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Subtype-Specific Aptamer Technologies for Breast Cancer: From Molecular Recognition to Precision Applications
Hui-Bin Wu
,Zhi Zeng
,Wen-Yi Huang
,Xin-Yan Lin
,Rui Tian
,Jian-Min Chen
,Qiu-Long Zhang
Posted: 04 September 2026
Investigation of Morphology and EMI Shielding Efficiency of Cotton Fabrics Functionalized with rGO and rGO–REVO₄ (RE =Sm, Nd, Dy, Gd) Nanoparticles for Advanced Wearable Applications
Dragana Marinković
,Mila Milenković
,Josip Išek
,Giancarlo C. Righini
,Maurizio Ferrari
,Maja Grbić
,Svetlana Jovanović
Posted: 03 September 2026
Bis(η6-Cycloborane)Chromium [Cr(η6-(BH)6H6)2] As A Boron Analog of Bis(η6-Benzene)Chromium
Qin-Wei Zhang
,Rui Wei
,Hao-Xian Pu
,Si-Dian Li
Posted: 03 September 2026
Synthesis of Iodinated Cobalt Bis(dicarbollide) Conjugates with Acridine and Their Biological Studies
Nadezhda V. Dudarova
,Egor V. Sidorskii
,Anna I. Kasatova
,Timofey A. Bykov
,Anastasia A. Antonets
,Alexey A. Nazarov
,Vsevolod A. Skribitsky
,Kristina E. Shpakova
,Anton A. Kasianov
,Yulia A. Finogenova
+7 authors
Posted: 03 September 2026
Divergent Chiral-Pool Synthesis of the C1–C8 and C9–C13 Fragments of Pladienolide B from L-Malic Acid
Sai Krishna Chilaka
,Ashok Nerella
,Hanumaiah Marumamula
,Ramachandra reddy Putta
Posted: 03 September 2026
Intestinal Microbial Metabolic Activation Converts Soy Isoflavones into Potent Direct Antioxidants: First Total Synthesis and Mechanistic Characterization of 5-Hydroxydehydroequol
Sakurako Okada
,Yoshimi Shoji
,Masao Morita
,Mika Hayashi
,Wakana Shimizu
,Kei Ohkubo
,Hiromu Ito
,Ikuo Nakanishi
,Kiyoshi Fukuhara
Posted: 03 September 2026
Effect of Zinc Substitution on the Structure and Catalytic Activity of Copper Chromite Spinels for Cyclohexane Oxidation
Kochurani George
,S. Sugunan
Posted: 03 September 2026
Kraft Cooking Kinetics of Poplar Wood and the Impact of a Mechano-Enzymatic Pre-Treatment
Arthur Valencony
,Sandra Tapin-Lingua
,Seyedeh Hadis Hashemi
,Gerard Mortha
Posted: 03 September 2026
Modulating Physicochemical Features of Mesoporous Strontium Silicate Nanostructures: A Design of Experiments Approach
Dhiraj Kumar
,Taruna Singh
,Tristen Nies
,Grace Liu
,Maycoll Johnson
,David Mei
,Wandi Gu
,Conrado Aparicio
,Isha Mutreja
,Robert S. Jones
Posted: 03 September 2026
Polyhydroxyurethanes as Emerging Non-Isocyanate Tougheners for PLA/PHA Blends: Chemistry, Morphology, Reprocessability, and End-of-Life Design
Rana Al Nakib
,Yusuf Z. Menceloğlu
Posted: 02 September 2026
Surface-Condition-Dependent Corrosion of Wire-Laser DED MoNiCr in FLiBe at 750 °C: Effects of As-Built Topography, Wire EDM, and Hot Isostatic Pressing
Josef Strejcius
,Martin Mareček
,Michal Cihlář
,David Bricín
,Zdeněk Fulín
Posted: 01 September 2026
Towards Enabling Diatom Frustule Integration in Advanced Micro/Nanosystems: Automated Micromanipulation as a Promising Approach
Mohamed Ghobara
,Mayar Abd El Rahim Badawy
,Emiliano Bellotti
Posted: 01 September 2026
Syntheses and Immunogenicities of Campylobacter jejuni Glycoconjugate Vaccines with Polysaccharide Capsules Activated by TEMPO/Bleach-Mediated Oxidation
Mario A. Monteiro
,Alexander C. Maue
,Yu-Han Chen
,Brittany Pequegnat
,Eman Omari
,Cheryl P. Ewing
,Jennifer Crha
,Silvia Borrelli
,Zuchao Ma
,Frederic Poly
+1 authors
Posted: 01 September 2026
Visible-Light Harvesting with CdTe Thin Films: Band Gap-Tunable Engineered Photocatalysts for Sustainable Water Treatment Applications
Ahmet Tuna
,Serap Yiğit Gezgin
,M. A. Basyooni-M. Kabatas
,Hamdi Şükür Kılıç
Posted: 01 September 2026
Materials and Surface Chemistry of Organ-on-Chip Microdevices: Polymer Networks, Interfacial Functionalization, and Small-Molecule Partitioning in Pharmaceutical Microphysiological Systems
Augustine Odibo
Posted: 31 August 2026
Stereochemical 2D NMR Assignment of Thietane, Synthesis and Antiplatelet Activity of Novel Thietane-Containing 1-Isobutylpurine-2,6-dione Derivatives as Potential GPIIb/IIIa Inhibitors
Ferkat A. Khaliullin
,Zhekshen K. Mamatov
,Iuliia V. Shabalina
,Wang Yi
,Aleksandr N. Lobov
,Elena E. Klen
,Kudaiberdi G. Kozhobekov
,Dmitry A. Kudlay
,Aleksandr V. Samorodov
Posted: 31 August 2026
Advances in Cold Spray for Repair and Additive Manufacturing: A Review
Carlos Poblano-Salas
,John Henao
,Astrid Giraldo-Betancur
,Jorge Corona-Castuera
,Paola Forero-Sossa
,Julio Villafuerte
Posted: 31 August 2026
Curcumin-Loaded Poly(xylitol sebacate) Nanoparticles Reduce Viability and Alter Morphology in Colorectal Cancer Cell Lines
Jillian Pope
,Xandria Chandler
,Natalie Arnett
Posted: 31 August 2026
From Screening to Generative Design: Advances in ML-Assisted MOFs for Carbon Capture
Muhammad Bilal
,Faisal Latif
,Muhammad Hasnain
,Muhammad Ali
,Mahnoor Saeed
