Molecular Rebar Design, a nanomaterials company based in Austin, Texas, has patented a new additive manufacturing (AM) composition that utilizes oxidized discrete carbon nanotubes (CNTs) with bonded dispersing agents to enhance 3D printing resins. The patent, published under US20210237509A1, outlines methods to improve resin properties for applications such as vat photopolymerization, sintering, and thermoplastic fusion. The inventors, Clive P. Bosnyak, Kurt W. Swogger, Steven Lowder, and Olga Ivanova, propose formulations that improve electrical conductivity, thermal stability, and mechanical strength, while overcoming dispersion challenges common with CNTs in composite materials. Image shows a schematic diagram of functionalized carbon nanotubes. Image via Molecular Rebar Design. Functionalized CNTs for additive manufacturing At the core of the invention is the chemical functionalization of CNTs with dispersing agents bonded to their sidewalls, enabling higher aspect ratios and more homogeneous dispersions. These dispersions integrate into UV-curable acrylates, thermoplastics, and elastomers, yielding improved green strength, sinterability, and faster cure rates. The patent emphasizes the benefit of using bimodal or trimodal distributions of CNT diameters (single-, double-, or multi-wall) to tune material performance. Additional fillers such as carbon black, silica, and metallic powders can also be incorporated for applications ranging from electronic encapsulation to impact-resistant parts. Experimental validation To validate the invention, the applicants oxidized carbon nanotubes using nitric acid and covalently bonded them with polyether dispersing agents such as Jeffamine M2005. These modified CNTs were incorporated into photopolymer resin formulations. In tensile testing, specimens produced with the dispersions demonstrated enhanced mechanical performance, with yield strengths exceeding 50 MPa and Young’s modulus values above 2.8 GPa. Impact strength improved by up to 90% in certain formulations compared to control samples without CNTs. These performance gains suggest suitability for applications demanding high strength-to-weight ratios, such as aerospace, electronics, and structural components. Nanotube innovations in AM Carbon nanotubes (CNTs) have long been explored for additive manufacturing (AM) due to their exceptional mechanical and electrical properties. However, challenges such as poor dispersion and inconsistent aspect ratios have hindered their widespread adoption in AM processes. Recent advancements aim to overcome these barriers by integrating oxidation and dispersion techniques into scalable production methods. For instance, researchers at Rice University have developed a novel acid-based solvent that prevents the common “spaghetti effect” of CNTs tangling together. This innovation simplifies the processing of CNTs, potentially enabling their scale-up for industrial 3D printing applications. Similarly, a research team led by the University of Glasgow has created a 3D printable CNT-based plastic material capable of sensing its own structural health. This material, inspired by natural porous structures, offers enhanced toughness and strength, with potential applications in medicine, prosthetics, automotive, and aerospace design. Subscribe to the 3D Printing Industry newsletter to keep up with the latest 3D printing news. You can also follow us onLinkedIn and subscribe to the 3D Printing Industry YouTube channel to access more exclusive content. At 3DPI, our mission is to deliver high-quality journalism, technical insight, and industry intelligence to professionals across the AM ecosystem.Help us shape the future of 3D printing industry news with our2025 reader survey. Feature image shows schematic diagram of functionalized carbon nanotubes. Image via Molecular Rebar Design.

You know the scene: someone reaches for a suspiciously dusty book in a stately library, the bookshelf creaks, and boom—a hidden passage swings open. Maybe it’s a secret tunnel, maybe it’s a villain’s lair, maybe it’s just where they keep the good snacks. Either way, it’s drama. It’s mystery. It’s very extra. And guess what? That magic is no longer reserved for Scooby-Doo or Sherlock Holmes.Thanks to interior designers’ customization offerings, as well as hidden-door-specific brands such as Murphy Door and Creative Home Engineering, those secret swings and sneaky reveals are showing up in real-life homes—and homeowners are absolutely obsessed. Like, “I’ll take three, please and thank you” obsessed.Designer Lynn Kloythanomsup of Landed Interiors and Home installed a Murphy Door to conceal a hallway bathroom in a San Francisco home.Haris KenjarTa-da! With a push of the shelf a pretty powder room is revealed.Haris KenjarThese cleverly disguised doors are popping up everywhere, from kitchens and closets to home offices and bedrooms, proving that the only thing better than good design is a good surprise. “There’s nothing better than a hidden surprise,” says interior designer Maria Vassiliou of Maria Zoe Designs. Shock value aside, Murphy doors can also have practical benefits as well. “Hidden doors often come with features like shelving, allowing for better organization and use of space,” says Vassiliou. They can also be designed to blend seamlessly with cabinetry.”Translation: not only do hidden doors look cool, they can actually do something. (Imagine that!) Here’s everything you need to know about hidden doors.A built in bookcase with a secret tucked within a modern mountain barn by A Classical Studio. Heidi HarrisA little push is all it takes to reveal a secret passageway behind the shelf. Heidi HarrisWhat, Exactly, Is a Murphy Door?There’s a difference between a well-disguised door and a hidden