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Periodicals

Quantum Capacitance-Limited MoS2 Biosensors Enable Remote Label-Free Enzyme Measurements

We have demonstrated atomically thin, quantum capacitance-limited, field-effect transistors (FETs) that enable the detection of pH changes with ~75-fold higher sensitivity (4.4 V/pH) over the Nernst value of 59 mV/pH at room temperature when used as a biosensor. The transistors, which are fabricated from a monolayer of MoS2 with a room temperature ionic liquid (RTIL) in place of a conventional oxide gate dielectric, exhibit very low intrinsic noise resulting in a pH limit of detection (LOD) of 92x10^-6 at 10 Hz. This high device performance, which is a function of the structure of our device, is achieved by remotely connecting the gate to a pH sensing element allowing the FETs to be reused. Because pH measurements are fundamentally important in biotechnology, the low limit of detection demonstrated here will benefit numerous applications ranging from pharmaceutical manufacturing to clinical diagnostics. As an example, we experimentally quantified the function of the kinase Cdk5, an enzyme implicated in Alzheimer's disease, at concentrations that are 5-fold lower than physiological values, and with sufficient time-resolution to allow the estimation of both steady-state and kinetic parameters in a single experiment. The high sensitivity, low LOD and fast turnaround time of the measurements will allow the development of early diagnostic tools and novel therapeutics to detect and treat neurological conditions years before currently possible.

Biotechnology• 2018• arXiv
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Periodicals

Deep learning framework DNN with conditional WGAN for protein solubility prediction

Protein solubility plays a critical role in improving production yield of recombinant proteins in biocatalyst and pharmaceutical field. To some extent, protein solubility can represent the function and activity of biocatalysts which are mainly composed of recombinant proteins. Highly soluble proteins are more effective in biocatalytic processes and can reduce the cost of biocatalysts. Screening proteins by experiments in vivo is time-consuming and expensive. In literature, large amounts of machine learning models have been investigated, whereas parameters of those models are underdetermined with insufficient data of protein solubility. A data augmentation algorithm that can enlarge the dataset of protein solubility and improve the performance of prediction model is highly desired, which can alleviate the common issue of insufficient data in biotechnology applications for developing machine learning models. We first implemented a novel approach that a data augmentation algorithm, conditional WGAN was used to improve prediction performance of DNN for protein solubility from protein sequence by generating artificial data. After adding mimic data produced from conditional WGAN, the prediction performance represented by $R^{2}$ was improved compared with the $R^{2}$ without data augmentation. After tuning the hyperparameters of two algorithms and organizing the dataset, we achieved a $R^{2}$ value of $45.04\%$, which enhanced $R^{2}$ about $10\%$ compared with the previous study using the same dataset. Data augmentation opens the door to applications of machine learning models on biological data, as machine learning models always fail to be well trained by small datasets.

Biotechnology• 2018• arXiv
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Periodicals

AptaBlocks Online - A web-based toolkit for the in-silico assembly of RNA complexes

Summary: The AptaBlocks Web Interface is focused on providing graphical, intuitive, and platform independent access to AptaBlocks, an experimentally validated algorithmic approach for the in-silico design of oligonucleotide sticky bridges. The availability of AptaBlocks online to the nucleic acid research community at large makes this software a highly effective tool for accelerating the design and development of novel oligonucleotide based drugs and other biotechnologies. Availability: The AptaBlocks Web Interface is freely available at www.ncbi.nlm.nih.gov/CBBresearch/Przytycka/index.cgi\#aptablocks

Biotechnology• 2019• arXiv
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Periodicals

Demystifying the trend of the healthcare index: Is historical price a key driver?