,Raziya Nadeem
The ongoing climate crisis, caused by the annual release of 37 billion metric tons of CO2 emissions, is putting pressure on the advancement of Carbon Capture and Storage (CCS) and Direct Air Capture (DAC) technologies. Metal–Organic Frameworks (MOFs), with their high surface areas and modular pore topologies, present a very attractive class of sorbents for CO2 capture; however, it currently remains computationally prohibitive to explore their extensive chemical design space. Herein, we provide a thorough evaluation of how machine learning (ML) (as an emerging technology) has played an increasing role in furthering our understanding of CO2 capture from MOFs. Through an organized investigation, we provide evaluations of the latest generation of models across four key areas: application at a process level, mechanistic interpretable modelling; physically relevant descriptors, and predictive performance metrics. Recent work with Machine Learning Interatomic Potentials (MLPs) shows that traditional assumptions about rigid frameworks are being challenged by the fact that diffusion properties and adsorption thermodynamics are heavily influenced by the flexibility of the framework. The use of physics-informed descriptor engineering yields R2 values of 0.81-0.97 across gas species and pressure regimes, while the generative nature of Deep Reinforcement Learning and transformer-based architectures has been shown to allow for the inverse design of frameworks with high affinities for gas species. The trend in this sector is moving towards optimization of multiple scales simultaneously and integrating processes to achieve an optimized property prediction. Current work with machine learning is focusing on using a combination of material properties and operational indicators (such as how much gas is recovered through pressure swing adsorption) to make predictions. As these techniques improve, there will be a similar need for a design that is both physically informed and understandable, thus allowing for a link between molecular discoveries and water-stable materials that have been experimentally verified and are suitable for use in commercial applications.
The ongoing climate crisis, caused by the annual release of 37 billion metric tons of CO2 emissions, is putting pressure on the advancement of Carbon Capture and Storage (CCS) and Direct Air Capture (DAC) technologies. Metal–Organic Frameworks (MOFs), with their high surface areas and modular pore topologies, present a very attractive class of sorbents for CO2 capture; however, it currently remains computationally prohibitive to explore their extensive chemical design space. Herein, we provide a thorough evaluation of how machine learning (ML) (as an emerging technology) has played an increasing role in furthering our understanding of CO2 capture from MOFs. Through an organized investigation, we provide evaluations of the latest generation of models across four key areas: application at a process level, mechanistic interpretable modelling; physically relevant descriptors, and predictive performance metrics. Recent work with Machine Learning Interatomic Potentials (MLPs) shows that traditional assumptions about rigid frameworks are being challenged by the fact that diffusion properties and adsorption thermodynamics are heavily influenced by the flexibility of the framework. The use of physics-informed descriptor engineering yields R2 values of 0.81-0.97 across gas species and pressure regimes, while the generative nature of Deep Reinforcement Learning and transformer-based architectures has been shown to allow for the inverse design of frameworks with high affinities for gas species. The trend in this sector is moving towards optimization of multiple scales simultaneously and integrating processes to achieve an optimized property prediction. Current work with machine learning is focusing on using a combination of material properties and operational indicators (such as how much gas is recovered through pressure swing adsorption) to make predictions. As these techniques improve, there will be a similar need for a design that is both physically informed and understandable, thus allowing for a link between molecular discoveries and water-stable materials that have been experimentally verified and are suitable for use in commercial applications.
Posted: 31 August 2026
From Explainable Machine Learning to Physics-Guided Design Rules for Inorganic Phosphors
Dilshod Nematov
,Makhmadzoir Kayumov
,Anushervon Ashurov
,Amondulloi Burkhonzoda
,Sherali Murodzoda
,Mekhrdod Kurboniyon
,Ning Wen
,Jia Li
,Atthar Luqman Ivansyah
,Peng Wang
+3 authors
Posted: 31 August 2026
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