthermore, three different degrees of complexity are differentiated, evolving to different required electronic control units (ECU) and architectures. Additionally, also the safety of the actuation unit (AU) is considered to realize a holistic safety concept for the actuator. Finally, a conclusion is drawn comparing the different investigated concepts.&lt;/div&gt;

In-situ crosslinking of a novel conjugated dialkynyl-based polyimide for high performance carbon molecular sieve membrane

High-performance and reliable membranes for efficient gas separation and their design strategies are key points for membrane-based carbon capture. Here, we reported a high-performance carbon membrane design protocol from a microporous, highly rigid polyimide precursor (6FBD1-CR), which was produced by in-situ crosslinking a polyimide (6FBD1) containing conjugated dialkynyl group at 300 °C. The possible crosslinking mechanisms are the [2+2+2] trimerization of three ethynyl groups to form a benzene ring and the Diels-Alder reaction between the ethynyl bond and phenylacetylene to generate the fused naphthalene ring. After crosslinking, the membrane shows a ∼ 8-fold improved microporosity (37.1–298 m2 g−1). When pyrolyzed at 550 °C, the resulting 6FBD1-CMS demonstrates very high pure- and mixed-gas separation performance. E.g., the CO2 permeability reaches 7661 Barrer and CO2/CH4 selectivity of 44, additionally, even at the CO2/CH4 mixed-gas pressure of 30 bar, the PCO2 can still reach 4920 Barrer combined with a αCO2/CH4 of 28, which is well above the latest CO2/CH4 mixed-gas Upper Bound line. We consider this is due to the acetylene crosslinking induced a very stable microporous structure composed of rigid fused rings that alleviate the micropore collapse during the carbonization process, and thus, resulting in a relatively higher BET surface area, larger d-spacing, relatively higher concentration of continuous disordered phase (higher sp3/sp2) and small activation energy (Ep) for gas transport than the CMSM derived from polyimide without pre-crosslinking. This finding provides new opportunity for the structural design of high-performance CMSMs.