Healthcare sector indices consolidate the economic health of pharmaceutical, biotechnology, and healthcare service firms. The short-term movements in these indices are closely intertwined with capital allocation decisions affecting research and development investment, drug availability, and long-term health outcomes. This research investigates whether historical open-high-low-close (OHLC) index data contain sufficient information for predicting the directional movement of the opening index on the subsequent trading day. The problem is formulated as a supervised classification task involving a one-step-ahead rolling window. A diverse feature set is constructed, comprising original prices, volatility-based technical indicators, and a novel class of nowcasting features derived from mutual OHLC ratios. The framework is evaluated on data from healthcare indices in the U.S. and Indian markets over a five-year period spanning multiple economic phases, including the COVID-19 pandemic. The results demonstrate robust predictive performance, with accuracy exceeding 0.8 and Matthews correlation coefficients above 0.6. Notably, the proposed nowcasting features have emerged as a key determinant of the market movement. We have employed the Shapley-based explainability paradigm to further elucidate the contribution of the features: outcomes reveal the dominant role of the nowcasting features, followed by a more moderate contribution of original prices. This research offers a societal utility: the proposed features and model for short-term forecasting of healthcare indices can reduce information asymmetry and support a more stable and equitable health economy.

Biotechnology• 2026• arXiv
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Periodicals

Overcharging of zinc ion in the structure of zinc finger protein is needed for DNA binding stability

The zinc finger structure where a Zn2+ ion binds to 4 cysteine or histidine amino acids in a tetrahedral structure is very common motif of nucleic acid binding proteins. The corresponding interaction model is present in 3% of the genes of human genome. As a result, zinc finger has been shown to be extremely useful in various therapeutic and research capacities, as well as in biotechnology. In stable configuration, the cysteine amino acids are deprotonated and become negatively charged. This means the Zn2+ ion is overscreened by 4 cysteine charges (overcharged). It is question of whether this overcharged configuration is also stable when such negatively charged zinc finger binds to negatively charged DNA molecule. Using all atom molecular dynamics simulation up to microsecond range of an androgen receptor protein dimer, we investigate how the deprotonated state of cysteine influences its structure, dynamics, and function in binding o DNA molecules. Our results show that the deprotonated state of cysteine residues are essential for mechanical stabilization of the functional, folded conformation. Not only this state stabilizes the protein structure, it also stabilizes the protein-DNA binding complex. The differences in structural and energetic properties of the two (sequence-identical) monomers are also investigated showing the strong influence of DNA on the structure of zinc fingers upon complexation. Our result has potential impact on better molecular understanding of one of the most common classes of zinc fingers

Biotechnology• 2019• arXiv
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Periodicals

ToF-SIMS data analysis of Shewanella oneidensis MR-1 biofilms

Analysis of bacterial biofilms is particularly challenging and important with diverse applications from systems biology to biotechnology. Among the variety of techniques that have been applied, time-of-flight secondary ion mass spectrometry (ToF-SIMS) has many promising features in studying the surface characteristics of biofilms. ToF-SIMS offers high spatial resolution and high mass accuracy, which permit surface sensitive analysis of biofilm components. Thus, ToF-SIMS provides a powerful solution to addressing the challenge of bacterial biofilm analysis. This dataset covers ToF-SIMS analysis of Shewanella oneidensis MR-1 isolated from freshwater lake sediment in New York state. The MR-1 strain is known to have metal and sulfur reducing properties and it can be used for bioremediation and wastewater treatment. There is a current need to identify small molecules and fragments produced from bacterial biofilms. Static ToF-SIMS spectra of MR-1 were obtained using an IONTOF TOF.SIMS V instrument equipped with a 25 keV Bi3+ metal ion gun. Identified molecules and molecular fragments are compared against known biological databases and the reported peaks have at least 65 ppm mass accuracy. These molecules range from lipids and fatty acids to flavonoids, quinolones, and other naturally occurring organic compounds. It is anticipated that the spectral identification of key peaks will assist detection of metabolites, extracellular polymeric substance molecules like polysaccharides, and biologically relevant small molecules using ToF-SIMS in future surface and interface research.