one. You can flush-mount a jib door into the wall, wrap it in wallpaper, and remove the hardware to make it nearly invisible. These minimalist doors are scattered throughout design-forward interiors. But a Murphy Door is something entirely different. It’s not invisible—it’s intentionally integrated. It could be a bookshelf, a staircase, or even a wine rack. It’s meant to be lived with, decorated, and admired. But here’s the catch: you’d never guess it’s also a door. HEIDI GELDHAUSER HARRISIn this dining room, designer Clary Bosbyshell used the same mural wallpaper to create a seamless transition on a jib door.Of course, the thrill factor is still alive and well. If you’ve ever walked through the fridge to get into Good Times at Davey Wayne’s in L.A., or slipped through the vending machine into Basement in NYC’s Chinatown, or snuck behind the bookshelf at Eatapas in Fort Lauderdale, then you know: a hidden entrance makes everything instantly cooler. And yes, your house deserves to be that cool. Take this Central Texas home designed by Sarah Stacey of Sarah Stacey Interior Design. She cleverly utilized an iconic British telephone booth as the secret entrance between the home’s garage-turned-speakeasy and dance hall. Then there’s the Grandpa who created his own version of C.S. Lewis’ Narnia by enclosing a secret room within his bedroom wardrobe. STEPHEN KARLISCHDesigner Sarah Stacey brought on a contractor to remove the back of the booth to recast it as a hidden door.What Styles of Murphy Doors Are Available?Murphy Door’s lineup is basically a choose-your-own-adventure of hidden access points. Want a full-length Mirror Door that’s secretly a portal to your glam room? Done. Need a Pantry Door to hide your cereal stash and your coffee machine? There’s a Murphy Door for that too. Or you can do your own custom built-in like Vassiliou did in her clients’ home below. “The hidden door allows the homeowners to hide away items that might otherwise seem cluttered, such as small appliances like coffee machines and toaster ovens and extra pantry items like fruit, vegetables and snacks,” she says.This seems to be just another display cabinet in this butler's pantry designed by Marie Zoe Designs.Linda Pordon PhotographyThis opens up to reveal additional storage and even tucked away appliances. Linda Pordon Photography“A Murphy Door isn’t just a door—it’s a gateway to possibility,” says Jeremy Barker, Founder and CEO at Murphy Door. “Homeowners are drawn to the idea that behind what looks like an ordinary bookcase or cabinet is something deeply personal: a wine cellar, a hidden study, a secure space, or even a walk-in pantry.”Murphy Door’s newest launches—debuted at the 2025 NAHB International Builders Show—include the Archway Bookcase Door for that Beauty and the Beast library moment, the Speakeasy Door if you want to relive the thrill of the prohibition, and let’s not forget the Tactical Murphy Door, which sounds like it moonlights as a Marvel superhero but is actually just a very secure place to keep your valuables. They also offer a number of these doors in a French Door style making way for double the drama and mystery.Courtesy of Murphy DoorOne of Murphy Door’s newest additions, the Archway Bookcase Door, in green. Where Can I Install a Murphy Door?These doors are as functional as they are fun. But the best part? Hidden doors can be installed in a variety of places. According to Steve Humble, president of Creative Home Engineering, the world’s premier designer and manufacturer of motorized and high-security secret passageways, “location is the first thing you must consider when installing a hidden door in your home. It determines which door ideas are feasible.” Humble recommends primary bedrooms, bathrooms, libraries, wardrobes, and basements as the top five locations to install a secret passthrough. With staircases as a very close runner up. No matter where you install them, they save space. They hide mess. They make you feel like you’re living in your own secret lair—but in a more chic versus villainous way.SARAH HEBENSTREITDesigner Regan Baker installed a bookshelf Murphy Door under a staircase to add function to an underused space. In a world where everyone’s trying to declutter, hide the chaos, and add personality to their space, hidden doors offer the perfect triple threat: style, storage, and just the right amount of sass. Because honestly, who doesn’t want to feel like they're in a Bond movie while grabbing a protein bar? “The appeal is emotional and functional,” says Barker. “They’re not just entrances, they’re the first step into an experience tailored to your vision.”Bottom line: Hidden doors aren’t just a trend—they’re a (secret) lifestyle. So go ahead, pull that book, press that panel, and swing open the possibilities. Your home’s next best-kept secret is just a hinge away. Shop Murphy DoorsSpice Rack Door$2,282 at Murphy DoorsCredit: Murphy DoorsArchway Bookcase Door$2,762 at Murphy DoorCredit: CREDIT: MURPHY DOORMirror Door$2,092 at Murphy DoorCredit: CREDIT: MURPHY DOORHamper Door$2,569 at Murphy DoorCredit: Murphy Doors

