How to Avoid Desk Rejection at Sensors

Sensors operates under MDPI's rapid editorial model, which means desk decisions typically come within 2-4 weeks. This speed works against authors who submit incomplete work hoping for constructive reviewer feedback, the paper gets rejected before it reaches reviewers.

MDPI journals, including Sensors, accept diverse sensor types: biosensors, chemical sensors, physical sensors, smart sensors for IoT applications. The diversity suggests broad acceptance criteria, but every sensor type must meet the same practical implementation standard. Whether you're detecting glucose or monitoring structural vibrations, your sensor needs real-sample testing and complete characterization.

Authors sometimes misread Sensors as a lower-bar venue because of MDPI's volume publishing model. That's a mistake. The journal's 3.5 impact factor reflects genuine selectivity on practical sensor development, and the editorial board includes active researchers who recognize incomplete characterization immediately.

Sensors publishes applied sensor technology, not fundamental studies of molecular recognition or signal transduction. The editorial filter catches authors off guard because it's about scope, not science quality.

Consider two glucose sensor papers. Paper A: new electrochemical sensor with 0.1 mM detection limit in phosphate buffer, stable response over 100 measurements, detailed electrochemical characterization. Paper B: similar sensor with 0.5 mM detection limit tested in human serum, interference studies with fructose and galactose, 30-day stability data at room temperature.

Paper B gets accepted despite worse detection limits because it demonstrates practical sensor performance. Paper A gets desk rejected because it's a proof-of-concept study disguised as sensor development.

The journal's readership develops sensor technology for real applications, medical diagnostics, environmental monitoring, food safety, industrial process control. They need sensors that work outside the laboratory. The impact factor of 3.5 positions Sensors competitively, but acceptance requires meeting practical implementation standards regardless of sensor type.

Testing your cancer biomarker sensor in diluted human serum isn't sufficient. Testing your pesticide detector in spiked river water doesn't prove environmental monitoring capability. Real-sample testing means actual samples from the intended application environment without artificial simplification.

Real samples introduce complications absent in controlled conditions:

• Biological samples: Proteins that foul sensor surfaces, salt concentrations that shift electrochemical baselines, pH variations that affect binding kinetics
• Environmental samples: Particulates that block optical signals, competing ions that interfere with selective recognition, organic matter that changes surface chemistry
• Dynamic effects: Response time increases in viscous biological fluids, detection limit degradation from sample turbidity, signal drift from active biochemical processes

A heavy metal sensor might work perfectly in laboratory solutions with cadmium, mercury, and lead at known concentrations. But groundwater contains natural organic acids that complex with metals, bacterial biofilms that modify electrode surfaces, and ionic strength variations that affect mass transport.

Editors spot authentic real-sample data because it shows more variability, requires more statistical analysis, and includes discussion of matrix effects. Clean laboratory data is suspiciously consistent.

Most sensor papers fail here because authors test simplified versions of real samples: filtered biological fluids, synthetic mixtures designed to mimic complexity, or spiked solutions that approximate real conditions. These approaches miss the unpredictable interactions that define sensor performance in actual use.

Testing in real samples also reveals practical limitations invisible in controlled studies. Response time might increase when sensors encounter viscous biological fluids. Detection limits might degrade when dealing with sample turbidity or color interference. Signal drift might accelerate in samples with active biochemical processes.

Smart sensor development tests crude prototypes in actual samples early to identify failure modes before optimizing sensor design. This prevents months of optimization producing laboratory performance that doesn't translate.

Reporting that your glucose sensor doesn't respond to fructose isn't sufficient selectivity analysis. Sensors editors want comprehensive interference studies reflecting the complexity of real analytical environments.

Complete selectivity characterization requires:

• Testing against all molecules reasonably present in target samples at realistic concentrations
• Quantitative interference coefficients and selectivity ratios, not qualitative "no interference" statements
• Detection limit changes in the presence of common interferents across their concentration range
• Error bars showing measurement uncertainty
• Discussion of how interference effects change with sensor aging or surface modification

For example, dopamine sensors for neurochemical monitoring must account for ascorbic acid, uric acid, serotonin, and norepinephrine, often present at higher concentrations than dopamine itself. Testing each compound individually and in mixtures that represent realistic neurochemical environments is the minimum.

Authors often try to shortcut selectivity studies by testing only a few obvious interferents or by testing at concentrations that don't reflect real sample conditions. This produces incomplete characterization that editors recognize immediately. If your glucose sensor claims good selectivity but you only tested fructose and sucrose at millimolar concentrations, you haven't characterized selectivity for clinical glucose monitoring where multiple interfering compounds exist at various concentration levels.

Smart selectivity studies also consider dynamic interference effects. Some interfering compounds don't produce false positive signals but reduce sensor sensitivity over time through surface fouling or competitive binding. These effects only become apparent during extended testing periods or repeated measurements in complex samples.

Selectivity requirements connect directly to real-sample testing because interference effects often amplify in complex matrices. A sensor might show good selectivity in buffer but suffer significant interference when the same compounds appear in biological fluids with different pH, ionic strength, or protein content.

Authors often submit to Sensors (MDPI) when IEEE Sensors Journal would be a better fit, or vice versa. The two journals have different editorial priorities despite overlapping scope.

If your paper is strong on novel transduction physics but light on real-sample validation, IEEE Sensors Journal may be more receptive. If your paper demonstrates complete practical sensor development with real-world testing, Sensors is the natural home.

• Stability studies: Long-term performance data showing how sensor response changes over time under storage and operating conditions. Shelf-life data at different temperatures. Operational stability over hundreds of measurement cycles.
• Mechanism understanding: You can explain why your sensor produces measurable signals. Surface binding kinetics, electron transfer mechanisms, optical property changes. Enough mechanistic insight to predict sensor behavior.
• Practical implementation data: Response time measurements, sample volume requirements, and detection protocols that could realistically be used by intended end users.

Missing any of these signals incomplete sensor development. The journal doesn't publish proof-of-concept studies, and editorial screening catches incomplete characterization quickly.

Consider whether you're trying to publish too early in the sensor development process. Many authors submit papers based on initial promising results before they've fully characterized sensor performance or demonstrated practical implementation. This usually leads to desk rejection because the manuscript presents preliminary data as complete sensor development. Better to delay submission until you have complete characterization than to submit prematurely and face rejection.

The review process at Sensors is fast enough that weak manuscripts get filtered early. If your paper has obvious gaps in characterization or testing, expect rapid desk rejection rather than extended peer review. Focus on strengthening the weakest aspects of your sensor characterization before submission. If you have good sensitivity and selectivity data but limited stability studies, invest time in long-term testing. If you have complete analytical data but limited real-sample testing, prioritize practical validation experiments.