Biotechnology• 2024• arXiv
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Periodicals

Stabilization of Age-Structured Competing Populations

Age-structured models capture the dynamic behavior of populations over time and result in nonlinear integro-partial differential equations (IPDEs). These processes arise in various fields such as biotechnology, economics, or demography. While coupled age-structured IPDEs modeling two or more interacting species occur naturally in epidemiology and ecology, they remain relatively underexplored. Prior work has primarily addressed stable and marginally stable dynamics. In constrast, this work considers an exponentially unstable model of two competing predator populations, formally referred to in the literature as ``competition'' dynamics. If one were to apply an input that simultaneously harvests both predator species, one would have control over only the product of the densities of the species, not over their ratio. Therefore, it is necessary to design a control input that directly harvests only one of the two predator species, while indirectly influencing the other via a backstepping approach. The model is transformed into a system of two coupled ordinary differential equations (ODEs), of which only one is actuated, and two autonomous, exponentially stable integral delay equations (IDEs) which enter the ODEs as nonlinear disturbances. The ODEs are globally stabilized with backstepping and an estimate of the region of attraction of the asymptotically stabilized equilibrium of the full IPDE system is provided, under a positivity restriction on control. Additionally, the full IPDE system is also shown to be local exponential stable. Such generalizations of competition dynamics open exciting possibilities for future research directions for systems with more than two species.

Biotechnology• 2025• arXiv
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Periodicals

The microbiome science of composting and human excrement composting: a review

Linear waste management systems are unsustainable and contribute to environmental degradation, economic inequity, and health disparities. Among the array of environmental challenges stemming from anthropogenic impacts, the management of human excrement (human feces and urine) stands as a significant concern. Over two billion people do not have access to adequate sanitation resulting in a global public health crisis. Composting is the microbial biotechnology aimed at cycling organic waste, including human excrement, for improved public health, agricultural productivity and safety, and environmental sustainability. Applications of modern microbiome-omics and related technologies have vast capacity to support continued advances in composting science and praxis. In this article, we review literature focused on applications of microbiome technologies to study composting systems and reactions. The studies we survey generally fall into the categories of animal manure composting, food and landscaping waste composting, biosolids composting, and human excrement composting. We review experiments utilizing microbiome technologies to investigate strategies for enhancing pathogen suppression and accelerating the biodegradation of organic matter. Additionally, we explore studies focused on the bioengineering potential of microbes as inoculants to facilitate degradation of toxins such as pharmaceuticals or per- and polyfluoroalkyl substances (PFAS). The findings from these studies underscore the importance of advancing our understanding of composting processes through the integration of emerging microbiome-omics technologies. We conclude that work to-date has demonstrated exciting basic and applied science potential from studying compost microbiomes, with promising implications for enhancing global environmental sustainability and public health.

Biotechnology• 2024• arXiv
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Periodicals

Electronic and Optical Properties of Zinc based Hybrid Organic-Inorganic Compounds for Ultraviolet Applications

Hybrid organic-inorganic (HOI) compounds are excellent candidates for a wide spectrum of applications in diverse fields such as optics, electronics, energy and biotechnology. Their broad range of versatility is achieved by combining the functionalities of organic and inorganic materials to generate unique properties. Current research has mostly focused on perovskite HOIs due to their wide range of uses in solar cells, photo detectors and memory devices. However, drawbacks such as instability and lead toxicity limit further implementation into other new areas. Thus, there is a need to develop stable and non-toxic HOI perovskite materials. Zinc is an attractive substitute for Pb in HOIs. Here, we apply a functionality based materials selection approach to screen for Zn-based HOI compounds from two crystallographic repositories; Inorganic Crystal Structure Database and American Mineralogist Crystal Structure Database. We successfully identify thirteen Zn-based HOI compounds. The electronic structure and optical properties of these compounds are investigated using density functional theory. The calculated optical absorbance fall within the far ultra-violet (FUV) region of 200-112 nm wavelength. We selected four of these compounds and calculated their band gaps; they were found to range between 4.9-5.7 eV. Considering that the UV absorbance is three times larger than average tissue absorbance and the refractive index (> 1.49) is greater than typical tissue materials, one could consider these Zn-based HOI compounds for selective photothermolysis treatment and UV protectant coating for electronic devices.