Reasoning tasks are a fundamental aspect of artificial intelligence, encompassing areas like commonsense understanding, mathematical problem-solving, and symbolic reasoning. These tasks often involve multiple steps of logical inference, which large language models (LLMs) attempt to mimic through structured approaches such as chain-of-thought (CoT) prompting. However, as LLMs grow in size and complexity, they tend to produce longer outputs across all tasks, regardless of difficulty, leading to significant inefficiencies. The field has been striving to balance the depth of reasoning with computational cost while also ensuring that models can adapt their reasoning strategies to meet the unique needs of each problem. A key issue with current reasoning models is the inability to tailor the reasoning process to different task complexities. Most models, including well-known ones like OpenAI’s o1 and DeepSeek-R1, apply a uniform strategy—typically relying on Long CoT across all tasks. This causes the “overthinking” problem, where models generate unnecessarily verbose explanations for simpler tasks. Not only does this waste resources, but it also degrades accuracy, as excessive reasoning can introduce irrelevant information. Approaches such as prompt-guided generation or token budget estimation have attempted to mitigate this issue. Still, these methods are limited by their dependence on predefined assumptions, which are not always reliable for diverse tasks. Attempts to address these issues include methods like GRPO (Group Relative Policy Optimization), length-penalty mechanisms, and rule-based prompt controls. While GRPO enables models to learn different reasoning strategies by rewarding correct answers, it leads to a “format collapse,” where models increasingly rely on Long CoT, crowding out more efficient formats, such as Short CoT or Direct Answer. Length-penalty techniques, such as those applied in methods like THINKPRUNE, control output length during training or inference, but often at the cost of reduced accuracy, especially in complex problem-solving tasks. These solutions struggle to achieve a consistent trade-off between reasoning effectiveness and efficiency, highlighting the need for an adaptive approach. A team of researchers from Fudan University and Ohio State University introduced the Adaptive Reasoning Model (ARM), which dynamically adjusts reasoning formats based on task difficulty. ARM supports four distinct reasoning styles: Direct Answer for simple tasks, Short CoT for concise reasoning, Code for structured problem-solving, and Long CoT for deep multi-step reasoning. It operates in an Adaptive Mode by default, automatically selecting the appropriate format, and also provides Instruction-Guided and Consensus-Guided Modes for explicit control or aggregation across formats. The key innovation lies in its training process, which utilizes Ada-GRPO, an extension of GRPO that introduces a format diversity reward mechanism. This prevents the dominance of Long CoT and ensures that ARM continues to explore and use simpler reasoning formats when appropriate. The ARM methodology is built on a two-stage framework. First, the model undergoes Supervised Fine-Tuning (SFT) with 10.8K questions, each annotated across four reasoning formats, sourced from datasets like AQuA-Rat and generated with tools such as GPT-4o and DeepSeek-R1. This stage teaches the model the structure of each reasoning format but does not instill adaptiveness. The second stage applies Ada-GRPO, where the model receives scaled rewards for using less frequent formats, such as Direct Answer or Short CoT. A decaying factor ensures that this reward gradually shifts back to accuracy as training progresses, preventing long-term bias toward inefficient exploration. This structure enables ARM to avoid format collapse and dynamically match reasoning strategies to task difficulty, achieving a balance of efficiency and performance. ARM demonstrated impressive results across various benchmarks, including commonsense, mathematical, and symbolic reasoning tasks. It reduced token usage by an average of 30%, with reductions as high as 70% for simpler tasks, compared to models relying solely on Long CoT. ARM achieved a 2x training speedup over GRPO-based models, accelerating model development without sacrificing accuracy. For example, ARM-7B achieved 75.9% accuracy on the challenging AIME’25 task while using 32.5% fewer tokens. ARM-14B achieved 85.6% accuracy on OpenBookQA and 86.4% accuracy on the MATH dataset, with a token usage reduction of over 30% compared to Qwen2.5SFT+GRPO models. These numbers demonstrate ARM’s ability to maintain competitive performance while delivering significant efficiency gains. Overall, the Adaptive Reasoning Model addresses the persistent inefficiency of reasoning models by enabling the adaptive selection of reasoning formats based on task difficulty. The introduction of Ada-GRPO and the multi-format training framework ensures that models no longer waste resources on overthinking. Instead, ARM provides a flexible and practical solution for balancing accuracy and computational cost in reasoning tasks, making it a promising approach for scalable and efficient large language models. Check out the Paper, Models on Hugging Face and Project Page. All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 95k+ ML SubReddit and Subscribe to our Newsletter. NikhilNikhil is an intern consultant at Marktechpost. He is pursuing an integrated dual degree in Materials at the Indian Institute of Technology, Kharagpur. Nikhil is an AI/ML enthusiast who is always researching applications in fields like biomaterials and biomedical science. With a strong background in Material Science, he is exploring new advancements and creating opportunities to contribute.Nikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces WEB-SHEPHERD: A Process Reward Model for Web Agents with 40K Dataset and 10× Cost EfficiencyNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces MMaDA: A Unified Multimodal Diffusion Model for Textual Reasoning, Visual Understanding, and Image GenerationNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces Differentiable MCMC Layers: A New AI Framework for Learning with Inexact Combinatorial Solvers in Neural NetworksNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces GRIT: A Method for Teaching MLLMs to Reason with Images by Interleaving Text and Visual Grounding