Biotechnology• 2019• arXiv
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Periodicals

GGBound: A Genome-Grounded Agent for Microbial Life-Boundary Prediction

Characterizing the physiological life boundaries of microbial strains, including viable temperature, pH, salinity, substrate utilization, and morphology, is central to biotechnology and ecology, yet traditionally requires exhaustive in vitro screening. Existing computational approaches either treat physiological traits as isolated supervised targets or repurpose biological foundation models as static encoders, leaving the genotype-to-physiology gap largely unbridged. We formulate microbial life-boundary prediction as a unified genome-to-physiology task and address it with a genome-conditioned, tool-augmented LLM agent. To support this task, we curate a strain-centric benchmark from IJSEM, NCBI, and BacDive covering 1,525 strains and 6,448 instances across viability intervals, environmental optima, substrate utilization, categorical traits, and morphology. Architecturally, the agent injects frozen LucaOne genome embeddings into a Qwen backbone via lightweight token fusion, and reasons over a similarity-based RAG module and a Genome-scale Metabolic Model (GEM) perturbation tool. We optimize the agent through a three-stage pipeline of gene-text alignment, agentic SFT on distilled trajectories, and GRPO with a novel counterfactual gene-grounding reward that reinforces the policy only when the authentic genome embedding causally improves correct-token generation relative to a zero-gene ablation. The resulting 4B-parameter agent matches or surpasses substantially larger frontier LLMs, with ablations confirming that genome-token fusion, dynamic tool use, and the counterfactual reward each yield distinct, significant gains.

Biotechnology• 2026• arXiv
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Periodicals

Channel Modeling for Diffusive Molecular Communication - A Tutorial Review

Molecular communication (MC) is a new communication engineering paradigm where molecules are employed as information carriers. MC systems are expected to enable new revolutionary applications such as sensing of target substances in biotechnology, smart drug delivery in medicine, and monitoring of oil pipelines or chemical reactors in industrial settings. As for any other kind of communication, simple yet sufficiently accurate channel models are needed for the design, analysis, and efficient operation of MC systems. In this paper, we provide a tutorial review on mathematical channel modeling for diffusive MC systems. The considered end-to-end MC channel models incorporate the effects of the release mechanism, the MC environment, and the reception mechanism on the observed information molecules. Thereby, the various existing models for the different components of an MC system are presented under a common framework and the underlying biological, chemical, and physical phenomena are discussed. Deterministic models characterizing the expected number of molecules observed at the receiver and statistical models characterizing the actual number of observed molecules are developed. In addition, we provide channel models for time-varying MC systems with moving transmitters and receivers, which are relevant for advanced applications such as smart drug delivery with mobile nanomachines. For complex scenarios, where simple MC channel models cannot be obtained from first principles, we investigate simulation-driven and experimentally-driven channel models. Finally, we provide a detailed discussion of potential challenges, open research problems, and future directions in channel modeling for diffusive MC systems.

Biotechnology• 2018• arXiv
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Periodicals

Bandit Theory and Thompson Sampling-Guided Directed Evolution for Sequence Optimization

Directed Evolution (DE), a landmark wet-lab method originated in 1960s, enables discovery of novel protein designs via evolving a population of candidate sequences. Recent advances in biotechnology has made it possible to collect high-throughput data, allowing the use of machine learning to map out a protein's sequence-to-function relation. There is a growing interest in machine learning-assisted DE for accelerating protein optimization. Yet the theoretical understanding of DE, as well as the use of machine learning in DE, remains limited. In this paper, we connect DE with the bandit learning theory and make a first attempt to study regret minimization in DE. We propose a Thompson Sampling-guided Directed Evolution (TS-DE) framework for sequence optimization, where the sequence-to-function mapping is unknown and querying a single value is subject to costly and noisy measurements. TS-DE updates a posterior of the function based on collected measurements. It uses a posterior-sampled function estimate to guide the crossover recombination and mutation steps in DE. In the case of a linear model, we show that TS-DE enjoys a Bayesian regret of order $\tilde O(d^{2}\sqrt{MT})$, where $d$ is feature dimension, $M$ is population size and $T$ is number of rounds. This regret bound is nearly optimal, confirming that bandit learning can provably accelerate DE. It may have implications for more general sequence optimization and evolutionary algorithms.

Biotechnology• 2022• arXiv
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Periodicals

Role of particle aggregation on the structure of dried colloidal silica layers

The process of colloidal drying gives way to particle self-assembly in numerous elds including photonics or biotechnology. Yet, the mechanisms and conditions driving the nal particle arrangement in dry colloidal layers remain elusive. Here, we examine how the drying rate selects the nanostructure of thick dried layers in four dierent suspensions of silica nanospheres. Depending on particle size and dispersity, either an amorphous arrangement, a crystalline arrangement, or a rate-dependent amorphous-to-crystalline transition occurs at the drying surface. Amorphous arrangements are observed in the two most polydisperse suspensions while crystallinity occurs when dispersity is lower. Counter-intuitively in the latter case, a higher drying rate favors ordering of the particles. To complement these measurements and to take stock of the bulk properties of the layer, tests on the layer porosity were undertaken. For all suspensions studied herein, faster drying yields denser dry layers. Crystalline surface arrangement implies large bulk volume fraction ($\sim$ 0.65) whereas amorphous arrangements can be observed in layers with either low (down to $\sim$ 0.53) or high ($\sim$ 0.65) volume fraction. Lastly, we demonstrate via targeted additional experiments and SAXS measurements, that the packing structure of the layers is mainly driven by the formation of aggregates and their subsequent packing, and not by the competition between Brownian diusion and convection. This highlights that a second dimensionless ratio in addition to the Peclet number should be taken into account, namely the aggregation over evaporation timescale.

Biotechnology• 2020• arXiv
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Periodicals

Cellular Automata

An introduction to cellular automata (both deterministic and probabilistic) with examples. Definition of deterministic automata, dynamical properties, damage spreading and Lyapunov exponents; probabilistic automata and Markov processes, nonequilibrium phase transitions, directed percolation, diffusion; simulation techniques, mean field. Investigation themes: life, epidemics, forest fires, percolation, modeling of ecosystems and speciation. They represent my notes for the school "Dynamical Modeling in Biotechnologies", ISI, Villa Gualino 1996.

Biotechnology• 1998• arXiv
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Periodicals

A high -quality and -throughput colloidal lithography by mechanical assembly and ice-based transfer

Colloidal lithography has emerged as a promising alternative to conventional nanofabrication techniques, offering the ability to create nanoscale patterns in a cost-effective and scalable manner. However, it has been so far limited by defects such as empty areas or multilayered regions, hindering its application. We introduce a novel "ice-assisted transfer" technique that combines rubbing-based particle assembly on elastomer substrates with ice-mediated transfer to achieve defect-free, high-quality polycrystalline particle monolayers. This approach eliminates foreign material contamination and enables precise control of particle arrangement and density. By optimizing process parameters, including surfactant concentration and water film thickness, we minimized defects and demonstrated the versatility of this method in fabricating functional nanoscale structures. We highlighted the benefits of this process through two applications: (1) antireflective "moth-eye" coatings, which achieved near-zero reflection in the mid-infrared spectrum due to improved particle monolayer quality; and (2) nanostructured surfaces for ligand-free T-cell activation, whose topography enhanced cell activation, showcasing potential for immunotherapy applications. The process achieves rapid, cost-efficient patterning without requiring specialized equipment, making it suitable for diverse fields requiring scalable nanostructuring. This work represents a significant advancement in colloidal lithography, addressing critical challenges and unlocking its potential for practical applications in optics, biotechnology, and beyond.

Biotechnology• 2025• arXiv
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Periodicals

MetaHMM: A Webserver for Identifying Novel Genes with Specified Functions in Metagenomic Samples

The fast and affordable sequencing of large clinical and environmental metagenomic datasets opens up new horizons in medical and biotechnological applications. It is believed that today we have described only about 1\% of the microorganisms on the Earth, therefore, metagenomic analysis mostly deals with unknown species in the samples. Microbial communities in extreme environments may contain genes with high biotechnological potential, and clinical metagenomes, related to diseases, may uncover still unknown pathogens and pathological mechanisms in known diseases. While the species-level identification and description of the taxa in the samples does not seem to be possible today, we can search for novel genes with known functions in these samples, using numerous techniques, including artificial intelligence tools, like the hidden Markov models (HMMs). Here we describe a simple-to-use webserver, the MetaHMM, which is capable of homology-based automatic model-building for the genes to be searched for, and it also finds the closest matches in the metagenome. The webserver uses already highly successful building blocks: it performs multiple alignment by applying Clustal Omega, builds a hidden Markov model with HMMER components of hmmbuild and uses hmmsearch for finding similar sequences to the specified model in the metagenomes. The webserver is publicly available at \url{https://metahmm.pitgroup.org}.

Biotechnology• 2017• arXiv
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Periodicals

BioFinBERT: Finetuning Large Language Models (LLMs) to Analyze Sentiment of Press Releases and Financial Text Around Inflection Points of Biotech Stocks

Large language models (LLMs) are deep learning algorithms being used to perform natural language processing tasks in various fields, from social sciences to finance and biomedical sciences. Developing and training a new LLM can be very computationally expensive, so it is becoming a common practice to take existing LLMs and finetune them with carefully curated datasets for desired applications in different fields. Here, we present BioFinBERT, a finetuned LLM to perform financial sentiment analysis of public text associated with stocks of companies in the biotechnology sector. The stocks of biotech companies developing highly innovative and risky therapeutic drugs tend to respond very positively or negatively upon a successful or failed clinical readout or regulatory approval of their drug, respectively. These clinical or regulatory results are disclosed by the biotech companies via press releases, which are followed by a significant stock response in many cases. In our attempt to design a LLM capable of analyzing the sentiment of these press releases,we first finetuned BioBERT, a biomedical language representation model designed for biomedical text mining, using financial textual databases. Our finetuned model, termed BioFinBERT, was then used to perform financial sentiment analysis of various biotech-related press releases and financial text around inflection points that significantly affected the price of biotech stocks.

Biotechnology• 2024• arXiv
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Periodicals

China leads scientific trends; the West launches new ones

How nations shape the scientific frontier matters for technological competition, but standard metrics, including publication counts, citations, and disruption indices, look backward and fail to distinguish between fundamentally different leadership strategies. We develop and validate two forward-looking model-based measures and apply them to tens of millions of articles since 1990. The first embeds research pathways within an evolving hypergraph of concepts and scientists to identify leadership in emerging areas--work that anticipates where the scientific crowd is heading. The second embeds evolving samples of ideas and disciplines drawn upon in past research to identify leadership in surprising new directions as unexpected combinations become routine and science reorganizes around them. China became the global leader in emerging areas roughly a decade ago, well before it led in volume, reflecting a capacity to detect and amplify nascent consensus at scale. The United States and Europe show the opposite profile: declining emergence shares but persistent leadership in prescient work, especially research bridging disciplinary boundaries. These patterns replicate across databases, attribution methods, and strategic domains, including AI, biotechnology, energy, and semiconductors. Nations lead science by reading the landscape or by reshaping it, and the institutional requirements for each strategy lie in tension. The distribution of these strategies promises to shape the global structure of technological leadership for decades.

Biotechnology• 2026• arXiv
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Periodicals

SpecHD: Hyperdimensional Computing Framework for FPGA-based Mass Spectrometry Clustering

Mass spectrometry-based proteomics is a key enabler for personalized healthcare, providing a deep dive into the complex protein compositions of biological systems. This technology has vast applications in biotechnology and biomedicine but faces significant computational bottlenecks. Current methodologies often require multiple hours or even days to process extensive datasets, particularly in the domain of spectral clustering. To tackle these inefficiencies, we introduce SpecHD, a hyperdimensional computing (HDC) framework supplemented by an FPGA-accelerated architecture with integrated near-storage preprocessing. Utilizing streamlined binary operations in an HDC environment, SpecHD capitalizes on the low-latency and parallel capabilities of FPGAs. This approach markedly improves clustering speed and efficiency, serving as a catalyst for real-time, high-throughput data analysis in future healthcare applications. Our evaluations demonstrate that SpecHD not only maintains but often surpasses existing clustering quality metrics while drastically cutting computational time. Specifically, it can cluster a large-scale human proteome dataset-comprising 25 million MS/MS spectra and 131 GB of MS data-in just 5 minutes. With energy efficiency exceeding 31x and a speedup factor that spans a range of 6x to 54x over existing state of-the-art solutions, SpecHD emerges as a promising solution for the rapid analysis of mass spectrometry data with great implications for personalized healthcare.

Biotechnology• 2023• arXiv
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Periodicals

Complex Dynamics of Real Nanosystems: Fundamental Paradigm for Nanoscience and Nanotechnology

Structure and dynamics of real nanosystems emerge from the unreduced solution of the underlying interaction problem. It has the property of dynamic multivaluedness giving genuine dynamic randomness and complexity (physics/9806002, physics/0405063). Chaoticity is irreducibly high in nanoscale systems and underlies exponentially high efficiency of their real, complex-dynamic operation (physics/0211071). Applications include real, dynamically chaotic quantum devices, nano-biotechnology, and genetics.

Biotechnology• 2004• arXiv
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Periodicals

Die to wafer direct bonding of (100) single-crystal diamond thin films for quantum optoelectronics

This work unlocks the manufacturing of nanophotonic quantum systems that exploit the unique material properties of single-crystal diamond (SCD). We achieve this by introducing a semiconductor-compatible process for the direct bonding of multiple high-quality, ultrathin diamond films onto a carrier wafer, enabling the subsequent parallel nanofabrication of optoelectronic integrated circuits. Central to this approach is a new diamond surface-preparation method that avoids boiling tri-acid mixtures while producing exceptionally clean 20 um thin single crystals. These platelets are bonded side-by-side to 100 mm silica wafers and exhibit a record shear strength of 45.1 MPa for (100)-oriented diamond, surpassing all previously reported bonding attempts. Evidence indicates that the bonding is dominated by van der Waals interactions, likely arising from mismatched protonation mechanisms between Si-OH and C-OH surface terminations, rather than from covalent-bond-driven mechanisms. Despite this non-molecular nature, the heterostructures remain stable through liquid immersions and standard nanofabrication steps. Because the method depends primarily on surface cleanliness and roughness rather than specific chemistries, it is broadly transferable across wafer materials. This capability to parallel-bond ultrathin SCD films onto large-area substrates provides a scalable route to high-performance platforms spanning nanophotonic quantum technologies, high-power electronics, MEMS, and biotechnology.

Biotechnology• 2026• arXiv
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Silicon is the next frontier in plant synthetic biology

Silicon has striking similarity with carbon and is found in plant cells. However, there is no specific role that has been assigned to silicon in the life cycle of plants. The amount of silicon in plant cells is species specific and can reach levels comparable to macronutrients. Silicon is the central element for artificial intelligence, nanotechnology and digital revolution thus can act as an informational molecule like nucleic acids while the diverse bonding potential of silicon with different chemical species is analogous to carbon and thus can serve as a structural candidate such as proteins. The discovery of large amounts of silicon on Mars and the moon along with the recent developments of enzyme that can incorporate silicon into organic molecules has propelled the theory of creating silicon-based life. More recently, bacterial cytochrome has been modified through directed evolution such that it could cleave silicon-carbon bonds in organo-silicon compounds thus consolidating on the idea of utilizing silicon in biomolecules. In this article the potential of silicon-based life forms has been hypothesized along with the reasoning that autotrophic virus-like particles can be a lucrative candidate to investigate such potential. Such investigations in the field of synthetic biology and astrobiology will have corollary benefit on Earth in the areas of medicine, sustainable agriculture and environmental sustainability. Bibliometric analysis indicates an increasing interest in synthetic biology. Germany leads in research related to plant synthetic biology, while Biotechnology and Biological Sciences Research Council (BBSRC) at UK has highest financial commitments and Chinese Academy of Sciences generates the highest number of publications in the field.

Biotechnology• 2025• arXiv
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Transmission IR Microscopy for the Quantitation of Biomolecular Mass In Live Cells

Absolute quantity imaging of biomolecules on a single cell level is critical for measurement assurance in biosciences and bioindustries. While infrared (IR) transmission microscopy is a powerful label-free imaging modality capable of chemical quantification, its applicability to hydrated biological samples remains challenging due to the strong water absorption. We overcome this challenge by applying a solvent absorption compensation (SAC) technique to a home-built quantum cascade laser IR microscope. SAC-IR microscopy improves the chemical sensitivity considerably by adjusting the incident light intensity to pre-compensate the IR absorption by water while retaining the full dynamic range. We demonstrate the label-free chemical imaging of key biomolecules of a cell, such as protein, fatty acid, and nucleic acid, with sub-cellular spatial resolution. By imaging live fibroblast cells over twelve hours, we monitor the mass change of the three molecular species of single cells at various phases, including cell division. While the current live-cell imaging demonstration involved three wavenumbers, more wavenumber images could measure more biomolecules in live cells with higher accuracy. As a label-free method to measure absolute quantities of various molecules in a cell, SAC-IR microscopy can potentially become a standard chemical characterization tool for live cells in biology, medicine, and biotechnology.

Biotechnology• 2024• arXiv
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Optimal Embeddedness and Governance in Biotech Venture Capital Syndicates

The biotech venture market faces intense capital demands and regulatory scrutiny, yet academic research on VC networks remains rooted in software and consumer-tech contexts. This dissertation investigates how repeated co-investment ties and domain-expertise homophily influence a venture's exit likelihood, timing, and route amid the sector's pronounced technological and market uncertainty. Using a novel panel of 11,680 biotechnology start-ups from the United States, Canada, and Europe (2010-2024), we apply pooled logit, Cox proportional-hazards, multinomial logit, and Fine-Gray competing-risk models. Our findings show that both average prior co-investment and investor homophily exhibit robust inverted-U relationships with exit outcomes. Moderate familiarity and scientific overlap maximize exit probability, while either sparse or excessive embedding reduces success. Governance mechanisms also play a crucial role: participation of a pharmaceutical corporate VC or a highly independent board flattens the negative effects of over-embedding, enabling syndicates to sustain exit momentum at higher levels of familiarity or homogeneity. Furthermore, the optimal degree of embeddedness is route-specific: IPOs require deeper coordination than trade sales, while acquisitions peak earlier and are less sensitive to homophily. These findings refine network-embeddedness theory in the life-science context, identify governance contingencies, and offer practitioners quantitative metrics to balance trust, expertise, and oversight in biotech financing.

Biotechnology• 2025• arXiv
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